diff --git a/README.md b/README.md
index 848d161..ce43b59 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@ This is updated **face-api.js** with latest available TensorFlow/JS as the origi
 
 Forked from [face-api.js](https://github.com/justadudewhohacks/face-api.js) version **0.22.2** released on March 22nd, 2020  
 
-Currently based on **`TensorFlow/JS` 3.0.0**  
+Currently based on **`TensorFlow/JS` 3.1.0**  
 
 ### Why?
 
@@ -19,7 +19,7 @@ Unfortunately, changes ended up being too large for a simple pull request on ori
 
 ### Differences
 
-- Compatible with `TensorFlow/JS 2.0+`  
+- Compatible with `TensorFlow/JS 2.0+ & 3.0+`  
 - Compatible with `WebGL`, `CPU` and `WASM` TFJS Browser backends
 - Compatible with both `tfjs-node` and `tfjs-node-gpu` TFJS NodeJS backends
 - Updated all type castings for TypeScript type checking to `TypeScript 4.1`  
diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js
index acacf34..cca7aa3 100644
--- a/dist/face-api.esm-nobundle.js
+++ b/dist/face-api.esm-nobundle.js
@@ -5,5 +5,5 @@
   author: <https://github.com/vladmandic>'
   */
 
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predictAgeAndGender(t){let e=await E(t),r=await this.forwardInput(e),n=ut.unstack(r.age),a=ut.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await m.data())[0],u=d>.5,f=u?yt.MALE:yt.FEMALE,v=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:f,genderProbability:v}}));return r.age.dispose(),r.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Io(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Lo(r)}extractParams(t){let e=512*1+1+(512*2+2),r=t.slice(0,t.length-e),n=t.slice(t.length-e);return 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r=R.avgPool(o,2,2,"valid"),n=R.zeros(r.shape),a=r.shape[3]!==e.shape[3];if(r.shape[1]!==e.shape[1]||r.shape[2]!==e.shape[2]){let i=[...e.shape];i[1]=1;let c=R.zeros(i);e=R.concat([e,c],1);let m=[...e.shape];m[2]=1;let p=R.zeros(m);e=R.concat([e,p],2)}return r=a?R.concat([r,n],3):r,e=R.add(r,e),e=R.relu(e),e}var be=class extends S{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceRecognitionNet - load model before inference");return U.tidy(()=>{let r=U.cast(t.toBatchTensor(150,!0),"float32"),a=ot(r,[122.782,117.001,104.298]).div(U.scalar(256)),s=fr(a,e.conv32_down);s=U.maxPool(s,3,2,"valid"),s=nt(s,e.conv32_1),s=nt(s,e.conv32_2),s=nt(s,e.conv32_3),s=$e(s,e.conv64_down),s=nt(s,e.conv64_1),s=nt(s,e.conv64_2),s=nt(s,e.conv64_3),s=$e(s,e.conv128_down),s=nt(s,e.conv128_1),s=nt(s,e.conv128_2),s=$e(s,e.conv256_down),s=nt(s,e.conv256_1),s=nt(s,e.conv256_2),s=$e(s,e.conv256_down_out);let i=s.mean([1,2]);return U.matMul(i,e.fc)})}async 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t.push({paramPath:`${m}/filters`},{paramPath:`${m}/batch_norm_scale`},{paramPath:`${m}/batch_norm_offset`},{paramPath:`${m}/batch_norm_mean`},{paramPath:`${m}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:u,batch_norm_mean:f,batch_norm_variance:v}}function r(c,m,p,d,u){let f=at.tensor4d(o(c*m*p*p),[p,p,c,m]),v=at.tensor1d(o(m));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${u?"batch_norm_offset":"bias"}`}),{filters:f,bias:v}}function n(c,m,p,d){let{filters:u,bias:f}=r(c,m,p,d,!0);return{filters:u,batch_norm_offset:f}}function a(c,m,p){let d=e(c,`${p}/depthwise_conv`),u=n(c,m,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:u}}function s(){let 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ft=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var co=class extends S{constructor(t){super("TinyYolov2");so(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=Tt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=Tt(r,e.conv6),r=Tt(r,e.conv7),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?ge(zt(t,e.conv0,"valid",!1)):Pt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Pt(r,e.conv6):r,r=e.conv7?Pt(r,e.conv7):r,zt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?ot(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new ft(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),f=m.map(h=>this.config.classes[h.label]);return Ar(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Et(d[h],u[h],f[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return en(t,this.config)}extractParams(t){let e=this.config.filterSizes||co.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return tn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,f]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],_=await u.array(),h=await d.array();for(let y=0;y<m;y++)for(let T=0;T<m;T++)for(let F=0;F<p;F++){let L=Ee(_[y][T][F][0]);if(!r||L>r){let G=(T+Ee(h[y][T][F][0]))/m*i,et=(y+Ee(h[y][T][F][1]))/m*c,it=Math.exp(h[y][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[y][T][F][3])*this.config.anchors[F].y/m*c,_t=G-it/2,wt=et-X/2,Dt={row:y,col:T,anchor:F},{classScore:te,label:lo}=this.withClassScores?await this.extractPredictedClass(f,Dt):{classScore:1,label:0};v.push({box:new re(_t,wt,_t+it,wt+X),score:L,classScore:L*te,label:lo,...Dt})}}return d.dispose(),u.dispose(),f.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ve=co;ve.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ye=class extends ve{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Xo,classes:["face"],...t?{anchors:qo,meanRgb:Zo}:{anchors:Jo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?Qo:Ko}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Kn(o,t=!0){let e=new ye(t);return e.extractWeights(o),e}var gr=class extends ft{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var tt=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var He=b(g());var mo=b(g());async function Jt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Vt(c)?n(c):c.detection),s=r||(t instanceof mo.Tensor?await ie(t,a):await se(t,a)),i=await e(s);return s.forEach(c=>c instanceof mo.Tensor&&c.dispose()),i}async function Fe(o,t,e,r,n){return Jt([o],t,async a=>e(a[0]),r,n)}var rn=.4,on=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],nn=[117.001,114.697,97.404];var Te=class extends ve{constructor(){let t={withSeparableConvs:!0,iouThreshold:rn,classes:["face"],anchors:on,meanRgb:nn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new Xt,tinyFaceDetector:new Te,tinyYolov2:new ye,faceLandmark68Net:new he,faceLandmark68TinyNet:new dr,faceRecognitionNet:new be,faceExpressionNet:new cr,ageGenderNet:new pr},an=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),Qn=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ta=(o,t)=>P.tinyYolov2.locateFaces(o,t),sn=o=>P.faceLandmark68Net.detectLandmarks(o),ea=o=>P.faceLandmark68TinyNet.detectLandmarks(o),ra=o=>P.faceRecognitionNet.computeFaceDescriptor(o),oa=o=>P.faceExpressionNet.predictExpressions(o),na=o=>P.ageGenderNet.predictAgeAndGender(o),cn=o=>P.ssdMobilenetv1.load(o),aa=o=>P.tinyFaceDetector.load(o),sa=o=>P.tinyYolov2.load(o),ia=o=>P.faceLandmark68Net.load(o),ca=o=>P.faceLandmark68TinyNet.load(o),ma=o=>P.faceRecognitionNet.load(o),pa=o=>P.faceExpressionNet.load(o),da=o=>P.ageGenderNet.load(o),ua=cn,fa=an,la=sn;var po=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},we=class extends po{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>mr(r,e[n]))}withAgeAndGender(){return new Pe(this,this.input)}},De=class extends po{async run(){let t=await this.parentTask;if(!t)return;let e=await Fe(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new _e(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new Wt(this,this.input)}},Qt=class extends De{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var uo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Pe=class extends uo{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>P.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return hr(xr(r,s,i),a)})}withFaceExpressions(){return new we(this,this.input)}},_e=class extends uo{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Fe(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return hr(xr(t,r,n),e)}withFaceExpressions(){return new De(this,this.input)}},qt=class extends Pe{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new Wt(this,this.input)}},Zt=class extends _e{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},Wt=class extends vr{async run(){let t=await this.parentTask;return(await Jt(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>lr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}},Bt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await Fe(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return lr(t,e)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}};var yr=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},Fr=class extends yr{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof He.Tensor?await ie(this.input,e):await se(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof He.Tensor&&a.dispose()),t.map((a,s)=>le(a,n[s]))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new Wt(this,this.input)}},Tr=class extends yr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof He.Tensor?await ie(this.input,[e]):await se(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof He.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var Pr=class extends tt{constructor(t,e=new Z){super();this.input=t;this.options=e}},Ye=class extends Pr{async run(){let{input:t,options:e}=this,r=e instanceof gr?n=>P.tinyFaceDetector.locateFaces(n,e):e instanceof Z?n=>P.ssdMobilenetv1.locateFaces(n,e):e instanceof ft?n=>P.tinyYolov2.locateFaces(n,e):null;if(!r)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return r(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>jt({},r)))})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Pe(this.runAndExtendWithFaceDetections(),this.input)}},_r=class extends Pr{async run(){let t=await new Ye(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?jt({},e):void 0)})}withFaceLandmarks(t=!1){return new Tr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new De(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new _e(this.runAndExtendWithFaceDetection(),this.input)}};function ha(o,t=new Z){return new _r(o,t)}function wr(o,t=new Z){return new Ye(o,t)}async function mn(o,t){return wr(o,new Z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function xa(o,t={}){return wr(o,new ft(t)).withFaceLandmarks().withFaceDescriptors()}var ba=mn;function fo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a**2,0))}var Dr=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof bt)return s;if(s instanceof Float32Array)return new bt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new bt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>fo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this.distanceThreshold?e:new Me("unknown",e.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>bt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function ga(o){let t=new Te;return t.extractWeights(o),t}function pn(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>pn(n,{width:e,height:r}));if(Vt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof V||o instanceof M?o.forSize(e,r):o}var ya=typeof process!="undefined",Fa=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Ta={faceapi:Eo,node:ya,browser:Fa};export{pr as AgeGenderNet,re as BoundingBox,D as Box,tt as ComposableTask,Wt as ComputeAllFaceDescriptorsTask,vr as ComputeFaceDescriptorsTaskBase,Bt as ComputeSingleFaceDescriptorTask,Fr as DetectAllFaceLandmarksTask,Ye as DetectAllFacesTask,yr as DetectFaceLandmarksTaskBase,Pr as DetectFacesTaskBase,Tr as DetectSingleFaceLandmarksTask,_r as DetectSingleFaceTask,A as Dimensions,qr as FACE_EXPRESSION_LABELS,M as FaceDetection,Uo as FaceDetectionNet,cr as FaceExpressionNet,Lt as FaceExpressions,he as FaceLandmark68Net,dr as FaceLandmark68TinyNet,Ao as FaceLandmarkNet,V as FaceLandmarks,bo as FaceLandmarks5,ne as FaceLandmarks68,Me as FaceMatch,Dr as FaceMatcher,be as FaceRecognitionNet,yt as Gender,Ce as LabeledBox,bt as LabeledFaceDescriptors,gt as NetInput,S as NeuralNetwork,Et as ObjectDetection,x as Point,go as PredictedBox,oe as Rect,Xt as SsdMobilenetv1,Z as SsdMobilenetv1Options,Te as TinyFaceDetector,gr as TinyFaceDetectorOptions,ye as TinyYolov2,ft as TinyYolov2Options,ba as allFaces,mn as allFacesSsdMobilenetv1,xa as allFacesTinyYolov2,Gr as awaitMediaLoaded,zr as bufferToImage,ra as computeFaceDescriptor,ae as createCanvas,Le as createCanvasFromMedia,Jn as createFaceDetectionNet,Rn as createFaceRecognitionNet,Vo as createSsdMobilenetv1,ga as createTinyFaceDetector,Kn as createTinyYolov2,wr as detectAllFaces,sn as detectFaceLandmarks,ea as detectFaceLandmarksTiny,la as detectLandmarks,ha as detectSingleFace,to as draw,w as env,fo as euclideanDistance,hr as extendWithAge,lr as extendWithFaceDescriptor,jt as extendWithFaceDetection,mr as extendWithFaceExpressions,le as extendWithFaceLandmarks,xr as extendWithGender,ie as extractFaceTensors,se as extractFaces,Mn as fetchImage,Xr as fetchJson,Cn as fetchNetWeights,Gt as fetchOrThrow,$ as getContext2dOrThrow,Yt as getMediaDimensions,Vr as imageTensorToCanvas,Ur as imageToSquare,vn as inverseSigmoid,kr as iou,qe as isMediaElement,Ie as isMediaLoaded,$n as isWithAge,pt as isWithFaceDetection,Zr as isWithFaceExpressions,Vt as isWithFaceLandmarks,On as isWithGender,da as loadAgeGenderModel,ua as loadFaceDetectionModel,pa as loadFaceExpressionModel,ia as loadFaceLandmarkModel,ca as loadFaceLandmarkTinyModel,ma as loadFaceRecognitionModel,cn as loadSsdMobilenetv1Model,aa as loadTinyFaceDetectorModel,sa as loadTinyYolov2Model,Jr as loadWeightMap,fa as locateFaces,Nn as matchDimensions,Sr as minBbox,P as nets,Ar as nonMaxSuppression,ot as normalize,Wr as padToSquare,na as predictAgeAndGender,oa as recognizeFaceExpressions,pn as resizeResults,Ht as resolveInput,gn as shuffleArray,Ee as sigmoid,an as ssdMobilenetv1,va as tf,Qn as tinyFaceDetector,ta as tinyYolov2,E as toNetInput,Cr as utils,so as validateConfig,Ta as version};
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predictAgeAndGender(t){let e=await E(t),r=await this.forwardInput(e),n=ut.unstack(r.age),a=ut.unstack(r.gender),s=n.map((c,m)=>({ageTensor:c,genderTensor:a[m]})),i=await Promise.all(s.map(async({ageTensor:c,genderTensor:m})=>{let p=(await c.data())[0],d=(await m.data())[0],u=d>.5,f=u?yt.MALE:yt.FEMALE,v=u?d:1-d;return c.dispose(),m.dispose(),{age:p,gender:f,genderProbability:v}}));return r.age.dispose(),r.gender.dispose(),e.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Io(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Lo(r)}extractParams(t){let e=512*1+1+(512*2+2),r=t.slice(0,t.length-e),n=t.slice(t.length-e);return 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p=s(`Prediction/BoxPredictor_${m}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${m}/box_encoding_predictor`),d=s(`Prediction/BoxPredictor_${m}/ClassPredictor`,`prediction_layer/box_predictor_${m}/class_predictor`);return{box_encoding_predictor:p,class_predictor:d}}function c(){return{conv_0:r("Prediction",0,"prediction_layer/conv_0"),conv_1:r("Prediction",1,"prediction_layer/conv_1"),conv_2:r("Prediction",2,"prediction_layer/conv_2"),conv_3:r("Prediction",3,"prediction_layer/conv_3"),conv_4:r("Prediction",4,"prediction_layer/conv_4"),conv_5:r("Prediction",5,"prediction_layer/conv_5"),conv_6:r("Prediction",6,"prediction_layer/conv_6"),conv_7:r("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:i(0),box_predictor_1:i(1),box_predictor_2:i(2),box_predictor_3:i(3),box_predictor_4:i(4),box_predictor_5:i(5)}}return{extractMobilenetV1Params:a,extractPredictionLayerParams:c}}function jo(o){let 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r=q(o,e.conv_0,[1,1]),n=q(r,e.conv_1,[2,2]),a=q(n,e.conv_2,[1,1]),s=q(a,e.conv_3,[2,2]),i=q(s,e.conv_4,[1,1]),c=q(i,e.conv_5,[2,2]),m=q(c,e.conv_6,[1,1]),p=q(m,e.conv_7,[2,2]),d=Ut(t,e.box_predictor_0),u=Ut(o,e.box_predictor_1),f=Ut(n,e.box_predictor_2),v=Ut(s,e.box_predictor_3),_=Ut(c,e.box_predictor_4),h=Ut(p,e.box_predictor_5),y=je.concat([d.boxPredictionEncoding,u.boxPredictionEncoding,f.boxPredictionEncoding,v.boxPredictionEncoding,_.boxPredictionEncoding,h.boxPredictionEncoding],1),T=je.concat([d.classPrediction,u.classPrediction,f.classPrediction,v.classPrediction,_.classPrediction,h.classPrediction],1);return{boxPredictions:y,classPredictions:T}})}var Z=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Xt=class extends S{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return st.tidy(()=>{let r=st.cast(t.toBatchTensor(512,!1),"float32"),n=st.sub(st.mul(r,st.scalar(.007843137718737125)),st.scalar(1)),a=Ho(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=zo(a.out,a.conv11,e.prediction_layer);return Go(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await E(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new Z(e),a=await E(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let F=1;F<s.length;F++)s[F].dispose(),i[F].dispose();let p=Array.from(await m.data()),u=Yo(c,p,r,.5,n),f=a.getReshapedInputDimensions(0),v=a.inputSize,_=v/f.width,h=v/f.height,y=c.arraySync(),T=u.map(F=>{let[L,G]=[Math.max(0,y[F][0]),Math.min(1,y[F][2])].map(X=>X*h),[et,it]=[Math.max(0,y[F][1]),Math.min(1,y[F][3])].map(X=>X*_);return new M(p[F],new oe(et,L,it-et,G-L),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return jo(t)}extractParams(t){return Oo(t)}};function Vo(o){let t=new Xt;return t.extractWeights(o),t}function Jn(o){return Vo(o)}var Uo=class extends Xt{};var Xo=.4,Jo=[new x(.738768,.874946),new x(2.42204,2.65704),new x(4.30971,7.04493),new x(10.246,4.59428),new x(12.6868,11.8741)],qo=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],Zo=[117.001,114.697,97.404],Ko="tiny_yolov2_model",Qo="tiny_yolov2_separable_conv_model";var N=b(g());var br=o=>typeof o=="number";function so(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!br(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>br(t.x)&&br(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(br)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var Q=b(g());var K=b(g());function ge(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Tt(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),ge(e)})}var At=b(g());function Pt(o,t){return At.tidy(()=>{let e=At.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=At.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=At.add(e,t.bias),ge(e)})}var io=b(g());function qn(o,t){let e=me(o,t);function r(s,i){let c=io.tensor1d(o(s)),m=io.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=pe(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function tn(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=B(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=qn(n,s),p;if(t.withSeparableConvs){let[d,u,f,v,_,h,y,T,F]=r,L=t.isFirstLayerConv2d?i(d,u,3,"conv0"):m(d,u,"conv0"),G=m(u,f,"conv1"),et=m(f,v,"conv2"),it=m(v,_,"conv3"),X=m(_,h,"conv4"),_t=m(h,y,"conv5"),wt=T?m(y,T,"conv6"):void 0,Dt=F?m(T,F,"conv7"):void 0,te=i(F||T||y,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:_t,conv6:wt,conv7:Dt,conv8:te}}else{let[d,u,f,v,_,h,y,T,F]=r,L=c(d,u,"conv0"),G=c(u,f,"conv1"),et=c(f,v,"conv2"),it=c(v,_,"conv3"),X=c(_,h,"conv4"),_t=c(h,y,"conv5"),wt=c(y,T,"conv6"),Dt=c(T,F,"conv7"),te=i(F,5*e,1,"conv8");p={conv0:L,conv1:G,conv2:et,conv3:it,conv4:X,conv5:_t,conv6:wt,conv7:Dt,conv8:te}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function Zn(o,t){let e=j(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=de(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function en(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=Zn(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var ft=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var co=class extends S{constructor(t){super("TinyYolov2");so(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=Tt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=Tt(r,e.conv6),r=Tt(r,e.conv7),zt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?ge(zt(t,e.conv0,"valid",!1)):Pt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Pt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Pt(r,e.conv6):r,r=e.conv7?Pt(r,e.conv7):r,zt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?ot(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new ft(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),f=m.map(h=>this.config.classes[h.label]);return Ar(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Et(d[h],u[h],f[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return en(t,this.config)}extractParams(t){let e=this.config.filterSizes||co.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return tn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,f]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],_=await u.array(),h=await d.array();for(let y=0;y<m;y++)for(let T=0;T<m;T++)for(let F=0;F<p;F++){let L=Ee(_[y][T][F][0]);if(!r||L>r){let G=(T+Ee(h[y][T][F][0]))/m*i,et=(y+Ee(h[y][T][F][1]))/m*c,it=Math.exp(h[y][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[y][T][F][3])*this.config.anchors[F].y/m*c,_t=G-it/2,wt=et-X/2,Dt={row:y,col:T,anchor:F},{classScore:te,label:lo}=this.withClassScores?await this.extractPredictedClass(f,Dt):{classScore:1,label:0};v.push({box:new re(_t,wt,_t+it,wt+X),score:L,classScore:L*te,label:lo,...Dt})}}return d.dispose(),u.dispose(),f.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ve=co;ve.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ye=class extends ve{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Xo,classes:["face"],...t?{anchors:qo,meanRgb:Zo}:{anchors:Jo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?Qo:Ko}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Kn(o,t=!0){let e=new ye(t);return e.extractWeights(o),e}var gr=class extends ft{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var tt=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var He=b(g());var mo=b(g());async function Jt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Vt(c)?n(c):c.detection),s=r||(t instanceof mo.Tensor?await ie(t,a):await se(t,a)),i=await e(s);return s.forEach(c=>c instanceof mo.Tensor&&c.dispose()),i}async function Fe(o,t,e,r,n){return Jt([o],t,async a=>e(a[0]),r,n)}var rn=.4,on=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],nn=[117.001,114.697,97.404];var Te=class extends ve{constructor(){let t={withSeparableConvs:!0,iouThreshold:rn,classes:["face"],anchors:on,meanRgb:nn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new Xt,tinyFaceDetector:new Te,tinyYolov2:new ye,faceLandmark68Net:new he,faceLandmark68TinyNet:new dr,faceRecognitionNet:new be,faceExpressionNet:new cr,ageGenderNet:new pr},an=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),Qn=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ta=(o,t)=>P.tinyYolov2.locateFaces(o,t),sn=o=>P.faceLandmark68Net.detectLandmarks(o),ea=o=>P.faceLandmark68TinyNet.detectLandmarks(o),ra=o=>P.faceRecognitionNet.computeFaceDescriptor(o),oa=o=>P.faceExpressionNet.predictExpressions(o),na=o=>P.ageGenderNet.predictAgeAndGender(o),cn=o=>P.ssdMobilenetv1.load(o),aa=o=>P.tinyFaceDetector.load(o),sa=o=>P.tinyYolov2.load(o),ia=o=>P.faceLandmark68Net.load(o),ca=o=>P.faceLandmark68TinyNet.load(o),ma=o=>P.faceRecognitionNet.load(o),pa=o=>P.faceExpressionNet.load(o),da=o=>P.ageGenderNet.load(o),ua=cn,fa=an,la=sn;var po=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},we=class extends po{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>mr(r,e[n]))}withAgeAndGender(){return new Pe(this,this.input)}},De=class extends po{async run(){let t=await this.parentTask;if(!t)return;let e=await Fe(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new _e(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new Wt(this,this.input)}},Qt=class extends De{withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var uo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Pe=class extends uo{async run(){let t=await this.parentTask,e=await Jt(t,this.input,async r=>Promise.all(r.map(n=>P.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return hr(xr(r,s,i),a)})}withFaceExpressions(){return new we(this,this.input)}},_e=class extends uo{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Fe(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return hr(xr(t,r,n),e)}withFaceExpressions(){return new De(this,this.input)}},qt=class extends Pe{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptors(){return new Wt(this,this.input)}},Zt=class extends _e{withFaceExpressions(){return new Qt(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},Wt=class extends vr{async run(){let t=await this.parentTask;return(await Jt(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>lr(t[n],r))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}},Bt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await Fe(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return lr(t,e)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}};var yr=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},Fr=class extends yr{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof He.Tensor?await ie(this.input,e):await se(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof He.Tensor&&a.dispose()),t.map((a,s)=>le(a,n[s]))}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptors(){return new Wt(this,this.input)}},Tr=class extends yr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof He.Tensor?await ie(this.input,[e]):await se(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof He.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new Qt(this,this.input)}withAgeAndGender(){return new Zt(this,this.input)}withFaceDescriptor(){return new Bt(this,this.input)}};var Pr=class extends tt{constructor(t,e=new Z){super();this.input=t;this.options=e}},Ye=class extends Pr{async run(){let{input:t,options:e}=this,r=e instanceof gr?n=>P.tinyFaceDetector.locateFaces(n,e):e instanceof Z?n=>P.ssdMobilenetv1.locateFaces(n,e):e instanceof ft?n=>P.tinyYolov2.locateFaces(n,e):null;if(!r)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return r(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>jt({},r)))})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Pe(this.runAndExtendWithFaceDetections(),this.input)}},_r=class extends Pr{async run(){let t=await new Ye(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?jt({},e):void 0)})}withFaceLandmarks(t=!1){return new Tr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new De(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new _e(this.runAndExtendWithFaceDetection(),this.input)}};function ha(o,t=new Z){return new _r(o,t)}function wr(o,t=new Z){return new Ye(o,t)}async function mn(o,t){return wr(o,new Z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function xa(o,t={}){return wr(o,new ft(t)).withFaceLandmarks().withFaceDescriptors()}var ba=mn;function fo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a**2,0))}var Dr=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof bt)return s;if(s instanceof Float32Array)return new bt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new bt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>fo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this.distanceThreshold?e:new Me("unknown",e.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>bt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function ga(o){let t=new Te;return t.extractWeights(o),t}function pn(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>pn(n,{width:e,height:r}));if(Vt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof V||o instanceof M?o.forSize(e,r):o}var ya=typeof process!="undefined",Fa=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Ta={faceapi:Eo,node:ya,browser:Fa};export{pr as AgeGenderNet,re as BoundingBox,D as Box,tt as ComposableTask,Wt as ComputeAllFaceDescriptorsTask,vr as ComputeFaceDescriptorsTaskBase,Bt as ComputeSingleFaceDescriptorTask,Fr as DetectAllFaceLandmarksTask,Ye as DetectAllFacesTask,yr as DetectFaceLandmarksTaskBase,Pr as DetectFacesTaskBase,Tr as DetectSingleFaceLandmarksTask,_r as DetectSingleFaceTask,A as Dimensions,qr as FACE_EXPRESSION_LABELS,M as FaceDetection,Uo as FaceDetectionNet,cr as FaceExpressionNet,Lt as FaceExpressions,he as FaceLandmark68Net,dr as FaceLandmark68TinyNet,Ao as FaceLandmarkNet,V as FaceLandmarks,bo as FaceLandmarks5,ne as FaceLandmarks68,Me as FaceMatch,Dr as FaceMatcher,be as FaceRecognitionNet,yt as Gender,Ce as LabeledBox,bt as LabeledFaceDescriptors,gt as NetInput,S as NeuralNetwork,Et as ObjectDetection,x as Point,go as PredictedBox,oe as Rect,Xt as SsdMobilenetv1,Z as SsdMobilenetv1Options,Te as TinyFaceDetector,gr as TinyFaceDetectorOptions,ye as TinyYolov2,ft as TinyYolov2Options,ba as allFaces,mn as allFacesSsdMobilenetv1,xa as allFacesTinyYolov2,Gr as awaitMediaLoaded,zr as bufferToImage,ra as computeFaceDescriptor,ae as createCanvas,Le as createCanvasFromMedia,Jn as createFaceDetectionNet,Rn as createFaceRecognitionNet,Vo as createSsdMobilenetv1,ga as createTinyFaceDetector,Kn as createTinyYolov2,wr as detectAllFaces,sn as detectFaceLandmarks,ea as detectFaceLandmarksTiny,la as detectLandmarks,ha as detectSingleFace,to as draw,w as env,fo as euclideanDistance,hr as extendWithAge,lr as extendWithFaceDescriptor,jt as extendWithFaceDetection,mr as extendWithFaceExpressions,le as extendWithFaceLandmarks,xr as extendWithGender,ie as extractFaceTensors,se as extractFaces,Mn as fetchImage,Xr as fetchJson,Cn as fetchNetWeights,Gt as fetchOrThrow,$ as getContext2dOrThrow,Yt as getMediaDimensions,Vr as imageTensorToCanvas,Ur as imageToSquare,vn as inverseSigmoid,kr as iou,qe as isMediaElement,Ie as isMediaLoaded,$n as isWithAge,pt as isWithFaceDetection,Zr as isWithFaceExpressions,Vt as isWithFaceLandmarks,On as isWithGender,da as loadAgeGenderModel,ua as loadFaceDetectionModel,pa as loadFaceExpressionModel,ia as loadFaceLandmarkModel,ca as loadFaceLandmarkTinyModel,ma as loadFaceRecognitionModel,cn as loadSsdMobilenetv1Model,aa as loadTinyFaceDetectorModel,sa as loadTinyYolov2Model,Jr as loadWeightMap,fa as locateFaces,Nn as matchDimensions,Sr as minBbox,P as nets,Ar as nonMaxSuppression,ot as normalize,Wr as padToSquare,na as predictAgeAndGender,oa as recognizeFaceExpressions,pn as resizeResults,Ht as resolveInput,gn as shuffleArray,Ee as sigmoid,an as ssdMobilenetv1,va as tf,Qn as tinyFaceDetector,ta as tinyYolov2,E as toNetInput,Cr as utils,so as validateConfig,Ta as version};
 //# sourceMappingURL=face-api.esm-nobundle.js.map
diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js
index 518417c..5f0d770 100644
--- a/dist/face-api.esm.js
+++ b/dist/face-api.esm.js
@@ -5,43 +5,43 @@
   author: <https://github.com/vladmandic>'
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Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${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 YE(this.backendInstance),!0}setupRegisteredKernels(){Xd(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Xd(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 sc)&&typeof n.then=="function"){let a=++this.pendingBackendInitId,r=n.then(s=>a<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(a<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,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:a,asyncInit:r}=this.initializeBackend(n);if(r||a)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),a=n.backend,r=this.readSync(t);a.disposeData(t),n.backend=e,e.move(t,r,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to 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this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=ay(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(ay(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=Kd(h,this.backendName);A(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let b=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,b);let v=b.map(x=>{if(x.rank!=null)return x;let{dataId:N,shape:T,dtype:C}=x;return this.makeTensorFromDataId(N,T,C)});if(a){let x=this.getTensorsForGradient(h,m,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:c,attrs:u}=e,p=ay(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)}),a&&this.addTapeNode(l,c,t,p,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(h=>c[h]!=null?c[h].shape:null),outputShapes:t.map(h=>h.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 a=Kg(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(A(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,c)=>s[c]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&Pr(e[0])&&(r=e.map(o=>Cc(o)));let s=a.write(r,t,n),i=new Ae(t,n,s,this.nextTensorId());if(this.incRef(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=Yw(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new Ae(t,n,e,this.nextTensorId());return this.incRef(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new Vr(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}incRef(e,t){let n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let a=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(a=e.size*Xw(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:a,refCount:0}),this.state.numBytes+=a}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof Vr||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;let t=this.state.tensorInfo.get(e.dataId);t.refCount<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):(t.backend.decComplexRef(e.dataId),this.state.tensorInfo.get(e.dataId).refCount--)}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(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of 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s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(A(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));A(r instanceof Ae,()=>"The result y returned by f() must be a tensor.");let s=ZE(this.state.activeTape,t,r);if(!a&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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+`;return d[d.length-1]=" "+d[d.length-1]+"]"+(s?"":m),d}function Ac(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Ot=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Pt(e),n!=null){let a=n.length;A(a===this.size,()=>`Length of values '${a}' does not match the size inferred by the shape '${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||Jw(t,this.size),this.strides=So(e)}set(e,...t){t.length===0&&(t=[0]),A(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 a of e){if(a<0||a>=this.shape[t]){let r=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(r)}t++}let n=e[e.length-1];for(let a=0;a<e.length-1;++a)n+=this.strides[a]*e[a];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 Va().makeTensor(this.values,this.shape,this.dtype)}},Va=null,_l=null,oA=null;function lA(e){Va=e}function uA(e){_l=e}function cA(e){oA=e}var Ae=class{constructor(e,t,n,a){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Pt(e),this.strides=So(e),this.dataId=n,this.id=a,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return _l.buffer(this.shape,this.dtype,e)}bufferSync(){return _l.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Co(this.shape,e)}arraySync(){return Co(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let e=Va().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>eh(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=Va().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>eh(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 Va().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Va().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return _l.print(this,e)}clone(){return this.throwIfDisposed(),_l.clone(this)}toString(e=!1){let t=this.dataSync();return iA(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),_l.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Va().makeVariable(this,e,t,n)}};Object.defineProperty(Ae,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Y(){return Yg("Tensor",()=>Ae)}Y();var Ur=class extends Ae{constructor(e,t,n,a){super(e.shape,e.dtype,e.dataId,a);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(!mr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Va().disposeTensor(this),this.dataId=e.dataId,Va().incRef(this,null)}dispose(){Va().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Ur,Symbol.hasInstance,{value:e=>e instanceof Ae&&e.assign!=null&&e.assign instanceof Function});var Na={};Oe(Na,{assertTypesMatch:()=>h0,getTensorsInContainer:()=>ey,isTensorInList:()=>pA,makeTypesMatch:()=>Nt});var ty;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(ty||(ty={}));var ny;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(ny||(ny={}));var ay;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(ay||(ay={}));var ry;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(ry||(ry={}));var sy;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(sy||(sy={}));var dA={float32:ry,int32:ny,bool:ay,complex64:sy};function ua(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return dA[e][t]}function nh(e){return ua(e,"int32")}function Nt(e,t){if(e.dtype===t.dtype)return[e,t];let n=ua(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function h0(e,t){A(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function pA(e,t){return t.some(n=>n.id===e.id)}function ey(e){let t=[],n=new Set;return m0(e,t,n),t}function m0(e,t,n){if(e==null)return;if(e instanceof Ae){t.push(e);return}if(!hA(e))return;let a=e;for(let r in a){let s=a[r];n.has(s)||(n.add(s),m0(s,t,n))}}function hA(e){return Array.isArray(e)||typeof e=="object"}function iy(e){return e.kernelName!=null}var f0=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Fc=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new f0}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;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 nA(this.backendInstance),!0}setupRegisteredKernels(){Jd(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Jd(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 rc)&&typeof n.then=="function"){let a=++this.pendingBackendInitId,r=n.then(s=>a<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(a<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,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:a,asyncInit:r}=this.initializeBackend(n);if(r||a)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),a=n.backend,r=this.readSync(t),s=a.refCount(t);a.disposeData(t,!0),n.backend=e,e.move(t,r,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 a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=iy(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(iy(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=Zd(h,this.backendName);A(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let b=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,b);let v=b.map(x=>{if(x.rank!=null)return x;let{dataId:N,shape:T,dtype:C}=x;return this.makeTensorFromDataId(N,T,C)});if(a){let x=this.getTensorsForGradient(h,m,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:c,attrs:u}=e,p=iy(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)}),a&&this.addTapeNode(l,c,t,p,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(h=>c[h]!=null?c[h].shape:null),outputShapes:t.map(h=>h.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 a=Jg(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(A(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,c)=>s[c]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&Or(e[0])&&(r=e.map(o=>Cc(o)));let s=a.write(r,t,n),i=new Ae(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=t0(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new Ae(t,n,e,this.nextTensorId());return this.trackTensor(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new Ur(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Gg(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 Ur||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*Gg(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(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of 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UP=P({softmaxCrossEntropy_:VP}),GP={fft:jc,ifft:Ul,rfft:qc,irfft:Eh},HP={hammingWindow:VM,hannWindow:P1,frame:O1,stft:jM},Ya={flipLeftRight:YM,resizeNearestNeighbor:U1,resizeBilinear:V1,rotateWithOffset:JM,cropAndResize:KM,nonMaxSuppression:eP,nonMaxSuppressionAsync:lP,nonMaxSuppressionWithScore:cP,nonMaxSuppressionWithScoreAsync:dP,nonMaxSuppressionPadded:mP,nonMaxSuppressionPaddedAsync:gP},H1={bandPart:vP,gramSchmidt:kP,qr:NP},jP={absoluteDifference:CP,computeWeightedLoss:yr,cosineDistance:EP,hingeLoss:FP,huberLoss:DP,logLoss:MP,meanSquaredError:OP,sigmoidCrossEntropy:BP,softmaxCrossEntropy:UP},br=class extends H0{minimize(e,t=!1,n){let{value:a,grads:r}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(s)}else this.applyGradients(r);return Fe(r),t?a:(a.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return g1(e,t)}dispose(){this.iterations_!=null&&Fe(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:de(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(br,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Lh=class extends br{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=M.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:D(()=>Ge(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:D(()=>Ge(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;D(()=>{let l=Z(L(i,this.rho),L(lt(s),1-this.rho)),c=L(ve(en(Z(o,this.epsilon)),en(Z(i,this.epsilon))),s),u=Z(L(o,this.rho),L(lt(c),1-this.rho));i.assign(l),o.assign(u);let 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br{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=M.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:D(()=>wn(a.shape,this.initialAccumulatorValue).variable(i))}}let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;D(()=>{let i=Z(s,lt(r));s.assign(i);let o=Z(L(ve(r,en(Z(i,M.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Fe(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Wh.className="Adamax";qr(Wh);var Kc=class extends br{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=M.registeredVariables[t];D(()=>{let s=Z(L(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=jt(de(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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VM({x:e,filter:t,strides:n,pad:a,dataFormat:r="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(l=l||"linear",Bh(M.state.gradientDepth,l)===!1){let N=At(e,t,n,a,r,s,i);return o!=null&&(N=Z(N,o)),zh(N,l,c,u)}let p=E(e,"x","conv2d"),d=E(t,"filter","conv2d"),h=p,m=!1;p.rank===3&&(m=!0,h=H(p,[1,p.shape[0],p.shape[1],p.shape[2]])),A(h.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`),A(d.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`),i!=null&&A(Gt(a),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`),A(h.shape[3]===d.shape[2],()=>`Error in conv2d: depth of input (${h.shape[3]}) must match input depth for filter ${d.shape[2]}.`),A(ja(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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n=E(e,"labels","sigmoidCrossEntropyWithLogits"),a=E(t,"logits","sigmoidCrossEntropyWithLogits");sn(n.shape,a.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Ye(a),s=L(a,n),i=xh(pn(Tt(Lt(a))));return Z(fe(r,s),i)}function HP(e,t,n,a=0,r=dn.SUM_BY_NONZERO_WEIGHTS){let s=E(e,"multiClassLabels","sigmoidCrossEntropy"),i=E(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=E(n,"weights","sigmoidCrossEntropy")),sn(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),a>0){let c=de(a),u=de(1),p=de(.5);s=Z(L(s,fe(u,c)),L(p,c))}let l=GP(s,i);return br(l,o,r)}var jP=O({sigmoidCrossEntropy_:HP});function qP(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);return Ka((a,r,s)=>{let i=Xy(r,[n],!0),o=fe(ce(r,"float32"),i);s([a,o]);let l=Tt(L(o,a));return{value:Te(l,[n]),gradFunc:(c,u)=>{let[p,d]=u,h=Ei(c.shape,[n]);return[L(H(c,h),fe(ce(p,"float32"),pn(d))),L(H(c,h),fe(pn(d),ce(p,"float32")))]}}})(e,t)}function KP(e,t,n,a=0,r=dn.SUM_BY_NONZERO_WEIGHTS){let s=E(e,"onehotLabels","softmaxCrossEntropy"),i=E(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=E(n,"weights","softmaxCrossEntropy")),sn(s.shape,i.shape,"Error in softmaxCrossEntropy: "),a>0){let c=de(a),u=de(1),p=de(s.shape[1]);s=Z(L(s,fe(u,c)),ve(c,p))}let l=qP(s,i);return br(l,o,r)}var XP=O({softmaxCrossEntropy_:KP}),YP={fft:jc,ifft:Ul,rfft:qc,irfft:$h},ZP={hammingWindow:KM,hannWindow:W1,frame:V1,stft:JM},Za={flipLeftRight:nP,resizeNearestNeighbor:K1,resizeBilinear:q1,rotateWithOffset:rP,cropAndResize:eP,nonMaxSuppression:iP,nonMaxSuppressionAsync:mP,nonMaxSuppressionWithScore:gP,nonMaxSuppressionWithScoreAsync:bP,nonMaxSuppressionPadded:vP,nonMaxSuppressionPaddedAsync:kP},Y1={bandPart:SP,gramSchmidt:_P,qr:AP},JP={absoluteDifference:DP,computeWeightedLoss:br,cosineDistance:MP,hingeLoss:OP,huberLoss:zP,logLoss:WP,meanSquaredError:UP,sigmoidCrossEntropy:jP,softmaxCrossEntropy:XP},xr=class extends Y0{minimize(e,t=!1,n){let{value:a,grads:r}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(s)}else this.applyGradients(r);return Fe(r),t?a:(a.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return w1(e,t)}dispose(){this.iterations_!=null&&Fe(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:de(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(xr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Wh=class extends xr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=M.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:D(()=>Ge(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:D(()=>Ge(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;D(()=>{let l=Z(L(i,this.rho),L(lt(s),1-this.rho)),c=L(ve(en(Z(o,this.epsilon)),en(Z(i,this.epsilon))),s),u=Z(L(o,this.rho),L(lt(c),1-this.rho));i.assign(l),o.assign(u);let p=Z(L(c,-this.learningRate),a);a.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Fe(this.accumulatedGrads.map(e=>e.variable)),Fe(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Wh.className="Adadelta";Kr(Wh);var Vh=class extends xr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=M.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:D(()=>wn(a.shape,this.initialAccumulatorValue).variable(i))}}let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;D(()=>{let i=Z(s,lt(r));s.assign(i);let o=Z(L(ve(r,en(Z(i,M.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Fe(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Vh.className="Adagrad";Kr(Vh);var Uh=class extends xr{constructor(e,t,n,a=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],D(()=>{this.accBeta1=de(t).variable(),this.accBeta2=de(n).variable()}),a==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);D(()=>{let n=fe(1,this.accBeta1),a=fe(1,this.accBeta2);t.forEach((r,s)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:D(()=>Ge(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:D(()=>Ge(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedSecondMoment[s].variable,p=Z(L(c,this.beta1),L(l,1-this.beta1)),d=Z(L(u,this.beta2),L(lt(l),1-this.beta2)),h=ve(p,n),m=ve(d,a);c.assign(p),u.assign(d);let 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t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Uh.className="Adam";Kr(Uh);var Gh=class extends xr{constructor(e,t,n,a=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],D(()=>{this.iteration=de(0).variable(),this.accBeta1=de(t).variable()}),a==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);D(()=>{let n=fe(1,this.accBeta1),a=ve(-this.learningRate,Z(L(this.iteration,this.decay),1));t.forEach((r,s)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Gh.className="Adamax";Kr(Gh);var Kc=class extends xr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=M.registeredVariables[t];D(()=>{let s=Z(L(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=jt(de(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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a=M.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:D(()=>Ge(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:D(()=>Ge(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:D(()=>Ge(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;D(()=>{let l=Z(L(i,this.decay),L(lt(s),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[n].variable,u=Z(L(c,this.decay),L(s,1-this.decay)),p=ve(L(s,this.learningRate),en(fe(l,Z(lt(u),this.epsilon)))),d=Z(L(o,this.momentum),p);i.assign(l),c.assign(u),o.assign(d);let h=fe(a,d);a.assign(h)}else{let c=Z(L(i,this.decay),L(lt(s),1-this.decay)),u=Z(L(o,this.momentum),ve(L(s,this.learningRate),en(Z(c,this.epsilon))));i.assign(c),o.assign(u);let p=fe(a,u);a.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Fe(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Fe(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Fe(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};jh.className="RMSProp";Kr(jh);var $i=class{static sgd(e){return new Kc(e)}static momentum(e,t,n=!1){return new Hh(e,t,n)}static rmsprop(e,t=.9,n=0,a=null,r=!1){return new jh(e,t,n,a,r)}static adam(e=.001,t=.9,n=.999,a=null){return new Uh(e,t,n,a)}static adadelta(e=.001,t=.95,n=null){return new Wh(e,t,n)}static adamax(e=.002,t=.9,n=.999,a=null,r=0){return new Gh(e,t,n,a,r)}static 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this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},CB=0,qe=class extends re.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=CB++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=vr(n)+"_"+nm(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let a=e.dtype;a==null&&(a=e.inputDType),a==null&&(a="float32"),this.dtype=a}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new _a(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new z(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return kn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return kn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new xr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Input received: ${e}`);for(let n=0;n<e.length;n++){let a=e[n],r=t[n];if(r==null)continue;let s=a.rank;if(r.ndim!=null&&s!==r.ndim)throw new z(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${s}`);if(r.maxNDim!=null&&s>r.maxNDim)throw new z(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${s}`);if(r.minNDim!=null&&s<r.minNDim)throw new z(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${s}.`);if(r.dtype!=null&&a.dtype!==r.dtype)throw new z(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${a.dtype}.`);if(r.axes){let i=a.shape;for(let o in r.axes){let l=Number(o),c=r.axes[o],u=l>=0?i[l]:i[i.length+l];if(c!=null&&[c,null].indexOf(u)===-1)throw new z(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${c} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],l=a.shape[i];if(o!=null&&l!=null&&o!==l)throw new z(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${a.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=yt(e),a=!0;for(let s of n)if(!(s instanceof Aa)){a=!1;break}let r=!0;for(let s of n)if(s instanceof Aa){r=!1;break}if(a===r)throw new z("Arguments to apply() must be all SymbolicTensors or all Tensors");return Mi(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of yt(e))s.push(i.shape);this.build(kn(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let s=this.call(e,t),i=yt(s),o=[];for(let l of i)n.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=kn(o),this.activityRegularizer!=null)throw new $e("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=_B(e),i=this.computeOutputShape(s),o,l=EB(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((c,u)=>new Aa(l,c,this,yt(e),t,this.name,u)):o=new Aa(l,i,this,yt(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new $e("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,a)=>{n!=null&&e[a]!=null&&e[a]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new xr(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new xr(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new _a(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return rm(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Pb(e?this.trainableWeights:this.weights)}setWeights(e){D(()=>{let t=this.weights;if(t.length!==e.length)throw new z(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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extends qe{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=nm(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],ts(this.inputs).length!==this.inputs.length)throw new z(`The list of inputs passed to the model is redundant. 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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 b=y.sourceLayer,v=y.nodeIndex,x=y.tensorIndex;this.outputLayers.push(b),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let y of this.inputs){let b=y.sourceLayer,v=y.nodeIndex,x=y.tensorIndex;Ja(v===0,"input layer has >1 nodes"),Ja(x===0,"input layer has >1 tensors"),this.inputLayers.push(b),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let b=this.inputLayers[y];if(!(b instanceof Kl))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${b.getClassName()}.`);this.inputNames.push(b.name),this.feedInputShapes.push(b.batchInputShape),this.feedInputNames.push(b.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={},a={},r={},s={},i=[],o=(y,b,v,x,N,T)=>{(x==null||N==null||T==null)&&(x=y.sourceLayer,N=y.nodeIndex,T=y.tensorIndex);let C=x.inboundNodes[N];if(v.indexOf(C)!==-1)throw new _a(`The tensor ${y.name} at layer "${x.name}" is part of a cycle.`);if(b.indexOf(C)!==-1)return;this.containerNodes.add(nr.nodeKey(x,N)),x.id in s||(s[x.id]=Object.keys(s).length),v.indexOf(C)===-1&&v.push(C);let $=C.inboundLayers.length;for(let F=0;F<$;F++){let O=C.inputTensors[F],B=C.inboundLayers[F],W=C.nodeIndices[F],G=C.tensorIndices[F];o(O,b,v,B,W,G)}for(b.push(C);v.indexOf(C)>=0;)v.splice(v.indexOf(C),1);i.push(C)},l=[],c=[];for(let y of this.outputs)o(y,l,c);let u=i.slice().reverse();for(let y of u){n[y.id]=y,y.id in t||(t[y.id]=0);let b=t[y.id],v=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];b=Math.max(b,v),a[y.outboundLayer.id]=b,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=b;for(let x=0;x<y.inboundLayers.length;x++){let N=y.inboundLayers[x],T=y.nodeIndices[x],C=N.inboundNodes[T],$=t[C.id]==null?0:t[C.id];t[C.id]=Math.max(b+1,$),n[C.id]=C}}let p={};for(let y in t){let b=t[y];b in p||(p[b]=[]),p[b].push(n[y])}let d={};for(let y in a){let b=a[y];b in d||(d[b]=[]),d[b].push(r[y])}let h=Object.keys(d).map(y=>parseInt(y,10)).sort(Hh);this.layers=[];for(let y of h){let b=d[y];b.sort((v,x)=>{let N=s[v.id],T=s[x.id];return N<T?-1:N>T?1:0});for(let v of b)v instanceof nr&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(y=>parseInt(y,10)).sort(Hh);let m=this.inputs.slice(),f=[];for(let y of h)for(let b of p[y]){let v=b.outboundLayer;if(v!=null){for(let x of b.inputTensors)if(m.indexOf(x)===-1)throw new _a(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${f}`);for(let x of b.outputTensors)m.push(x);f.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(y=>y.name);for(let y of g){let b=g.filter(v=>v===y).length;if(b!==1)throw new _a(`The name "${y}" is used ${b} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new sm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new z("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new z(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,a++}let r=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[s]]);else if(t)throw new z(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new z(`${s.length} of ${a} weights are not set: ${s}`)}Ob(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${mm}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=Gb(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return D(()=>{e=yt(e);let n=new Li;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return sp(this.outputs,n,t)})}computeMask(e,t){return D(()=>{e=yt(e);let n;return t==null?n=$i(null,e.length):n=yt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=am(e);if(t.length!==this.inputLayers.length)throw new z(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Hh);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(c.id)!==-1)continue;let u=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],b=`${f.name}_${g}_${y}`,v=n[b];u.push(v)}let p=c.computeOutputShape(kn(u)),d=am(p),h=c.inboundNodes.indexOf(l);for(let m=0;m<d.length;m++){let f=`${c.name}_${h}_${m}`;n[f]=d[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${c}`;s.push(u)}for(let i=0;i<s.length;i++){let o=s[i];Ja(o in n),r.push(n[o])}return kn(r)}runInternalGraph(e,t){t==null&&(t=$i(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],c=e[o],u=t[o];n[l.id]=[c,u]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Hh);for(let o of a){let l=this.nodesByDepth[o];for(let c of l){let u=c.outboundLayer,p=c.inputTensors,d=c.outputTensors,h=new Array;for(let m of p)m.id in n&&h.push(n[m.id]);if(h.length===p.length){let m={},f,g,y,b;if(c.callArgs!=null&&(m=c.callArgs),h.length===1){let[v,x]=h[0];m.mask==null&&(m.mask=x),y=yt(u.call(v,m)),b=yt(u.computeMask(v,x)),f=[v],g=[x]}else f=h.map(v=>v[0]),g=h.map(v=>v[1]),m.mask==null&&(m.mask=g),y=yt(u.call(f,m)),b=yt(u.computeMask(f,g));if(u.activityRegularizer)throw new $e("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],N=y[v],T=b[v];n[x.id]=[N,T]}}}}let r=[],s=[],i=[];for(let o of this.outputs){Ja(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,c]=n[o.id];i.push(l.shape),r.push(l),s.push(c)}return[r,s,i]}buildNodeConversionMap(e){let t={},n;for(let a of this.layers){n=a instanceof nr?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=nr.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new z(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new z("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new z(`No such layer: ${e}`)}calculateLosses(){return D(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let a=nr.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let u=0;u<s.inboundNodes.length;u++){let p=s.inboundNodes[u],d=nr.nodeKey(s,u),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let m=[];for(let f=0;f<p.inboundLayers.length;f++){let g=p.inboundLayers[f],y=p.nodeIndices[f],b=p.tensorIndices[f],v=nr.nodeKey(g,y),x=t[v];x==null&&(x=0),m.push([g.name,x,b,h])}l.push(m)}}}let c={};c.name=s.name,c.className=i,c.config=o,c.inboundNodes=l,n.push(c)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=nr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[s];a.push([i.name,c,u])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=nr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[s];r.push([i.name,c,u])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(f,g){f.name in s?s[f.name].push(g):s[f.name]=[g]}function o(f,g){let y=[],b;for(let v of g){let x=v[0],N=v[1],T=v[2];if(b=v[3]==null?{}:v[3],!(x in r)){i(f,g);return}let C=r[x];if(C.inboundNodes.length<=N){i(f,g);return}let $=C.inboundNodes[N];y.push($.outputTensors[T])}y.length>0&&f.apply(kn(y),b)}function l(f){let g=f.name,y=Fa(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),r[g]=y,f.inboundNodes.forEach(b=>{if(!(b instanceof Array))throw new z(`Corrupted configuration, expected array for nodeData: ${b}`);i(y,b)})}let c=t.name,u=t.layers;for(let f of u)l(f);for(;!$3(s);)for(let f of u){let g=r[f.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let b of y)o(g,b)}}let p=[],d=[],h=t.inputLayers;for(let f of h){let g=f[0],y=f[1],b=f[2];Ja(g in r);let v=r[g].inboundNodes[y].outputTensors;p.push(v[b])}let m=t.outputLayers;for(let f of m){let g=f[0],y=f[1],b=f[2];Ja(g in r);let v=r[g].inboundNodes[y].outputTensors;d.push(v[b])}return new e({inputs:p,outputs:d,name:c})}get stateful(){if(this._stateful)throw new z("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){D(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function oW(e,t,n){let a=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(a===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==a)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${a} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(s=>{s in e?r.push(e[s]):r.push(null)}),r}else throw new Error(`The model has multiple (${a}) outputs, so ${n} must be either an array with ${a} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function Yk(e,t){return oW(e,t,"classWeight")}async function Zk(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=D(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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 r.data());Fe(r);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])}),tt(i,"float32")}else return null}function lW(e,t){return L(e,t)}var uW=32;function Qk(e,t){let n,a,r=t;n=r.xs,a=r.ys,k.assert(n!=null&&a!=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=Jk("input",e.inputNames,n),i=Jk("output",e.outputNames,a),o=s[0].shape[0];k.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)})`),k.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)k.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++)k.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 Jk(e,t,n){if(n instanceof Ae)return[n];if(Array.isArray(n))return k.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let a=[];for(let r of t){if(n[r]==null)throw new z(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function cW(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 dW(e,t,n){let a=n.batchesPerEpoch!=null;if(k.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k.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}`),k.assert(!a||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,s,i;if(r)if(eI(n.validationData))k.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=cW(n.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;r?c=l.slice().concat(l.map(g=>"val_"+g)):c=l.slice();let u=Lk(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=zk(u,p,n.epochs,null,null,pW(t,n),null,r,c);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let m=n.initialEpoch==null?0:n.initialEpoch,f=await t.iterator();for(;m<n.epochs;){let g={};await d.onEpochBegin(m);let y=0,b=0;for(a||(f=await t.iterator());a?y<n.batchesPerEpoch:!0;){let v=await f.next();if(a&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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e.metrics)r[s]=Di(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=Ht.getSaveHandlers(e);if(i.length===0)throw new z(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new z(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new z("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Ht.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:wW,generatedBy:`TensorFlow.js tfjs-layers v${mm}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Ht.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=Ht.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let 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compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new _a("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new _a("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new _a("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},a=!1){let r,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new z("Legacy serialization format not supported yet.");r=t}else k.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."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Zl))throw new $e(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Fa(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new z("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 z("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Zl.className="Sequential";re.registerClass(Zl);function SW(e){return new wr(e)}function CW(e){return new Zl(e)}function _W(e,t){return t==null&&(t={}),NW(e,t)}function Ik(e){return $k(e)}function EW(e,t){ma.registerCallbackConstructor(e,t)}var Pn=class extends re.Serializable{getConfig(){return{}}},iI=class extends Pn{apply(e,t=1){return oz(e,t)}};iI.className="elu";re.registerClass(iI);var oI=class extends Pn{apply(e){return Th(e)}};oI.className="selu";re.registerClass(oI);var lI=class extends Pn{apply(e){return Ye(e)}};lI.className="relu";re.registerClass(lI);var uI=class extends Pn{apply(e){return D(()=>Bl(6,Ye(e)))}};uI.className="relu6";re.registerClass(uI);var cI=class extends Pn{apply(e){return e}};cI.className="linear";re.registerClass(cI);var pI=class extends Pn{apply(e){return 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};ax.className="ThresholdedReLU";re.registerClass(ax);var rx=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Yb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Me(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}};rx.className="Softmax";re.registerClass(rx);function Jl(e,t,n){if(typeof e=="number")return $i(e,t);if(e.length!==t)throw new z(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){let r=e[a];if(!az(r))throw new z(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function $a(e,t,n,a,r=1){if(e==null)return e;let s=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+a-1)/a)}function fm(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+as([n-t,0]);else if(a==="same")e=e*t;else throw new z(`Unsupport padding mode: ${a}.`);return e}function sx(e,t){return D(()=>(Dt(t),t==="channelsFirst"?Ue(e,[0,2,3,1]):e))}function wI(e,t){return D(()=>(Dt(t),t==="channelsFirst"?Ue(e,[0,2,3,4,1]):e))}function $W(e,t,n,a=1,r="valid",s,i=1){return D(()=>{if(s==null&&(s=Ca()),Dt(s),e.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new z(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Ue(e,[0,2,1])),r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=ch(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=er(o,n)),o})}function kI(e,t,n,a=[1,1],r="valid",s,i,o=null){return D(()=>{if(s==null&&(s=Ca()),Dt(s),e.rank!==3&&e.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=sx(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=es.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Ue(l,[0,3,1,2])),l})}function DW(e,t,n,a=[1,1,1],r="valid",s,i){return D(()=>{if(s==null&&(s=Ca()),Dt(s),e.rank!==4&&e.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=wI(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Py(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=er(o,n)),s==="channelsFirst"&&(o=Ue(o,[0,4,1,2,3])),o})}var ix=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",ix.verifyArgs(t),this.rank=e,Kt(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=Jl(t.kernelSize,e,"kernelSize"),this.strides=Jl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Jn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Dt(this.dataFormat),this.activation=is(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=wt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Vt(t.biasConstraint),this.biasRegularizer=kt(t.biasRegularizer),this.activityRegularizer=kt(t.activityRegularizer),this.dilationRate=Jl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`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 z(`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 z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ja("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!fb(e.kernelSize,"number",1,3))throw new z(`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:ss(this.activation),useBias:this.useBias,biasInitializer:Ct(this.biasInitializer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},lp=class extends ix{constructor(e,t){super(e,t);this.kernel=null,lp.verifyArgs(t),this.filters=t.filters,Kt(this.filters,"filters"),this.kernelInitializer=wt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Vt(t.kernelConstraint),this.kernelRegularizer=kt(t.kernelRegularizer)}build(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return D(()=>{e=Me(e);let n,a=this.bias==null?null:this.bias.read(),r=ok(this.activation.getClassName());if(r!=null&&this.rank===2)n=kI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=$W(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=kI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=DW(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new $e("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ht(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let s=$a(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:Ct(this.kernelInitializer),kernelRegularizer:mt(this.kernelRegularizer),kernelConstraint:Wt(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 z(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},up=class extends lp{constructor(e){super(2,e);up.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!fb(e.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};up.className="Conv2D";re.registerClass(up);var gm=class extends lp{constructor(e){super(3,e);gm.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 z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};gm.className="Conv3D";re.registerClass(gm);var ox=class extends up{constructor(e){super(e);if(this.inputSpec=[new Xt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ht(e),e.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Xt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{let n=Me(e);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],c=this.kernelSize[0],u=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=fm(o,p,c,this.padding),m=fm(l,d,u,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ue(n,[0,2,3,1]));let g=ph(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ue(g,[0,3,1,2])),this.bias!=null&&(g=er(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ht(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=fm(t[a],o,s,this.padding),t[r]=fm(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ox.className="Conv2DTranspose";re.registerClass(ox);var II=class extends lp{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 z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new z("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 z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=kt(t.depthwiseRegularizer),this.depthwiseConstraint=Vt(t.depthwiseConstraint),this.pointwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=kt(t.pointwiseRegularizer),this.pointwiseConstraint=Vt(t.pointwiseConstraint)}build(e){if(e=ht(e),e.length<this.rank+2)throw new z(`Inputs to SeparableConv${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 z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"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 Xt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{e=Me(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=Ue(e,[0,2,3,1])),n=Ei(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=er(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ue(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=Ct(this.depthwiseInitializer),e.pointwiseInitializer=Ct(this.pointwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.pointwiseRegularizer=mt(this.pointwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseConstraint),e.pointwiseConstraint=Wt(this.pointwiseConstraint),e}};II.className="SeparableConv";var lx=class extends II{constructor(e){super(2,e)}};lx.className="SeparableConv2D";re.registerClass(lx);var ym=class extends lp{constructor(e){super(1,e);ym.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"&&!fb(e.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};ym.className="Conv1D";re.registerClass(ym);var ux=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 D(()=>{if(e=Me(e),this.dataFormat==="channelsLast"){let n=jh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return jh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=jh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return jh(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}};ux.className="Cropping2D";re.registerClass(ux);var cx=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,Dt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,ez(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 D(()=>{let n=Me(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ue(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s]);return Ue(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};cx.className="UpSampling2D";re.registerClass(cx);function RW(e,t,n=[1,1],a="valid",r,s){return D(()=>{r==null&&(r=Ca()),Dt(r);let i=sx(e,r);if(e.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Yr(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}var px=class extends ix{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=Vt(e.depthwiseConstraint),this.depthwiseRegularizer=kt(e.depthwiseRegularizer)}build(e){if(e=ht(e),e.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,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 D(()=>{e=Me(e);let n=RW(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=er(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],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=$a(t,this.kernelSize[0],this.padding,this.strides[0]),s=$a(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ct(this.depthwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseRegularizer),e}};px.className="DepthwiseConv2D";re.registerClass(px);function NI(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function TI(e,t,n,a=!1,r,s,i=!1,o=!1){return D(()=>{let l=t.shape.length;if(l<3)throw new z(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Ea(2,l));if(t=Ue(t,c),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=Fn(r,-1)),r=Ue(r,c)),a&&(t=Rn(t,0),r!=null&&(r=Rn(r,0)));let u=[],p,d=n,h=t.shape[0],m=dt(t),f;r!=null&&(f=dt(r));for(let y=0;y<h;++y){let b=m[y],v=D(()=>e(b,d));if(r==null)p=v[0],d=v[1];else{let x=D(()=>{let N=f[y],T=Dn(N).sub(N),C=v[0].mul(N).add(d[0].mul(T)),$=d.map((F,O)=>v[1][O].mul(N).add(F.mul(T)));return{output:C,newStates:$}});p=x.output,d=x.newStates}o&&u.push(p)}let g;return o&&(g=$t(u,1)),[p,g,d]})}var tr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new bm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new z("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 Xt({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 Ea(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Mb(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return D(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}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.");Mb(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new Xt({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new $e("Constants support is not implemented in RNN yet.");this.cell.build(r);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!k.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Xt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new xr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("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(a=>vt([n,a])):this.states_=[vt([n,this.cell.stateSize])];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>vt([n,a])):this.states_[0]=vt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`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()):Fe(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!k.arraysEqual(r.shape,i))throw new z(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>jt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=NI(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.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 Xt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Aa){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 D(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Me(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new z(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=TI((d,h)=>{let m=this.cell.call([d].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,a);let p=this.returnSequences?c:l;return this.returnState?[p].concat(u):p})}getInitialState(e){return D(()=>{let t=vt(e.shape);return t=Te(t,[1,2]),t=Qc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?wb(t,[1,n]):t):this.cell.stateSize>1?[wb(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()===tr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Fa(a,n);return new e(Object.assign(t,{cell:r}))}};tr.className="RNN";re.registerClass(tr);var np=class extends qe{},xm=class extends np{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=is(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=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=ql([1,as([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ql([1,as([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 D(()=>{if(e=e,e.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=os({ones:()=>Dn(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=os({ones:()=>Dn(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=Qa(L(e,s),this.kernel.read()):r=Qa(e,this.kernel.read()),this.bias!=null&&(r=er(r,this.bias.read())),i!=null&&(n=L(n,i));let o=Z(r,Qa(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:ss(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};xm.className="SimpleRNNCell";re.registerClass(xm);var dx=class extends tr{constructor(e){e.cell=new xm(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};dx.className="SimpleRNN";re.registerClass(dx);var vm=class extends np{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 z("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Kt(this.units,"units"),this.activation=is(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=is(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=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=ql([1,as([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ql([1,as([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 D(()=>{if(e=e,e.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=os({ones:()=>Dn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=os({ones:()=>Dn(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,r[0]));let c=Qa(e,this.kernel.read());this.useBias&&(c=er(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,s[0]));let u=this.recurrentKernel.read(),[p,d]=Mn(u,[2*this.units,this.units],u.rank-1),h=Qa(a,p),[m,f,g]=Mn(c,3,c.rank-1),[y,b]=Mn(h,2,h.rank-1);i=this.recurrentActivation.apply(Z(m,y)),o=this.recurrentActivation.apply(Z(f,b));let v=Qa(L(o,a),d);l=this.activation.apply(Z(g,v));let x=Z(L(i,a),L(Z(1,Tt(i)),l));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ss(this.activation),recurrentActivation:ss(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};vm.className="GRUCell";re.registerClass(vm);var hx=class extends tr{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 vm(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};hx.className="GRU";re.registerClass(hx);var cp=class extends np{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=is(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=is(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=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=ql([1,as([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ql([1,as([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 a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends ha{apply(i,o){let l=r.apply([s]),c=new Kh().apply([s]),u=r.apply([s*2]);return yk(yk(l,c),u)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=os({ones:()=>Dn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=os({ones:()=>Dn(a),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=L(e,s[0]));let p=Qa(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,i[0])),p=Z(p,Qa(a,this.recurrentKernel.read())),this.useBias&&(p=er(p,this.bias.read()));let[d,h,m,f]=Mn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(h),c=Z(L(l,r),L(o,this.activation.apply(m))),u=this.recurrentActivation.apply(f);let g=L(u,this.activation.apply(c));return[g,g,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ss(this.activation),recurrentActivation:ss(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};cp.className="LSTMCell";re.registerClass(cp);var mx=class extends tr{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 cp(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};mx.className="LSTM";re.registerClass(mx);var bm=class extends np{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 D(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){Mb(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Mi(`RNNCell_${a}`,()=>{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=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Fa(r,n));return new e({cells:a})}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 Pb(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}Ob(t)}};bm.className="StackedRNNCells";re.registerClass(bm);function os(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>xk(t(),n),i=()=>tp(s,t,a);return!r||r<=1?jt(i().clone()):Array(r).fill(void 0).map(i).map(o=>jt(o.clone()))}var MW=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},SI=class extends tr{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 Xt({ndim:5})]}call(e,t){return D(()=>{if(this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new z("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}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 D(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=vt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new xr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new z("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(()=>vt(r)):this.states_=[vt(r)];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>vt(r)):this.states_[0]=vt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`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()):Fe(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!k.arraysEqual(i.shape,o))throw new z(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>jt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=$a(l,a[0],r,s[0],i[0]),p=$a(c,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,p]:[u,p,n]]}};SI.className="ConvRNN2D";var wm=class extends cp{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Kt(this.filters,"filters"),this.kernelSize=Jl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Kt(o,"kernelSize")),this.strides=Jl(a||1,2,"strides"),this.strides.forEach(o=>Kt(o,"strides")),this.padding=r||"valid",Jn(this.padding),this.dataFormat=s||"channelsLast",Dt(this.dataFormat),this.dilationRate=Jl(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 z(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);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 ha{apply(u,p){let d=l.apply([c]),h=Xa([c]),m=l.apply([c*2]);return Ib([d,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return D(()=>{if(e.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=os({ones:()=>Dn(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(J,se,Q)=>!se||!se[Q]?J:L(se[Q],J),c=l(a,o,0),u=l(a,o,1),p=l(a,o,2),d=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=os({ones:()=>Dn(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[v,x,N,T]=Mn(this.kernel.read(),i,b),[C,$,F,O]=this.useBias?Mn(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,v,C,this.padding),u=this.inputConv(u,x,$,this.padding),p=this.inputConv(p,N,F,this.padding),d=this.inputConv(d,T,O,this.padding);let[B,W,G,q]=Mn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,B),f=this.recurrentConv(f,W),g=this.recurrentConv(g,G),y=this.recurrentConv(y,q);let H=this.recurrentActivation.apply(Z(c,m)),K=this.recurrentActivation.apply(Z(u,f)),X=Z(L(K,s),L(H,this.activation.apply(Z(p,g)))),ne=L(this.recurrentActivation.apply(Z(d,y)),this.activation.apply(X));return[ne,ne,X]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=MW(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=At(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?er(r,n,this.dataFormat):r}recurrentConv(e,t){return At(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};wm.className="ConvLSTM2DCell";re.registerClass(wm);var fx=class extends SI{constructor(e){let t=new wm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};fx.className="ConvLSTM2D";re.registerClass(fx);var km=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 a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return tp(()=>xk(n,this.rate,r,this.seed),()=>n,a)}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()}};km.className="Dropout";re.registerClass(km);var gx=class extends km{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};gx.className="SpatialDropout1D";re.registerClass(gx);var yx=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,Kt(this.units,"units"),this.activation=is(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=Vt(e.kernelConstraint),this.biasConstraint=Vt(e.biasConstraint),this.kernelRegularizer=kt(e.kernelRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.activityRegularizer=kt(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 D(()=>{this.invokeCallHook(e,t);let n=Me(e),a=ok(this.activation.getClassName()),r;return a!=null?r=Qa(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=Qa(n,this.kernel.read()),this.bias!=null&&(r=er(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ss(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};yx.className="Dense";re.registerClass(yx);var bx=class extends qe{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 z(`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],ns(e,1)]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=n.transpose(a)}return iz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};bx.className="Flatten";re.registerClass(bx);var xx=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=is(e.activation)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);return this.activation.apply(n)})}getConfig(){let e={activation:ss(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};xx.className="Activation";re.registerClass(xx);var vx=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 D(()=>(e=Me(e),rz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};vx.className="RepeatVector";re.registerClass(vx);var wx=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.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new z("Can only specifiy one unknown dimension.");else r*=l}let i=ns(e);if(s!==null){if(r===0||i%r!=0)throw new z(n);a[s]=i/r}else if(i!==r)throw new z(n);return a}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 D(()=>{this.invokeCallHook(e,t);let n=Me(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return n.reshape(r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};wx.className="Reshape";re.registerClass(wx);var kx=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=Ea(1,e.dims.length+1);if(!k.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 Xt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ht(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Ue(Me(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};kx.className="Permute";re.registerClass(kx);var Ix=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=Me(e),a=-1;return Rc(_i(n,this.maskValue),a)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e),a=-1,r=!0,s=Rc(_i(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};Ix.className="Masking";re.registerClass(Ix);var Nx=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(yt(e.inputLength))}this.inputDim=e.inputDim,Kt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Kt(this.outputDim,"outputDim"),this.embeddingsInitializer=wt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=kt(e.embeddingsRegularizer),this.activityRegularizer=kt(e.activityRegularizer),this.embeddingsConstraint=Vt(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 D(()=>this.maskZero?(e=Me(e),_i(e,Ge(e))):null)}computeOutputShape(e){if(e=ht(e),this.inputLength==null)return[...e,this.outputDim];let t=yt(this.inputLength);if(t.length!==e.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a<t.length;++a){let r=t[a],s=e[a+1];if(r!=null&&s!=null&&r!==s)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);return n.dtype!=="int32"&&(n=Jc(n,"int32")),bk(this.embeddings.read(),n.as1D()).reshape(ht(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ct(this.embeddingsInitializer),embeddingsRegularizer:mt(this.embeddingsRegularizer),activityRegularizer:mt(this.activityRegularizer),embeddingsConstraint:Wt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Nx.className="Embedding";re.registerClass(Nx);var Bi=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 a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ht(e)]),e=e,e.length<2)throw new z(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=ts(t),t.length>1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&ts(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return D(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=as(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=Qc(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],p=c.slice(1).concat([u]),d=o.reshape([u].concat(ns(c.slice(1))));d=Ue(d,[1,0]),d=d.reshape(p),n.push(d),r=!0}else if(l>1){let c=Ea(1,l).concat([0]);n.push(Ue(o,c)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=Ue(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(Ea(0,i-1));s=Ue(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=ts(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return D(()=>{if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an Array");if(!Array.isArray(e))throw new z("`inputs` should be an Array");if(t.length!==e.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:Fn(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=pa(n,t[a]);return n})}},Tx=class extends Bi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Z(t,e[n]);return t})}};Tx.className="Add";re.registerClass(Tx);var Sx=class extends Bi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};Sx.className="Multiply";re.registerClass(Sx);var Cx=class extends Bi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Z(t,e[n]);return L(1/e.length,t)})}};Cx.className="Average";re.registerClass(Cx);var _x=class extends Bi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ka(t,e[n]);return t})}};_x.className="Maximum";re.registerClass(_x);var Ex=class extends Bi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Bl(t,e[n]);return t})}};Ex.className="Minimum";re.registerClass(Ex);var Ax=class extends Bi{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 z("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let a of e)if(a!=null){t=!1;break}if(t)return;let n=[];for(let a=0;a<e.length;++a){let r=e[a].slice();r.splice(this.axis,1);let s=!1;for(let i of n)if(k.arraysEqual(i,r)){s=!0;break}s||n.push(r)}if(n.length>1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return D(()=>Ib(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new z("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new z("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new z(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return D(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s<e.length;++s)t[s]==null?a.push(Dn(e[s]).asType("bool")):t[s].rank<e[s].rank?a.push(Fn(t[s],-1)):a.push(t[s]);let r=Qe(a,this.axis);return lh(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Ax.className="Concatenate";re.registerClass(Ax);function pp(e,t){for(;e<0;)e+=t;return e}function PW(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(k.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k.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 a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return D(()=>{let i;if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(r>a){i=r-a;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;a>r?l=a+r-3:l=a-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 Fx=class extends Bi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k.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 a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new z(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>pp(r,e[s].shape.length)):a=[pp(this.axes,t.shape.length),pp(this.axes,n.shape.length)],this.normalize&&(t=im(t,a[0]),n=im(n,a[1])),PW(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[pp(this.axes,e.length),pp(this.axes,t.length)],n}computeOutputShape(e){k.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 a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Fx.className="Dot";re.registerClass(Fx);var $x=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);return tp(()=>qh(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};$x.className="GaussianNoise";re.registerClass($x);var Dx=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 D(()=>{this.invokeCallHook(e,t);let n=Me(e);return this.rate>0&&this.rate<1?tp(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(qh(n.shape,1,a))},()=>n,t.training||!1):n})}};Dx.className="GaussianDropout";re.registerClass(Dx);var Rx=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Me(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 D(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return tp(()=>{let a=Me(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=Jr(Wl(n),this.rate);o=Jc(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>Me(e),t.training||!1)}return e})}};Rx.className="AlphaDropout";re.registerClass(Rx);function dp(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=t1(e,t,n,a,r,s);else if(e.rank===3)i=n1(e,t,n,a,r,s);else if(e.rank===4)i=a1(e,t,n,a,r,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function OW(e,t,n,a,r=.001){return D(()=>{let s=vh(e,a),i=s.mean,o=s.variance;return[dp(e,i,o,n,t,r),i,o]})}function LW(e,t,n,a,r=.001){return D(()=>{let s=vh(e,a),i=s.mean,o=s.variance,l=[];for(let h of Ea(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let c=i.reshape(l),u=o.reshape(l),p=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[dp(e,c,u,d,p,r),i,o]})}function zW(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),Ea(0,e.rank-1))?OW(e,t,n,a,r):LW(e,t,n,a,r)}var Mx=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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.movingMeanInitializer=wt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=wt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Vt(e.betaConstraint),this.gammaConstraint=Vt(e.gammaConstraint),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer)}build(e){e=ht(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new z(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Xt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training,a=Me(e),r=a.shape,s=r.length,i=Ea(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=$i(1,s);l[o]=r[o];let c=i.slice();c.sort();let u=!k.arraysEqual(c,Ea(0,s).slice(0,s-1)),p=()=>{if(u){let g=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),b=this.center?this.beta.read().reshape(l):null,v=this.scale?this.gamma.read().reshape(l):null;return dp(a,g,y,b,v,this.epsilon)}else return dp(a,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 p();let[d,h,m]=zW(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,b)=>{D(()=>{let v=1-b,x=g.read(),N=x.sub(y).mul(v);g.write(x.sub(N))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),movingMeanInitializer:Ct(this.movingMeanInitializer),movingVarianceInitializer:Ct(this.movingVarianceInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer),betaConstraint:Wt(this.betaConstraint),gammaConstraint:Wt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Mx.className="BatchNormalization";re.registerClass(Mx);var Px=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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ht(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==ts(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Me(e),a=n.shape,r=a.length;return D(()=>{let s=!0,{mean:i,variance:o}=vh(n,this.axis,s),l=$i(1,r);for(let m of this.axis)l[m]=a[m];let c=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(l):m,u=c(this.gamma.read()),p=c(this.beta.read()),d=[],h=[];for(let m=0;m<r;++m)this.axis.indexOf(m)!==-1?(d.push(a[m]),h.push(1)):(d.push(1),h.push(a[m]));return i=i.tile(d),o=o.tile(d),u=u.tile(h),p=p.tile(h),dp(n,i,o,p,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Px.className="LayerNormalization";re.registerClass(Px);function BW(e,t,n){return D(()=>{if(e.rank!==4)throw new z(`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 z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Ca()),n!=="channelsLast"&&n!=="channelsFirst")throw new z(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],Zn(e,a)})}var Ox=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Ca():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new z(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new z(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new z(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){e=ht(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return D(()=>BW(Me(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ox.className="ZeroPadding2D";re.registerClass(Ox);function Im(e,t,n,a,r,s){return D(()=>{Dt(r),pk(s),Jn(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Ca()),s==null&&(s="max"),e=sx(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Ft(e,t,n,o):i=Xn(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}function CI(e,t,n,a,r,s){return D(()=>{Dt(r),pk(s),Jn(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Ca()),s==null&&(s="max"),e=wI(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=qy(e,t,n,o):i=Dy(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,4,1,2,3])),i})}var _I=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 z(`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 z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Jn(this.padding),this.inputSpec=[new Xt({ndim:3})]}computeOutputShape(e){e=ht(e);let t=$a(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return D(()=>{this.invokeCallHook(e,t),e=Qc(Me(e),2);let n=this.poolingFunction(Me(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Qr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Lx=class extends _I{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Jn(a),Im(e,t,n,a,r,"max")}};Lx.className="MaxPooling1D";re.registerClass(Lx);var zx=class extends _I{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Jn(a),Im(e,t,n,a,r,"avg")}};zx.className="AveragePooling1D";re.registerClass(zx);var EI=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 z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Dt(this.dataFormat),Jn(this.padding),this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=$a(t,this.poolSize[0],this.padding,this.strides[0]),n=$a(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 D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(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}},Bx=class extends EI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Jn(a),Im(e,t,n,a,r,"max")}};Bx.className="MaxPooling2D";re.registerClass(Bx);var Wx=class extends EI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Jn(a),Im(e,t,n,a,r,"avg")}};Wx.className="AveragePooling2D";re.registerClass(Wx);var AI=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 z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Dt(this.dataFormat),Jn(this.padding),this.inputSpec=[new Xt({ndim:5})]}computeOutputShape(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=$a(t,this.poolSize[0],this.padding,this.strides[0]),n=$a(n,this.poolSize[1],this.padding,this.strides[1]),a=$a(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(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}},Vx=class extends AI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Jn(a),CI(e,t,n,a,r,"max")}};Vx.className="MaxPooling3D";re.registerClass(Vx);var Ux=class extends AI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Jn(a),CI(e,t,n,a,r,"avg")}};Ux.className="AveragePooling3D";re.registerClass(Ux);var FI=class extends qe{constructor(e){super(e);this.inputSpec=[new Xt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},Gx=class extends FI{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Me(e);return St(n,1)})}};Gx.className="GlobalAveragePooling1D";re.registerClass(Gx);var Hx=class extends FI{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Me(e);return Yn(n,1)})}};Hx.className="GlobalMaxPooling1D";re.registerClass(Hx);var $I=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Dt(this.dataFormat),this.inputSpec=[new Xt({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}},jx=class extends $I{call(e,t){return D(()=>{let n=Me(e);return this.dataFormat==="channelsLast"?St(n,[1,2]):St(n,[2,3])})}};jx.className="GlobalAveragePooling2D";re.registerClass(jx);var qx=class extends $I{call(e,t){return D(()=>{let n=Me(e);return this.dataFormat==="channelsLast"?Yn(n,[1,2]):Yn(n,[2,3])})}};qx.className="GlobalMaxPooling2D";re.registerClass(qx);var DI=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 a=t.layer,r=Fa(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},Kx=class extends DI{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ht(e),e.length<3)throw new z(`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=ht(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return D(()=>(e=Me(e),TI((n,a)=>[Me(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Kx.className="TimeDistributed";re.registerClass(Kx);function WW(e){Ri(Q3,"BidirectionalMergeMode",e)}var VW="concat",Xx=class extends DI{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Fa(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Fa(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?VW:e.mergeMode,WW(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,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):kn(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=NI(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new z("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 Xt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),i.push(...c)}if(a!=null)throw new $e("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Aa;for(let l of s)if(l instanceof Aa!==o)throw new z("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),c=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=c;let p=super.apply(l,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return D(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=Rn(r,1));let 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extends qe{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=sm(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],ts(this.inputs).length!==this.inputs.length)throw new z(`The list of inputs passed to the model is redundant. 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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 b=y.sourceLayer,v=y.nodeIndex,x=y.tensorIndex;this.outputLayers.push(b),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let y of this.inputs){let b=y.sourceLayer,v=y.nodeIndex,x=y.tensorIndex;Qa(v===0,"input layer has >1 nodes"),Qa(x===0,"input layer has >1 tensors"),this.inputLayers.push(b),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let b=this.inputLayers[y];if(!(b instanceof Kl))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${b.getClassName()}.`);this.inputNames.push(b.name),this.feedInputShapes.push(b.batchInputShape),this.feedInputNames.push(b.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={},a={},r={},s={},i=[],o=(y,b,v,x,N,T)=>{(x==null||N==null||T==null)&&(x=y.sourceLayer,N=y.nodeIndex,T=y.tensorIndex);let C=x.inboundNodes[N];if(v.indexOf(C)!==-1)throw new Ea(`The tensor ${y.name} at layer "${x.name}" is part of a cycle.`);if(b.indexOf(C)!==-1)return;this.containerNodes.add(ar.nodeKey(x,N)),x.id in s||(s[x.id]=Object.keys(s).length),v.indexOf(C)===-1&&v.push(C);let F=C.inboundLayers.length;for(let $=0;$<F;$++){let P=C.inputTensors[$],B=C.inboundLayers[$],W=C.nodeIndices[$],G=C.tensorIndices[$];o(P,b,v,B,W,G)}for(b.push(C);v.indexOf(C)>=0;)v.splice(v.indexOf(C),1);i.push(C)},l=[],c=[];for(let y of this.outputs)o(y,l,c);let u=i.slice().reverse();for(let y of u){n[y.id]=y,y.id in t||(t[y.id]=0);let b=t[y.id],v=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];b=Math.max(b,v),a[y.outboundLayer.id]=b,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=b;for(let x=0;x<y.inboundLayers.length;x++){let N=y.inboundLayers[x],T=y.nodeIndices[x],C=N.inboundNodes[T],F=t[C.id]==null?0:t[C.id];t[C.id]=Math.max(b+1,F),n[C.id]=C}}let p={};for(let y in t){let b=t[y];b in p||(p[b]=[]),p[b].push(n[y])}let d={};for(let y in a){let b=a[y];b in d||(d[b]=[]),d[b].push(r[y])}let h=Object.keys(d).map(y=>parseInt(y,10)).sort(Kh);this.layers=[];for(let y of h){let b=d[y];b.sort((v,x)=>{let N=s[v.id],T=s[x.id];return N<T?-1:N>T?1:0});for(let v of b)v instanceof ar&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(y=>parseInt(y,10)).sort(Kh);let m=this.inputs.slice(),f=[];for(let y of h)for(let b of p[y]){let v=b.outboundLayer;if(v!=null){for(let x of b.inputTensors)if(m.indexOf(x)===-1)throw new Ea(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${f}`);for(let x of b.outputTensors)m.push(x);f.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(y=>y.name);for(let y of g){let b=g.filter(v=>v===y).length;if(b!==1)throw new Ea(`The name "${y}" is used ${b} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new lm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new z("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new z(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,a++}let r=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[s]]);else if(t)throw new z(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new z(`${s.length} of ${a} weights are not set: ${s}`)}Bb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${ym}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=qb(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return D(()=>{e=yt(e);let n=new Bi;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return sp(this.outputs,n,t)})}computeMask(e,t){return D(()=>{e=yt(e);let n;return t==null?n=Ri(null,e.length):n=yt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=im(e);if(t.length!==this.inputLayers.length)throw new z(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Kh);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(c.id)!==-1)continue;let u=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],b=`${f.name}_${g}_${y}`,v=n[b];u.push(v)}let p=c.computeOutputShape(kn(u)),d=im(p),h=c.inboundNodes.indexOf(l);for(let m=0;m<d.length;m++){let f=`${c.name}_${h}_${m}`;n[f]=d[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${c}`;s.push(u)}for(let i=0;i<s.length;i++){let o=s[i];Qa(o in n),r.push(n[o])}return kn(r)}runInternalGraph(e,t){t==null&&(t=Ri(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],c=e[o],u=t[o];n[l.id]=[c,u]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Kh);for(let o of a){let l=this.nodesByDepth[o];for(let c of l){let u=c.outboundLayer,p=c.inputTensors,d=c.outputTensors,h=new Array;for(let m of p)m.id in n&&h.push(n[m.id]);if(h.length===p.length){let m={},f,g,y,b;if(c.callArgs!=null&&(m=c.callArgs),h.length===1){let[v,x]=h[0];m.mask==null&&(m.mask=x),y=yt(u.call(v,m)),b=yt(u.computeMask(v,x)),f=[v],g=[x]}else f=h.map(v=>v[0]),g=h.map(v=>v[1]),m.mask==null&&(m.mask=g),y=yt(u.call(f,m)),b=yt(u.computeMask(f,g));if(u.activityRegularizer)throw new $e("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],N=y[v],T=b[v];n[x.id]=[N,T]}}}}let r=[],s=[],i=[];for(let o of this.outputs){Qa(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,c]=n[o.id];i.push(l.shape),r.push(l),s.push(c)}return[r,s,i]}buildNodeConversionMap(e){let t={},n;for(let a of this.layers){n=a instanceof ar?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=ar.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new z(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new z("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new z(`No such layer: ${e}`)}calculateLosses(){return D(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let a=ar.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let u=0;u<s.inboundNodes.length;u++){let p=s.inboundNodes[u],d=ar.nodeKey(s,u),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let m=[];for(let f=0;f<p.inboundLayers.length;f++){let g=p.inboundLayers[f],y=p.nodeIndices[f],b=p.tensorIndices[f],v=ar.nodeKey(g,y),x=t[v];x==null&&(x=0),m.push([g.name,x,b,h])}l.push(m)}}}let c={};c.name=s.name,c.className=i,c.config=o,c.inboundNodes=l,n.push(c)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=ar.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[s];a.push([i.name,c,u])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=ar.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[s];r.push([i.name,c,u])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(f,g){f.name in s?s[f.name].push(g):s[f.name]=[g]}function o(f,g){let y=[],b;for(let v of g){let x=v[0],N=v[1],T=v[2];if(b=v[3]==null?{}:v[3],!(x in r)){i(f,g);return}let C=r[x];if(C.inboundNodes.length<=N){i(f,g);return}let F=C.inboundNodes[N];y.push(F.outputTensors[T])}y.length>0&&f.apply(kn(y),b)}function l(f){let g=f.name,y=$a(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),r[g]=y,f.inboundNodes.forEach(b=>{if(!(b instanceof Array))throw new z(`Corrupted configuration, expected array for nodeData: ${b}`);i(y,b)})}let c=t.name,u=t.layers;for(let f of u)l(f);for(;!L3(s);)for(let f of u){let g=r[f.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let b of y)o(g,b)}}let p=[],d=[],h=t.inputLayers;for(let f of h){let g=f[0],y=f[1],b=f[2];Qa(g in r);let v=r[g].inboundNodes[y].outputTensors;p.push(v[b])}let m=t.outputLayers;for(let f of m){let g=f[0],y=f[1],b=f[2];Qa(g in r);let v=r[g].inboundNodes[y].outputTensors;d.push(v[b])}return new e({inputs:p,outputs:d,name:c})}get stateful(){if(this._stateful)throw new z("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){D(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function hW(e,t,n){let a=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(a===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==a)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${a} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(s=>{s in e?r.push(e[s]):r.push(null)}),r}else throw new Error(`The model has multiple (${a}) outputs, so ${n} must be either an array with ${a} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function tI(e,t){return hW(e,t,"classWeight")}async function nI(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=D(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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 r.data());Fe(r);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])}),tt(i,"float32")}else return null}function mW(e,t){return L(e,t)}var fW=32;function rI(e,t){let n,a,r=t;n=r.xs,a=r.ys,k.assert(n!=null&&a!=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=aI("input",e.inputNames,n),i=aI("output",e.outputNames,a),o=s[0].shape[0];k.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)})`),k.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)k.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++)k.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 aI(e,t,n){if(n instanceof Ae)return[n];if(Array.isArray(n))return k.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let a=[];for(let r of t){if(n[r]==null)throw new z(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function gW(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 bW(e,t,n){let a=n.batchesPerEpoch!=null;if(k.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k.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}`),k.assert(!a||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,s,i;if(r)if(sI(n.validationData))k.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=gW(n.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;r?c=l.slice().concat(l.map(g=>"val_"+g)):c=l.slice();let u=Uk(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=Gk(u,p,n.epochs,null,null,yW(t,n),null,r,c);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let m=n.initialEpoch==null?0:n.initialEpoch,f=await t.iterator();for(;m<n.epochs;){let g={};await d.onEpochBegin(m);let y=0,b=0;for(a||(f=await t.iterator());a?y<n.batchesPerEpoch:!0;){let v=await f.next();if(a&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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instead`);if(s==="channelsFirst"&&(e=Ue(e,[0,2,1])),r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=hh(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=tr(o,n)),o})}function CI(e,t,n,a=[1,1],r="valid",s,i,o=null){return D(()=>{if(s==null&&(s=_a()),Dt(s),e.rank!==3&&e.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=lx(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=es.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Ue(l,[0,3,1,2])),l})}function zW(e,t,n,a=[1,1,1],r="valid",s,i){return D(()=>{if(s==null&&(s=_a()),Dt(s),e.rank!==4&&e.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=SI(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=zy(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=tr(o,n)),s==="channelsFirst"&&(o=Ue(o,[0,4,1,2,3])),o})}var ux=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",ux.verifyArgs(t),this.rank=e,Kt(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=Jl(t.kernelSize,e,"kernelSize"),this.strides=Jl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Jn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Dt(this.dataFormat),this.activation=is(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=wt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Vt(t.biasConstraint),this.biasRegularizer=kt(t.biasRegularizer),this.activityRegularizer=kt(t.activityRegularizer),this.dilationRate=Jl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`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 z(`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 z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Qa("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!bb(e.kernelSize,"number",1,3))throw new z(`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:ss(this.activation),useBias:this.useBias,biasInitializer:Ct(this.biasInitializer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},lp=class extends ux{constructor(e,t){super(e,t);this.kernel=null,lp.verifyArgs(t),this.filters=t.filters,Kt(this.filters,"filters"),this.kernelInitializer=wt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Vt(t.kernelConstraint),this.kernelRegularizer=kt(t.kernelRegularizer)}build(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,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 D(()=>{e=Me(e);let n,a=this.bias==null?null:this.bias.read(),r=dk(this.activation.getClassName());if(r!=null&&this.rank===2)n=CI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=LW(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=CI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=zW(e,this.kernel.read(),a,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=ht(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let s=Da(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:Ct(this.kernelInitializer),kernelRegularizer:mt(this.kernelRegularizer),kernelConstraint:Wt(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 z(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},up=class extends lp{constructor(e){super(2,e);up.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!bb(e.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};up.className="Conv2D";re.registerClass(up);var xm=class extends lp{constructor(e){super(3,e);xm.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 z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};xm.className="Conv3D";re.registerClass(xm);var cx=class extends up{constructor(e){super(e);if(this.inputSpec=[new Xt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ht(e),e.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Xt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{let n=Me(e);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],c=this.kernelSize[0],u=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=bm(o,p,c,this.padding),m=bm(l,d,u,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ue(n,[0,2,3,1]));let g=mh(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ue(g,[0,3,1,2])),this.bias!=null&&(g=tr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ht(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=bm(t[a],o,s,this.padding),t[r]=bm(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};cx.className="Conv2DTranspose";re.registerClass(cx);var _I=class extends lp{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 z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new z("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 z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=kt(t.depthwiseRegularizer),this.depthwiseConstraint=Vt(t.depthwiseConstraint),this.pointwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=kt(t.pointwiseRegularizer),this.pointwiseConstraint=Vt(t.pointwiseConstraint)}build(e){if(e=ht(e),e.length<this.rank+2)throw new z(`Inputs to SeparableConv${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 z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"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 Xt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{e=Me(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=Ue(e,[0,2,3,1])),n=Fi(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=tr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ue(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=Ct(this.depthwiseInitializer),e.pointwiseInitializer=Ct(this.pointwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.pointwiseRegularizer=mt(this.pointwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseConstraint),e.pointwiseConstraint=Wt(this.pointwiseConstraint),e}};_I.className="SeparableConv";var px=class extends _I{constructor(e){super(2,e)}};px.className="SeparableConv2D";re.registerClass(px);var vm=class extends lp{constructor(e){super(1,e);vm.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"&&!bb(e.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};vm.className="Conv1D";re.registerClass(vm);var dx=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 D(()=>{if(e=Me(e),this.dataFormat==="channelsLast"){let n=Xh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Xh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Xh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Xh(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}};dx.className="Cropping2D";re.registerClass(dx);var hx=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,Dt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,iz(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 D(()=>{let n=Me(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ue(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s]);return Ue(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};hx.className="UpSampling2D";re.registerClass(hx);function BW(e,t,n=[1,1],a="valid",r,s){return D(()=>{r==null&&(r=_a()),Dt(r);let i=lx(e,r);if(e.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Yr(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}var mx=class extends ux{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=Vt(e.depthwiseConstraint),this.depthwiseRegularizer=kt(e.depthwiseRegularizer)}build(e){if(e=ht(e),e.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,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 D(()=>{e=Me(e);let n=BW(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=tr(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],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Da(t,this.kernelSize[0],this.padding,this.strides[0]),s=Da(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ct(this.depthwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseRegularizer),e}};mx.className="DepthwiseConv2D";re.registerClass(mx);function EI(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function AI(e,t,n,a=!1,r,s,i=!1,o=!1){return D(()=>{let l=t.shape.length;if(l<3)throw new z(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Aa(2,l));if(t=Ue(t,c),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=$n(r,-1)),r=Ue(r,c)),a&&(t=Mn(t,0),r!=null&&(r=Mn(r,0)));let u=[],p,d=n,h=t.shape[0],m=dt(t),f;r!=null&&(f=dt(r));for(let y=0;y<h;++y){let b=m[y],v=D(()=>e(b,d));if(r==null)p=v[0],d=v[1];else{let x=D(()=>{let N=f[y],T=Rn(N).sub(N),C=v[0].mul(N).add(d[0].mul(T)),F=d.map(($,P)=>v[1][P].mul(N).add($.mul(T)));return{output:C,newStates:F}});p=x.output,d=x.newStates}o&&u.push(p)}let g;return o&&(g=$t(u,1)),[p,g,d]})}var nr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new wm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new z("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 Xt({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 Aa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Lb(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return D(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}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.");Lb(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new Xt({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new $e("Constants support is not implemented in RNN yet.");this.cell.build(r);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!k.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Xt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new vr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("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(a=>vt([n,a])):this.states_=[vt([n,this.cell.stateSize])];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>vt([n,a])):this.states_[0]=vt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`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()):Fe(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!k.arraysEqual(r.shape,i))throw new z(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>jt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=EI(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.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 Xt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Fa){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 D(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Me(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new z(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=AI((d,h)=>{let m=this.cell.call([d].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,a);let p=this.returnSequences?c:l;return this.returnState?[p].concat(u):p})}getInitialState(e){return D(()=>{let t=vt(e.shape);return t=Te(t,[1,2]),t=Qc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Nb(t,[1,n]):t):this.cell.stateSize>1?[Nb(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===nr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=$a(a,n);return new e(Object.assign(t,{cell:r}))}};nr.className="RNN";re.registerClass(nr);var np=class extends qe{},km=class extends np{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=is(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=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=ql([1,as([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ql([1,as([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 D(()=>{if(e=e,e.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=os({ones:()=>Rn(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=os({ones:()=>Rn(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=er(L(e,s),this.kernel.read()):r=er(e,this.kernel.read()),this.bias!=null&&(r=tr(r,this.bias.read())),i!=null&&(n=L(n,i));let o=Z(r,er(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:ss(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};km.className="SimpleRNNCell";re.registerClass(km);var fx=class extends nr{constructor(e){e.cell=new km(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};fx.className="SimpleRNN";re.registerClass(fx);var Im=class extends np{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 z("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Kt(this.units,"units"),this.activation=is(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=is(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=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=ql([1,as([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ql([1,as([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 D(()=>{if(e=e,e.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=os({ones:()=>Rn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=os({ones:()=>Rn(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,r[0]));let c=er(e,this.kernel.read());this.useBias&&(c=tr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,s[0]));let u=this.recurrentKernel.read(),[p,d]=Pn(u,[2*this.units,this.units],u.rank-1),h=er(a,p),[m,f,g]=Pn(c,3,c.rank-1),[y,b]=Pn(h,2,h.rank-1);i=this.recurrentActivation.apply(Z(m,y)),o=this.recurrentActivation.apply(Z(f,b));let v=er(L(o,a),d);l=this.activation.apply(Z(g,v));let x=Z(L(i,a),L(Z(1,Tt(i)),l));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ss(this.activation),recurrentActivation:ss(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Im.className="GRUCell";re.registerClass(Im);var gx=class extends nr{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 Im(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};gx.className="GRU";re.registerClass(gx);var cp=class extends np{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=is(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=is(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=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=ql([1,as([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ql([1,as([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 a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends ma{apply(i,o){let l=r.apply([s]),c=new Zh().apply([s]),u=r.apply([s*2]);return kk(kk(l,c),u)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=os({ones:()=>Rn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=os({ones:()=>Rn(a),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=L(e,s[0]));let p=er(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,i[0])),p=Z(p,er(a,this.recurrentKernel.read())),this.useBias&&(p=tr(p,this.bias.read()));let[d,h,m,f]=Pn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(h),c=Z(L(l,r),L(o,this.activation.apply(m))),u=this.recurrentActivation.apply(f);let g=L(u,this.activation.apply(c));return[g,g,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ss(this.activation),recurrentActivation:ss(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};cp.className="LSTMCell";re.registerClass(cp);var yx=class extends nr{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 cp(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};yx.className="LSTM";re.registerClass(yx);var wm=class extends np{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 D(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){Lb(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Oi(`RNNCell_${a}`,()=>{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=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push($a(r,n));return new e({cells:a})}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 zb(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}Bb(t)}};wm.className="StackedRNNCells";re.registerClass(wm);function os(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>Nk(t(),n),i=()=>tp(s,t,a);return!r||r<=1?jt(i().clone()):Array(r).fill(void 0).map(i).map(o=>jt(o.clone()))}var WW=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},FI=class extends nr{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 Xt({ndim:5})]}call(e,t){return D(()=>{if(this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new z("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}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 D(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=vt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new vr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new z("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(()=>vt(r)):this.states_=[vt(r)];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>vt(r)):this.states_[0]=vt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`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()):Fe(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!k.arraysEqual(i.shape,o))throw new z(`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=>jt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=Da(l,a[0],r,s[0],i[0]),p=Da(c,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,p]:[u,p,n]]}};FI.className="ConvRNN2D";var Nm=class extends cp{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Kt(this.filters,"filters"),this.kernelSize=Jl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Kt(o,"kernelSize")),this.strides=Jl(a||1,2,"strides"),this.strides.forEach(o=>Kt(o,"strides")),this.padding=r||"valid",Jn(this.padding),this.dataFormat=s||"channelsLast",Dt(this.dataFormat),this.dilationRate=Jl(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 z(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);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 ma{apply(u,p){let d=l.apply([c]),h=Ya([c]),m=l.apply([c*2]);return Sb([d,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return D(()=>{if(e.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=os({ones:()=>Rn(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(J,se,Q)=>!se||!se[Q]?J:L(se[Q],J),c=l(a,o,0),u=l(a,o,1),p=l(a,o,2),d=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=os({ones:()=>Rn(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[v,x,N,T]=Pn(this.kernel.read(),i,b),[C,F,$,P]=this.useBias?Pn(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,v,C,this.padding),u=this.inputConv(u,x,F,this.padding),p=this.inputConv(p,N,$,this.padding),d=this.inputConv(d,T,P,this.padding);let[B,W,G,q]=Pn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,B),f=this.recurrentConv(f,W),g=this.recurrentConv(g,G),y=this.recurrentConv(y,q);let j=this.recurrentActivation.apply(Z(c,m)),X=this.recurrentActivation.apply(Z(u,f)),K=Z(L(X,s),L(j,this.activation.apply(Z(p,g)))),ne=L(this.recurrentActivation.apply(Z(d,y)),this.activation.apply(K));return[ne,ne,K]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=WW(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=At(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?tr(r,n,this.dataFormat):r}recurrentConv(e,t){return At(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Nm.className="ConvLSTM2DCell";re.registerClass(Nm);var bx=class extends FI{constructor(e){let t=new Nm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};bx.className="ConvLSTM2D";re.registerClass(bx);var Tm=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 a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return tp(()=>Nk(n,this.rate,r,this.seed),()=>n,a)}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()}};Tm.className="Dropout";re.registerClass(Tm);var xx=class extends Tm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};xx.className="SpatialDropout1D";re.registerClass(xx);var vx=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,Kt(this.units,"units"),this.activation=is(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=Vt(e.kernelConstraint),this.biasConstraint=Vt(e.biasConstraint),this.kernelRegularizer=kt(e.kernelRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.activityRegularizer=kt(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 D(()=>{this.invokeCallHook(e,t);let n=Me(e),a=dk(this.activation.getClassName()),r;return a!=null?r=er(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=er(n,this.kernel.read()),this.bias!=null&&(r=tr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ss(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};vx.className="Dense";re.registerClass(vx);var wx=class extends qe{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 z(`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],ns(e,1)]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=n.transpose(a)}return dz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};wx.className="Flatten";re.registerClass(wx);var kx=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=is(e.activation)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);return this.activation.apply(n)})}getConfig(){let e={activation:ss(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};kx.className="Activation";re.registerClass(kx);var Ix=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 D(()=>(e=Me(e),cz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Ix.className="RepeatVector";re.registerClass(Ix);var Nx=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.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new z("Can only specifiy one unknown dimension.");else r*=l}let i=ns(e);if(s!==null){if(r===0||i%r!=0)throw new z(n);a[s]=i/r}else if(i!==r)throw new z(n);return a}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 D(()=>{this.invokeCallHook(e,t);let n=Me(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return n.reshape(r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Nx.className="Reshape";re.registerClass(Nx);var Tx=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=Aa(1,e.dims.length+1);if(!k.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 Xt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ht(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Ue(Me(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Tx.className="Permute";re.registerClass(Tx);var Sx=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=Me(e),a=-1;return Rc(Ai(n,this.maskValue),a)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e),a=-1,r=!0,s=Rc(Ai(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};Sx.className="Masking";re.registerClass(Sx);var Cx=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(yt(e.inputLength))}this.inputDim=e.inputDim,Kt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Kt(this.outputDim,"outputDim"),this.embeddingsInitializer=wt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=kt(e.embeddingsRegularizer),this.activityRegularizer=kt(e.activityRegularizer),this.embeddingsConstraint=Vt(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 D(()=>this.maskZero?(e=Me(e),Ai(e,Ge(e))):null)}computeOutputShape(e){if(e=ht(e),this.inputLength==null)return[...e,this.outputDim];let t=yt(this.inputLength);if(t.length!==e.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a<t.length;++a){let r=t[a],s=e[a+1];if(r!=null&&s!=null&&r!==s)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);return n.dtype!=="int32"&&(n=Jc(n,"int32")),Ik(this.embeddings.read(),n.as1D()).reshape(ht(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ct(this.embeddingsInitializer),embeddingsRegularizer:mt(this.embeddingsRegularizer),activityRegularizer:mt(this.activityRegularizer),embeddingsConstraint:Wt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Cx.className="Embedding";re.registerClass(Cx);var Vi=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 a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ht(e)]),e=e,e.length<2)throw new z(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=ts(t),t.length>1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&ts(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return D(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=as(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=Qc(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],p=c.slice(1).concat([u]),d=o.reshape([u].concat(ns(c.slice(1))));d=Ue(d,[1,0]),d=d.reshape(p),n.push(d),r=!0}else if(l>1){let c=Aa(1,l).concat([0]);n.push(Ue(o,c)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=Ue(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(Aa(0,i-1));s=Ue(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=ts(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return D(()=>{if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an Array");if(!Array.isArray(e))throw new z("`inputs` should be an Array");if(t.length!==e.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:$n(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=da(n,t[a]);return n})}},_x=class extends Vi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Z(t,e[n]);return t})}};_x.className="Add";re.registerClass(_x);var Ex=class extends Vi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};Ex.className="Multiply";re.registerClass(Ex);var Ax=class extends Vi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Z(t,e[n]);return L(1/e.length,t)})}};Ax.className="Average";re.registerClass(Ax);var Fx=class extends Vi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Xa(t,e[n]);return t})}};Fx.className="Maximum";re.registerClass(Fx);var $x=class extends Vi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Bl(t,e[n]);return t})}};$x.className="Minimum";re.registerClass($x);var Dx=class extends Vi{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new z("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let a of e)if(a!=null){t=!1;break}if(t)return;let n=[];for(let a=0;a<e.length;++a){let r=e[a].slice();r.splice(this.axis,1);let s=!1;for(let i of n)if(k.arraysEqual(i,r)){s=!0;break}s||n.push(r)}if(n.length>1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return D(()=>Sb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new z("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new z("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new z(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return D(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s<e.length;++s)t[s]==null?a.push(Rn(e[s]).asType("bool")):t[s].rank<e[s].rank?a.push($n(t[s],-1)):a.push(t[s]);let r=Qe(a,this.axis);return ph(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Dx.className="Concatenate";re.registerClass(Dx);function pp(e,t){for(;e<0;)e+=t;return e}function VW(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(k.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k.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 a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return D(()=>{let i;if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(r>a){i=r-a;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;a>r?l=a+r-3:l=a-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 Rx=class extends Vi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k.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 a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new z(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>pp(r,e[s].shape.length)):a=[pp(this.axes,t.shape.length),pp(this.axes,n.shape.length)],this.normalize&&(t=um(t,a[0]),n=um(n,a[1])),VW(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[pp(this.axes,e.length),pp(this.axes,t.length)],n}computeOutputShape(e){k.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 a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Rx.className="Dot";re.registerClass(Rx);var Mx=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 D(()=>{this.invokeCallHook(e,t);let n=Me(e);return tp(()=>Yh(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Mx.className="GaussianNoise";re.registerClass(Mx);var Px=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 D(()=>{this.invokeCallHook(e,t);let n=Me(e);return this.rate>0&&this.rate<1?tp(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(Yh(n.shape,1,a))},()=>n,t.training||!1):n})}};Px.className="GaussianDropout";re.registerClass(Px);var Ox=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Me(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 D(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return tp(()=>{let a=Me(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=Jr(Wl(n),this.rate);o=Jc(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>Me(e),t.training||!1)}return e})}};Ox.className="AlphaDropout";re.registerClass(Ox);function dp(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=i1(e,t,n,a,r,s);else if(e.rank===3)i=o1(e,t,n,a,r,s);else if(e.rank===4)i=l1(e,t,n,a,r,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function UW(e,t,n,a,r=.001){return D(()=>{let s=Ih(e,a),i=s.mean,o=s.variance;return[dp(e,i,o,n,t,r),i,o]})}function GW(e,t,n,a,r=.001){return D(()=>{let s=Ih(e,a),i=s.mean,o=s.variance,l=[];for(let h of Aa(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let c=i.reshape(l),u=o.reshape(l),p=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[dp(e,c,u,d,p,r),i,o]})}function HW(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),Aa(0,e.rank-1))?UW(e,t,n,a,r):GW(e,t,n,a,r)}var Lx=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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.movingMeanInitializer=wt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=wt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Vt(e.betaConstraint),this.gammaConstraint=Vt(e.gammaConstraint),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer)}build(e){e=ht(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new z(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Xt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training,a=Me(e),r=a.shape,s=r.length,i=Aa(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Ri(1,s);l[o]=r[o];let c=i.slice();c.sort();let u=!k.arraysEqual(c,Aa(0,s).slice(0,s-1)),p=()=>{if(u){let g=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),b=this.center?this.beta.read().reshape(l):null,v=this.scale?this.gamma.read().reshape(l):null;return dp(a,g,y,b,v,this.epsilon)}else return dp(a,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 p();let[d,h,m]=HW(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,b)=>{D(()=>{let v=1-b,x=g.read(),N=x.sub(y).mul(v);g.write(x.sub(N))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),movingMeanInitializer:Ct(this.movingMeanInitializer),movingVarianceInitializer:Ct(this.movingVarianceInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer),betaConstraint:Wt(this.betaConstraint),gammaConstraint:Wt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Lx.className="BatchNormalization";re.registerClass(Lx);var zx=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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ht(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==ts(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Me(e),a=n.shape,r=a.length;return D(()=>{let s=!0,{mean:i,variance:o}=Ih(n,this.axis,s),l=Ri(1,r);for(let m of this.axis)l[m]=a[m];let c=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(l):m,u=c(this.gamma.read()),p=c(this.beta.read()),d=[],h=[];for(let m=0;m<r;++m)this.axis.indexOf(m)!==-1?(d.push(a[m]),h.push(1)):(d.push(1),h.push(a[m]));return i=i.tile(d),o=o.tile(d),u=u.tile(h),p=p.tile(h),dp(n,i,o,p,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};zx.className="LayerNormalization";re.registerClass(zx);function jW(e,t,n){return D(()=>{if(e.rank!==4)throw new z(`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 z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=_a()),n!=="channelsLast"&&n!=="channelsFirst")throw new z(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],Zn(e,a)})}var Bx=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?_a():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new z(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new z(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new z(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){e=ht(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return D(()=>jW(Me(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Bx.className="ZeroPadding2D";re.registerClass(Bx);function Sm(e,t,n,a,r,s){return D(()=>{Dt(r),gk(s),Jn(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=_a()),s==null&&(s="max"),e=lx(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Ft(e,t,n,o):i=Xn(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}function $I(e,t,n,a,r,s){return D(()=>{Dt(r),gk(s),Jn(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=_a()),s==null&&(s="max"),e=SI(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Yy(e,t,n,o):i=Py(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,4,1,2,3])),i})}var DI=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 z(`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 z(`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,Jn(this.padding),this.inputSpec=[new Xt({ndim:3})]}computeOutputShape(e){e=ht(e);let t=Da(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return D(()=>{this.invokeCallHook(e,t),e=Qc(Me(e),2);let n=this.poolingFunction(Me(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Qr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Wx=class extends DI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Jn(a),Sm(e,t,n,a,r,"max")}};Wx.className="MaxPooling1D";re.registerClass(Wx);var Vx=class extends DI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Jn(a),Sm(e,t,n,a,r,"avg")}};Vx.className="AveragePooling1D";re.registerClass(Vx);var RI=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 z(`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];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Dt(this.dataFormat),Jn(this.padding),this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Da(t,this.poolSize[0],this.padding,this.strides[0]),n=Da(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 D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(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}},Ux=class extends RI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Jn(a),Sm(e,t,n,a,r,"max")}};Ux.className="MaxPooling2D";re.registerClass(Ux);var Gx=class extends RI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Jn(a),Sm(e,t,n,a,r,"avg")}};Gx.className="AveragePooling2D";re.registerClass(Gx);var MI=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 z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Dt(this.dataFormat),Jn(this.padding),this.inputSpec=[new Xt({ndim:5})]}computeOutputShape(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Da(t,this.poolSize[0],this.padding,this.strides[0]),n=Da(n,this.poolSize[1],this.padding,this.strides[1]),a=Da(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Hx=class extends MI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Jn(a),$I(e,t,n,a,r,"max")}};Hx.className="MaxPooling3D";re.registerClass(Hx);var jx=class extends MI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Jn(a),$I(e,t,n,a,r,"avg")}};jx.className="AveragePooling3D";re.registerClass(jx);var PI=class extends qe{constructor(e){super(e);this.inputSpec=[new Xt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},qx=class extends PI{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Me(e);return St(n,1)})}};qx.className="GlobalAveragePooling1D";re.registerClass(qx);var Kx=class extends PI{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Me(e);return Yn(n,1)})}};Kx.className="GlobalMaxPooling1D";re.registerClass(Kx);var OI=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Dt(this.dataFormat),this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new $e}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Xx=class extends OI{call(e,t){return D(()=>{let n=Me(e);return this.dataFormat==="channelsLast"?St(n,[1,2]):St(n,[2,3])})}};Xx.className="GlobalAveragePooling2D";re.registerClass(Xx);var Yx=class extends OI{call(e,t){return D(()=>{let n=Me(e);return this.dataFormat==="channelsLast"?Yn(n,[1,2]):Yn(n,[2,3])})}};Yx.className="GlobalMaxPooling2D";re.registerClass(Yx);var LI=class extends qe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let a=t.layer,r=$a(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},Zx=class extends LI{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ht(e),e.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=ht(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return D(()=>(e=Me(e),AI((n,a)=>[Me(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Zx.className="TimeDistributed";re.registerClass(Zx);function qW(e){Pi(sz,"BidirectionalMergeMode",e)}var KW="concat",Jx=class extends LI{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=$a(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=$a(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?KW:e.mergeMode,qW(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,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):kn(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=EI(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new z("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 Xt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),i.push(...c)}if(a!=null)throw new $e("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Fa;for(let l of s)if(l instanceof Fa!==o)throw new z("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),c=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=c;let p=super.apply(l,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return D(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=Mn(r,1));let 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+          because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),ga(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,jt(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,a)=>this.write(n,t[a]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let a=0;a<this.size();a++)e.push(a)}if(e.length===0)return qn([],[0].concat(this.elementShape));let n=this.readMany(e);return ga(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),$t(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return qn([],[0].concat(this.elementShape));let t=[];for(let a=0;a<this.size();a++)t.push(a);let n=this.readMany(t);return ga(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),Qe(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);let n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n}  vs. ${this.maxSize})`);this.writeMany(e,dt(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,a=e.map(o=>(n+=o,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
-        ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,s=[];D(()=>{t=j(t,[1,n,r]);for(let o=0;o<e.length;++o){let l=o===0?0:a[o-1],c=[0,l,0],u=[1,e[o],r];s[o]=j(We(t,c,u),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},hp=class{constructor(e,t,n,a=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);fa(t,r.shape,"TensorList shape mismatch: "),jt(r)}),this.idTensor=de(0),this.maxNumElements=a,jt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new hp([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);return fa(e,this.elementShape,"TensorList shape mismatch: "),D(()=>{let a=this.tensors.map(r=>j(r,e));return $t(a,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=this.tensors.pop();return fa(n.shape,e,"TensorList shape mismatch: "),j(n,e)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(fa(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");jt(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);return fa(this.tensors[e].shape,t,"TensorList shape mismatch: "),this.tensors[e]}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);fa(this.elementShape,t.shape,"TensorList shape mismatch: "),jt(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);return fa(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size()),e.length===0?qn([],[0].concat(this.elementShape)):D(()=>{let a=e.map(r=>j(this.tensors[r],n));return $t(a,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);return fa(this.elementShape,t,"TensorList shape mismatch: "),this.size()===0?qn([],[0].concat(this.elementShape)):D(()=>{let n=this.tensors.map(a=>j(a,t));return Qe(n,0)})}};function M4(e,t,n){let a=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);fa(r,t,"TensorList shape mismatch: ");let s=dt(e);return new hp(s,t,a)}function P4(e,t,n){return new hp([],e,t,n)}function O4(e,t,n,a){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(a!=null&&a!==-1&&r>=a)throw new Error(`Max index must be < array size (${r}  vs. ${a})`);let s=new hp([],n,e.dtype,a),i=dt(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function L4(e,t,n){let a=0,r=t.map(l=>(a+=l,a));if(a!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
+        ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,s=[];D(()=>{t=H(t,[1,n,r]);for(let o=0;o<e.length;++o){let l=o===0?0:a[o-1],c=[0,l,0],u=[1,e[o],r];s[o]=H(We(t,c,u),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},mp=class{constructor(e,t,n,a=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);ga(t,r.shape,"TensorList shape mismatch: "),jt(r)}),this.idTensor=de(0),this.maxNumElements=a,jt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new mp([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);ga(e,this.elementShape,"TensorList shape mismatch: ");let a=hp(this.elementShape,this.tensors,e);return D(()=>{let r=this.tensors.map(s=>H(s,a));return $t(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=hp(this.elementShape,this.tensors,e),a=this.tensors.pop();return ga(a.shape,e,"TensorList shape mismatch: "),H(a,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(ga(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");jt(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);ga(this.tensors[e].shape,t,"TensorList shape mismatch: ");let a=hp(this.elementShape,this.tensors,t);return H(this.tensors[e],a)}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);ga(this.elementShape,t.shape,"TensorList shape mismatch: "),jt(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);ga(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let a=hp(this.elementShape,this.tensors,n);return e.length===0?qn([],[0].concat(a)):D(()=>{let r=e.map(s=>H(this.tensors[s],a));return $t(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);ga(this.elementShape,t,"TensorList shape mismatch: ");let n=hp(this.elementShape,this.tensors,t);return this.size()===0?qn([],[0].concat(n)):D(()=>{let a=this.tensors.map(r=>H(r,n));return Qe(a,0)})}};function B4(e,t,n){let a=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);ga(r,t,"TensorList shape mismatch: ");let s=dt(e);return new mp(s,t,a)}function W4(e,t,n){return new mp([],e,t,n)}function V4(e,t,n,a){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(a!=null&&a!==-1&&r>=a)throw new Error(`Max index must be < array size (${r}  vs. ${a})`);let s=new mp([],n,e.dtype,a),i=dt(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function U4(e,t,n){let a=0,r=t.map(u=>(a+=u,a));if(a!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
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-        ${a}, and tensor's shape is: ${e.shape}`);let s=a===0?0:e.size/a,i=D(()=>{let l=[];e=j(e,[1,a,s]);for(let c=0;c<t.length;++c){let u=c===0?0:r[c-1],p=[0,u,0],d=[1,t[c],s];l[c]=j(We(e,p,d),n)}return e.dispose(),l}),o=new hp([],n,e.dtype,t.length);for(let l=0;l<i.length;l++)o.setItem(l,i[l]);return o}var z4=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let a=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await s.data())[0]?n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let a=I("body",e,t,n),r=I("cond",e,t,n),s=I("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(u=>u.id),l=await i[0].data();i.forEach(u=>{!u.kept&&o.indexOf(u.id)===-1&&u.dispose()});let c=s;for(;l[0];){let u=c;c=await n.functionMap[a].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);let p=c.map(h=>h.id);u.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()});let d=await n.functionMap[r].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);l=await d[0].data(),d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()})}return c}case"LoopCond":{let a=I("pred",e,t,n);return[Ir(a)]}case"Switch":{let a=I("pred",e,t,n),r=I("data",e,t,n);return r.kept||(r=Ir(r)),(await a.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let a=e.inputNames.find(r=>Nn(r,t,n)!==void 0);if(a){let r=Nn(a,t,n);return[Ir(r)]}return}case"Enter":{let a=I("frameName",e,t,n),r=I("tensor",e,t,n);return n.enterFrame(a),[Ir(r)]}case"Exit":{let a=I("tensor",e,t,n);return n.exitFrame(),[Ir(a)]}case"NextIteration":{let a=I("tensor",e,t,n);return n.nextIteration(),[Ir(a)]}case"TensorArrayV3":{let a=I("size",e,t,n),r=I("dtype",e,t,n),s=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),l=I("identicalElementShapes",e,t,n),c=I("name",e,t,n),u=new R4(c,r,a,s,l,i,o);return n.addTensorArray(u),[u.idTensor,de(1)]}case"TensorArrayWriteV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(a.id).read(r)]}case"TensorArrayGatherV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(a.id).gather(r,s)]}case"TensorArrayScatterV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id),s=I("dtype",e,t,n);return[r.concat(s)]}case"TensorArraySplitV3":{let a=I("tensorArrayId",e,t,n),r=I("tensor",e,t,n),s=I("lengths",e,t,n),i=n.getTensorArray(a.id);return i.split(s,r),[i.idTensor]}case"TensorArraySizeV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return[de(r.size(),"int32")]}case"TensorArrayCloseV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let a=I("tensorListId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorList(a.id);return i.setItem(r,s),[i.idTensor]}case"TensorListGetItem":{let a=I("tensorListId",e,t,n),r=I("index",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).getItem(r,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let a=I("indices",e,t,n),r=I("tensor",e,t,n),s=I("elementShape",e,t,n),i=I("numElements",e,t,n),o=O4(r,a,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let a=I("elementShape",e,t,n),r=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=P4(a,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let a=I("tensorListId",e,t,n),r=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).gather(r,i,s)]}case"TensorListStack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(a.id).stack(r,s,i)]}case"TensorListFromTensor":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=M4(a,r,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let a=I("tensorListId",e,t,n),r=n.getTensorList(a.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[r.concat(s,i)]}case"TensorListPushBack":{let a=I("tensorListId",e,t,n),r=I("tensor",e,t,n),s=n.getTensorList(a.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let 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c=I("strides",e,t,n),u=Sm(e,t,n),p=I("dataFormat",e,t,n).toUpperCase(),d=I("dilations",e,t,n),[h,m]=I("args",e,t,n),f=I("leakyreluAlpha",e,t,n);return{stride:c,pad:u,dataFormat:p,dilations:d,biasArg:h,preluArg:m,activationFunc:r,leakyreluAlpha:f}}var B4=(e,t,n)=>{switch(e.op){case"Conv1D":{let a=I("stride",e,t,n),r=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[ch(I("x",e,t,n),I("filter",e,t,n),a,r,s,i)]}case"Conv2D":{let 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a=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[Oy(I("x",e,t,n),a,r)]}case"BroadcastTo":return[Lc(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function cN(e,t,n,a){let r=((s,i,o)=>{switch(s.category){case"arithmetic":return D(()=>F4(s,i,o));case"basic_math":return D(()=>$4(s,i,o));case"control":return z4(s,i,o);case"convolution":return D(()=>B4(s,i,o));case"creation":return D(()=>W4(s,i,o));case"dynamic":return V4(s,i,o);case"evaluation":return D(()=>U4(s,i,o));case"image":return D(()=>q4(s,i,o));case"graph":return D(()=>G4(s,i,o));case"logical":return D(()=>K4(s,i,o));case"matrices":return D(()=>X4(s,i,o));case"normalization":return D(()=>Y4(s,i,o));case"reduction":return D(()=>Z4(s,i,o));case"slice_join":return D(()=>J4(s,i,o));case"spectral":return D(()=>Q4(s,i,o));case"transformation":return D(()=>eV(s,i,o));case"hash_table":return j4(s,i,o,a);case"custom":let l=WI(s.op);if(l&&l.customExecutor)return l.customExecutor(new A4(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return k.isPromise(r)?r.then(s=>[].concat(s)):[].concat(r)}var pN=class{constructor(e={},t={},n={},a={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function hN(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>On(d)[0]),u=[];a!=null&&(u=a.map(d=>On(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((dN(d)||tV(d)||nV(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&c.indexOf(d.name)===-1&&u.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function aV(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(u=>On(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{a.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{a.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{a.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(p=>{!l.has(p.name)&&a.has(p.name)&&p.inputs.every(d=>l.has(d.name))&&s.push(p)})}return c}var rV=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],sV=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],iV=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function dN(e){return rV.indexOf(e.op)>=0}function tV(e){return sV.indexOf(e.op)>=0}function nV(e){return iV.indexOf(e.op)>=0}var uv=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new uv(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=hN(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return aV(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(u=>this.graph.nodes[On(u)[0]]),r=t.map(u=>On(u)[0]),s=r.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},c={};return D(()=>{let u=new pN(this.weightMap,l,c,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,g]=On(m),y=[];y[g]=e[m],p[f]=y});let d=this.getFrozenTensorIds(p),h={};for(let m=0;m<o.length;m++){let f=o[m];if(!p[f.name]){let g=cN(f,p,u,this._resourceManager);if(k.isPromise(g))throw new Error(`The execution of the op '${f.op}' returned a promise. 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You can use model.execute() instead.");let y=o.filter(b=>!dN(b)&&!Nn(b.name,h,t)).map(b=>b.name);if(y.length>0){let b="";throw u!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${c}]. ${b}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let p="";if(u.node.op==="Enter"&&I("isConstant",u.node,a,n)&&([p]=kr(u.node.name,n)),a[u.node.name]==null){let d=cN(u.node,a,n,this._resourceManager);p||([p]=kr(u.node.name,n));let h=n.currentContext;k.isPromise(d)?c.push(d.then(m=>(a[p]=m,n.currentContext=h,this.checkTensorForDisposal(p,u.node,a,n,s,i,o),this.processChildNodes(u.node,t,n,a,r,l),m))):(a[p]=d,this.checkTensorForDisposal(p,u.node,a,n,s,i,o),this.processChildNodes(u.node,t,n,a,r,l))}else this.processChildNodes(u.node,t,n,a,r,l)}return c}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=kr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Nn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Nn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=On(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&k.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=On(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=On(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},oV=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},lV="?tfjs-format=file",uV="model.json",mN=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new oV}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Ht.browserHTTPRequest(e,this.loadOptions);else{let t=Ht.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Ht.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let a=Ht.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new uv(iN.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=iN.Instance.transformGraph(e.modelInitializer);this.initializer=new uv(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Ht.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Ae)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function cV(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${uV}${lV}`);let n=new mN(e,t);return await n.load(),n}var fN="3.0.0",gN={};Oe(gN,{CSVDataset:()=>bN,Dataset:()=>Ql,FileDataSource:()=>xN,TextLineDataset:()=>yN,URLDataSource:()=>vN,array:()=>pV,csv:()=>hV,func:()=>mV,generator:()=>fV,microphone:()=>yV,version_data:()=>wN,webcam:()=>gV,zip:()=>dV});var bV=Io(dd()),xV=Io(dd());function vV(e,t){return Cm(e,t)}function Cm(e,t,n=new Map,a=new Set){if(e==null)return null;if(a.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(eu(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=Cm(o,t,n,a);s[i]=l}return a.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function wV(e,t=IN){return kN(e,t)}function kN(e,t,n=new Set){let a=e[0];if(n.has(a))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(eu(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(c=>c[i]),l=kN(o,t,n);s[i]=l}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function IN(e){return e===null?null:eu(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function NN(e,t){let n=new Map;Cm(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(k.isPromise(r)){let s=await r;n.set(a,s)}}return Cm(e,t,n)}function eu(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ae))}function IV(e){return e==null||kV(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ae||k.isTypedArray(e)}function kV(e){return e===null||typeof e!="object"&&typeof e!="function"}function TV(e){return vV(e,NV)}function NV(e){return e instanceof Ae?{value:e.clone(),recurse:!1}:eu(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var TN=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},cv=class extends TN{constructor(){super(cv.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let a=0;a<n;a++)t[a]=this.get(this.wrap(this.begin+a));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};cv.INITIAL_CAPACITY=32;function SN(e){return new SV(e)}function pv(e){return new CV(e)}function _V(e,t){return new CN(e,t)}function AV(e,t=ls.FAIL){return new EV(e,t)}var Yt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new OV(this,e)}filter(e){return new MV(this,e)}map(e){return new PV(this,e)}mapAsync(e){return new _N(this,e)}serialMapAsync(e){return new _N(this,e).serial()}flatmap(e){return new LV(this,e)}async forEachAsync(e){return 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Yt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},$V=class extends Yt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Fe(e.value)}return this.upstream.next()}},DV=class extends Yt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RV=class extends Yt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},MV=class extends Yt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Fe(e.value)}}},PV=class extends Yt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ia.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ia.getTensorsInContainer(n);for(let r of t)Ia.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},OV=class extends Yt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},_N=class extends Yt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ia.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Ia.getTensorsInContainer(n);for(let r of t)Ia.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},dv=class extends Yt{constructor(){super();this.outputQueue=new cv,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},LV=class extends dv{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ia.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ia.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Ia.isTensorInList(r,a)||r.dispose();return!0}},CN=class extends Yt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},ls;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ls||(ls={}));var EV=class extends Yt{constructor(e,t=ls.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function a(s){return s instanceof 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-`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},_m='"',mp=Symbol("out"),AN=Symbol("field"),Em=Symbol("quote"),hv=Symbol("quoteafterquote"),FN=Symbol("quoteinquote"),bN=class extends Ql{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new yN(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r<this.fullColumnNames.length;r++){let s=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let c=Number(o);if(isNaN(c))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=c;else switch(i.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(o);break;default:l=c}}i&&i.isLabel?a[s]=l:n[s]=l}}return Object.keys(a).length===0?n:{xs:n,ys:a}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],a=0,r=e.length,s=mp;for(let i=0;i<r;i++)switch(s){case mp:switch(e.charAt(i)){case _m:a=i+1,s=Em;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=mp;break;default:s=AN,a=i;break}break;case AN:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=mp,a=i+1;break;default:}break;case Em:switch(e.charAt(i)){case _m:s=hv;break;default:}break;case hv:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=mp,a=i+1;break;case _m:s=Em;break;default:s=FN;break}break;case FN:switch(e.charAt(i)){case _m:s=Em;break;default:}break;default:}if(s===hv?n.push(e.substring(a,r-1)):n.push(e.substring(a)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},$N=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(ee().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new $N(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 a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[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(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&a({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(r),a({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((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),qn(n,t)}},DN=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=tt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Ta([s,r,o,i],[1,4])}else this.cropBox=Ta([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(ee().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 DN(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.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=ki.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return D(()=>{let t=Fn(ce(e,"float32"),0),n;n=Ya.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return j(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},RN=class{},MN=class extends Yt{split(e){return new VV(this,e)}},VV=class extends MN{constructor(e,t){super();this.upstream=e,this.impl=new UV(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},UV=class extends dv{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},HV=class extends Yt{decodeUTF8(){return new GV(this)}},GV=class extends MN{constructor(e){super();this.upstream=e,this.impl=new jV(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},jV=class extends dv{constructor(e){super();if(this.upstream=e,ee().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=mE();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return ee().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},PN=class extends HV{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(ee().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 a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function KV(e,t={}){let n,a;typeof e=="string"?n=e:(n=e.url,a=qV(e));let r=await k.fetch(n,a);if(r.ok){let s=new Uint8Array(await r.arrayBuffer());return new PN(s,t)}else throw new Error(r.statusText)}var qV=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function ON(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var xN=class extends RN{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(ON(this.input)&&ee().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new PN(this.input,this.options)}},vN=class extends RN{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return ON(this.url)?new xN(this.url,this.fileOptions).iterator():KV(this.url,this.fileOptions)}};function hV(e,t={}){return new bN(new vN(e),t)}function mV(e){let t=pv(e);return Ln(async()=>t)}function fV(e){return Ln(async()=>{let t=await e();return pv(()=>t.next())})}async function gV(e,t){return DN.create(e,t)}async function yV(e){return $N.create(e)}var wN="3.0.0";function we(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var XV=Za.whereImpl,YV=class extends sc{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new hd(this,Kr())}write(e,t,n){this.firstUse&&(this.firstUse=!1,ee().get("IS_NODE")&&_.warn(`
+        ${a}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=pv(s,n),o=a===0?0:e.size/a,l=D(()=>{let u=[];e=H(e,[1,a,o]);for(let p=0;p<t.length;++p){let d=p===0?0:r[p-1],h=[0,d,0],m=[1,t[p],o];u[p]=H(We(e,h,m),i)}return e.dispose(),u}),c=new mp([],n,e.dtype,t.length);for(let u=0;u<l.length;u++)c.setItem(u,l[u]);return c}var G4=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let a=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await s.data())[0]?n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let a=I("body",e,t,n),r=I("cond",e,t,n),s=I("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(u=>u.id),l=await i[0].data();i.forEach(u=>{!u.kept&&o.indexOf(u.id)===-1&&u.dispose()});let c=s;for(;l[0];){let u=c;c=await n.functionMap[a].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);let p=c.map(h=>h.id);u.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()});let d=await n.functionMap[r].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);l=await d[0].data(),d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()})}return c}case"LoopCond":{let a=I("pred",e,t,n);return[Nr(a)]}case"Switch":{let a=I("pred",e,t,n),r=I("data",e,t,n);return r.kept||(r=Nr(r)),(await a.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let a=e.inputNames.find(r=>Nn(r,t,n)!==void 0);if(a){let r=Nn(a,t,n);return[Nr(r)]}return}case"Enter":{let a=I("frameName",e,t,n),r=I("tensor",e,t,n);return n.enterFrame(a),[Nr(r)]}case"Exit":{let a=I("tensor",e,t,n);return n.exitFrame(),[Nr(a)]}case"NextIteration":{let a=I("tensor",e,t,n);return n.nextIteration(),[Nr(a)]}case"TensorArrayV3":{let a=I("size",e,t,n),r=I("dtype",e,t,n),s=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),l=I("identicalElementShapes",e,t,n),c=I("name",e,t,n),u=new z4(c,r,a,s,l,i,o);return n.addTensorArray(u),[u.idTensor,de(1)]}case"TensorArrayWriteV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(a.id).read(r)]}case"TensorArrayGatherV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(a.id).gather(r,s)]}case"TensorArrayScatterV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id),s=I("dtype",e,t,n);return[r.concat(s)]}case"TensorArraySplitV3":{let a=I("tensorArrayId",e,t,n),r=I("tensor",e,t,n),s=I("lengths",e,t,n),i=n.getTensorArray(a.id);return i.split(s,r),[i.idTensor]}case"TensorArraySizeV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return[de(r.size(),"int32")]}case"TensorArrayCloseV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let a=I("tensorListId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorList(a.id);return i.setItem(r,s),[i.idTensor]}case"TensorListGetItem":{let a=I("tensorListId",e,t,n),r=I("index",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).getItem(r,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let a=I("indices",e,t,n),r=I("tensor",e,t,n),s=I("elementShape",e,t,n),i=I("numElements",e,t,n),o=V4(r,a,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let a=I("elementShape",e,t,n),r=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=W4(a,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let a=I("tensorListId",e,t,n),r=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).gather(r,i,s)]}case"TensorListStack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(a.id).stack(r,s,i)]}case"TensorListFromTensor":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=B4(a,r,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let a=I("tensorListId",e,t,n),r=n.getTensorList(a.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[r.concat(s,i)]}case"TensorListPushBack":{let a=I("tensorListId",e,t,n),r=I("tensor",e,t,n),s=n.getTensorList(a.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let 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a=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[By(I("x",e,t,n),a,r)]}case"BroadcastTo":return[Lc(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function gN(e,t,n,a){let r=((s,i,o)=>{switch(s.category){case"arithmetic":return D(()=>O4(s,i,o));case"basic_math":return D(()=>L4(s,i,o));case"control":return G4(s,i,o);case"convolution":return D(()=>H4(s,i,o));case"creation":return D(()=>j4(s,i,o));case"dynamic":return q4(s,i,o);case"evaluation":return D(()=>K4(s,i,o));case"image":return D(()=>J4(s,i,o));case"graph":return D(()=>X4(s,i,o));case"logical":return D(()=>Q4(s,i,o));case"matrices":return D(()=>eV(s,i,o));case"normalization":return D(()=>tV(s,i,o));case"reduction":return D(()=>nV(s,i,o));case"slice_join":return D(()=>aV(s,i,o));case"spectral":return D(()=>rV(s,i,o));case"transformation":return D(()=>sV(s,i,o));case"hash_table":return Z4(s,i,o,a);case"custom":let l=jI(s.op);if(l&&l.customExecutor)return l.customExecutor(new P4(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. 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e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function xN(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>Ln(d)[0]),u=[];a!=null&&(u=a.map(d=>Ln(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((bN(d)||iV(d)||oV(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&c.indexOf(d.name)===-1&&u.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function lV(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(u=>Ln(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{a.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{a.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{a.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(p=>{!l.has(p.name)&&a.has(p.name)&&p.inputs.every(d=>l.has(d.name))&&s.push(p)})}return c}var uV=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],cV=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],pV=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function bN(e){return uV.indexOf(e.op)>=0}function iV(e){return cV.indexOf(e.op)>=0}function oV(e){return pV.indexOf(e.op)>=0}var hv=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new hv(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=xN(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. 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Missing the following inputs: [${a}]`)}return lV(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(u=>this.graph.nodes[Ln(u)[0]]),r=t.map(u=>Ln(u)[0]),s=r.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},c={};return D(()=>{let u=new yN(this.weightMap,l,c,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,g]=Ln(m),y=[];y[g]=e[m],p[f]=y});let d=this.getFrozenTensorIds(p),h={};for(let m=0;m<o.length;m++){let f=o[m];if(!p[f.name]){let g=gN(f,p,u,this._resourceManager);if(k.isPromise(g))throw new Error(`The execution of the op '${f.op}' returned a promise. 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You can use model.execute() instead.");let y=o.filter(b=>!bN(b)&&!Nn(b.name,h,t)).map(b=>b.name);if(y.length>0){let b="";throw u!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. 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c}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Ir(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Nn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Nn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Ln(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&k.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Ln(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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 '${t}' is not found in the graph`)})}},dV=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},hV="?tfjs-format=file",mV="model.json",vN=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new dV}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Ht.browserHTTPRequest(e,this.loadOptions);else{let t=Ht.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Ht.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let a=Ht.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new hv(pN.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=pN.Instance.transformGraph(e.modelInitializer);this.initializer=new hv(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Ht.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Ae)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function fV(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${mV}${hV}`);let n=new vN(e,t);return await n.load(),n}var wN="3.1.0",kN={};Oe(kN,{CSVDataset:()=>NN,Dataset:()=>Ql,FileDataSource:()=>TN,TextLineDataset:()=>IN,URLDataSource:()=>SN,array:()=>gV,csv:()=>bV,func:()=>xV,generator:()=>vV,microphone:()=>kV,version_data:()=>CN,webcam:()=>wV,zip:()=>yV});var IV=To(fd()),NV=To(fd());function TV(e,t){return Am(e,t)}function Am(e,t,n=new Map,a=new Set){if(e==null)return null;if(a.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(eu(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=Am(o,t,n,a);s[i]=l}return a.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function SV(e,t=EN){return _N(e,t)}function _N(e,t,n=new Set){let a=e[0];if(n.has(a))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(eu(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(c=>c[i]),l=_N(o,t,n);s[i]=l}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function EN(e){return e===null?null:eu(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function AN(e,t){let n=new Map;Am(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(k.isPromise(r)){let s=await r;n.set(a,s)}}return Am(e,t,n)}function eu(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ae))}function _V(e){return e==null||CV(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ae||k.isTypedArray(e)}function 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Yt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},OV=class extends Yt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Fe(e.value)}return this.upstream.next()}},LV=class extends Yt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},zV=class extends Yt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},BV=class extends Yt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Fe(e.value)}}},WV=class extends Yt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Na.getTensorsInContainer(e.value),n=this.transform(e.value),a=Na.getTensorsInContainer(n);for(let r of t)Na.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},VV=class extends Yt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},RN=class extends Yt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Na.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Na.getTensorsInContainer(n);for(let r of t)Na.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},gv=class extends Yt{constructor(){super();this.outputQueue=new mv,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},UV=class extends gv{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Na.getTensorsInContainer(e.value),n=this.transform(e.value),a=Na.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Na.isTensorInList(r,a)||r.dispose();return!0}},DN=class extends Yt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},ls;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ls||(ls={}));var RV=class extends Yt{constructor(e,t=ls.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function a(s){return s instanceof 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+`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Fm='"',fp=Symbol("out"),PN=Symbol("field"),$m=Symbol("quote"),yv=Symbol("quoteafterquote"),ON=Symbol("quoteinquote"),NN=class extends Ql{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new IN(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r<this.fullColumnNames.length;r++){let s=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let c=Number(o);if(isNaN(c))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=c;else switch(i.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(o);break;default:l=c}}i&&i.isLabel?a[s]=l:n[s]=l}}return Object.keys(a).length===0?n:{xs:n,ys:a}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],a=0,r=e.length,s=fp;for(let i=0;i<r;i++)switch(s){case fp:switch(e.charAt(i)){case Fm:a=i+1,s=$m;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=fp;break;default:s=PN,a=i;break}break;case PN:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=fp,a=i+1;break;default:}break;case $m:switch(e.charAt(i)){case Fm:s=yv;break;default:}break;case yv:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=fp,a=i+1;break;case Fm:s=$m;break;default:s=ON;break}break;case ON:switch(e.charAt(i)){case Fm:s=$m;break;default:}break;default:}if(s===yv?n.push(e.substring(a,r-1)):n.push(e.substring(a)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},LN=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(ee().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new LN(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 a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[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(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&a({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(r),a({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((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),qn(n,t)}},zN=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=tt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Sa([s,r,o,i],[1,4])}else this.cropBox=Sa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(ee().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 zN(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.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=Ni.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return D(()=>{let t=$n(ce(e,"float32"),0),n;n=Za.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return H(n,a.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.")}},BN=class{},WN=class extends Yt{split(e){return new qV(this,e)}},qV=class extends WN{constructor(e,t){super();this.upstream=e,this.impl=new KV(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},KV=class extends gv{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}},YV=class extends Yt{decodeUTF8(){return new XV(this)}},XV=class extends WN{constructor(e){super();this.upstream=e,this.impl=new ZV(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},ZV=class extends gv{constructor(e){super();if(this.upstream=e,ee().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=vE();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 ee().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},VN=class extends YV{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(ee().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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function QV(e,t={}){let n,a;typeof e=="string"?n=e:(n=e.url,a=JV(e));let r=await k.fetch(n,a);if(r.ok){let s=new Uint8Array(await r.arrayBuffer());return new VN(s,t)}else throw new Error(r.statusText)}var JV=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function UN(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var TN=class extends BN{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(UN(this.input)&&ee().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new VN(this.input,this.options)}},SN=class extends BN{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return UN(this.url)?new TN(this.url,this.fileOptions).iterator():QV(this.url,this.fileOptions)}};function bV(e,t={}){return new NN(new SN(e),t)}function xV(e){let t=fv(e);return zn(async()=>t)}function vV(e){return zn(async()=>{let t=await e();return fv(()=>t.next())})}async function wV(e,t){return zN.create(e,t)}async function kV(e){return LN.create(e)}var CN="3.1.0";function we(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var eU=Ja.whereImpl,bv=class extends rc{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new gd(this,Ha())}nextDataId(){return bv.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,ee().get("IS_NODE")&&_.warn(`
 ============================
 Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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lG={kernelName:lc,backendName:"cpu",kernelFunc:oG},uG=it(Eo,e=>Math.asin(e)),cG={kernelName:Eo,backendName:"cpu",kernelFunc:uG},pG=it(Ao,e=>Math.asinh(e)),dG={kernelName:Ao,backendName:"cpu",kernelFunc:pG},hG=it(Fo,e=>Math.atan(e)),mG={kernelName:Fo,backendName:"cpu",kernelFunc:hG},fG=Rt((e,t)=>Math.atan2(e,t)),gG=Zt(Do,fG),yG={kernelName:Do,backendName:"cpu",kernelFunc:gG},bG=it($o,e=>Math.atanh(e)),xG={kernelName:$o,backendName:"cpu",kernelFunc:bG};function Iv(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,c=r.dilationWidth,u=r.effectiveFilterHeight,p=r.effectiveFilterWidth,d=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=Le(r.outShape,n),g=f.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],b=r.outShape[2]*r.outShape[3],v=r.outShape[3];for(let x=0;x<r.batchSize;++x){let N=x*y,T=x*a[0];for(let C=0;C<r.inChannels;++C)for(let $=0;$<r.outHeight;++$){let 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n.makeTensorInfo(r.shape,r.dtype,f)}var AG={kernelName:Bs,backendName:"cpu",kernelFunc:EG};function FG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;we([r],"batchToSpaceND");let o=s.reduce((y,b)=>y*b),l=_.getReshaped(r.shape,s,o),c=_.getPermuted(l.length,s.length),u=_.getReshapedPermuted(r.shape,s,o),p=_.getSliceBeginCoords(i,s.length),d=_.getSliceSize(u,i,s.length),h=It({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ga({inputs:{x:h},backend:n,attrs:{perm:c}}),f=It({inputs:{x:m},backend:n,attrs:{shape:u}}),g=Vi({inputs:{x:f},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var $G={kernelName:cc,backendName:"cpu",kernelFunc:FG};function DG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,c=mv(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var 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n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>k.sizeFromShape(f.shape)>0);if(o.length===1)return ar({inputs:{x:o[0]},backend:n});let l=o.map(f=>f.shape);if(_.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(x=>Wi({inputs:{input:x},backend:n})),g=o.map(x=>au({inputs:{input:x},backend:n})),y=ru({inputs:f,backend:n,attrs:{axis:s}}),b=ru({inputs:g,backend:n,attrs:{axis:s}}),v=zn({inputs:{real:y,imag:b},backend:n});return f.forEach(x=>n.disposeIntermediateTensorInfo(x)),g.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),v}let c=o.map(f=>{let g=k.sizeFromShape(f.shape.slice(s));return It({inputs:{x:f},backend:n,attrs:{shape:[-1,g]}})}),u=c.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=_.computeOutShape(c.map(f=>f.shape),1);let p=c[0].shape[0]===1,d=fv(u,i,t[0].dtype,p),h=_.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,d);return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var BG={kernelName:Mo,backendName:"cpu",kernelFunc:ru};function xT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=a;we([r,s],"conv2d");let p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!1,p),h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.padInfo.left,b=d.padInfo.top,v=d.dataFormat==="channelsLast",x=new Ot(d.outShape,r.dtype),N=k.computeStrides(r.shape),T=k.computeStrides(s.shape),C=N[0],$=v?N[1]:N[2],F=v?N[2]:1,O=v?1:N[1],B=x.strides[0],W=v?x.strides[1]:x.strides[2],G=v?x.strides[2]:1,q=v?1:x.strides[1],H=n.data.get(r.dataId).values,K=n.data.get(s.dataId).values,X=x.values;for(let ne=0;ne<d.batchSize;++ne){let J=ne*C,se=ne*B;for(let Q=0;Q<d.outHeight;++Q){let oe=se+Q*W,ae=Q*d.strideHeight-b;for(let pe=0;pe<h;++pe){let ue=ae+pe*f;if(ue<0||ue>=d.inHeight)continue;let ge=pe*T[0],ye=J+ue*$;for(let ke=0;ke<d.outWidth;++ke){let Se=oe+ke*G,Ce=ke*d.strideWidth-y;for(let Re=0;Re<m;++Re){let Ke=Ce+Re*g;if(Ke<0||Ke>=d.inWidth)continue;let Ve=ge+Re*T[1],rt=ye+Ke*F,ot=Ve;for(let He=0;He<d.inChannels;++He){let ct=H[rt+He*O];for(let pt=0;pt<d.outChannels;++pt)X[Se+pt*q]+=ct*K[ot+pt];ot+=d.outChannels}}}}}}return n.makeTensorInfo(x.shape,x.dtype,X)}var WG={kernelName:Fs,backendName:"cpu",kernelFunc:xT};function VG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=a;we([r,s],"conv2dBackpropFilter");let p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,u,i,1,o,c,!1,p),{strideHeight:h,strideWidth:m,filterHeight:f,filterWidth:g}=d,y=d.dataFormat==="channelsLast",b=new Ot(d.filterShape,"float32"),v=d.padInfo.left,x=d.padInfo.top,N=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,C=new Ot(r.shape,r.dtype,N),$=new Ot(s.shape,s.dtype,T);for(let F=0;F<f;++F){let O=Math.max(0,Math.ceil((x-F)/h)),B=Math.min(d.outHeight,(d.inHeight+x-F)/h);for(let W=0;W<g;++W){let G=Math.max(0,Math.ceil((v-W)/m)),q=Math.min(d.outWidth,(d.inWidth+v-W)/m);for(let H=0;H<d.inChannels;++H)for(let K=0;K<d.outChannels;++K){let X=0;for(let ne=0;ne<d.batchSize;++ne)for(let J=O;J<B;++J){let se=F+J*h-x;for(let Q=G;Q<q;++Q){let oe=W+Q*m-v;y?X+=C.get(ne,se,oe,H)*$.get(ne,J,Q,K):X+=C.get(ne,H,se,oe)*$.get(ne,K,J,Q)}}b.set(X,F,W,H,K)}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var UG={kernelName:Id,backendName:"cpu",kernelFunc:VG};function GG(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=a;we([r,s],"conv2dBackpropInput");let p=k.computeStrides(s.shape),d=k.computeStrides(r.shape),h=_.convertConv2DDataFormat(c),m=_.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),f=new 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HG={kernelName:$s,backendName:"cpu",kernelFunc:GG};function jG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;we([r,s],"conv3d");let c=_.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=c,y=g.front,b=g.left,v=g.top,x=new Ot(c.outShape,r.dtype),N=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,C=x.values,$=k.computeStrides(r.shape),F=k.computeStrides(s.shape);for(let O=0;O<c.batchSize;++O){let B=O*$[0],W=O*x.strides[0];for(let G=0;G<c.outDepth;++G){let q=W+G*x.strides[1],H=G*c.strideDepth-y;for(let K=0;K<u;++K){let X=H+K*h;if(X<0||X>=c.inDepth)continue;let ne=K*F[0],J=B+X*$[1];for(let se=0;se<c.outHeight;++se){let Q=q+se*x.strides[2],oe=se*c.strideHeight-v;for(let ae=0;ae<p;++ae){let pe=oe+ae*m;if(pe<0||pe>=c.inHeight)continue;let ue=ne+ae*F[1],ge=J+pe*$[2];for(let ye=0;ye<c.outWidth;++ye){let ke=Q+ye*c.outChannels,Se=ye*c.strideWidth-b;for(let Ce=0;Ce<d;++Ce){let Re=Se+Ce*f;if(Re<0||Re>=c.inWidth)continue;let Ke=ue+Ce*F[2],Ve=ge+Re*c.inChannels,rt=Ke;for(let ot=0;ot<c.inChannels;++ot){let He=N[Ve+ot];for(let ct=0;ct<c.outChannels;++ct)C[ke+ct]+=He*T[rt+ct];rt+=c.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var qG={kernelName:dc,backendName:"cpu",kernelFunc:jG};function KG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a;we([r,s],"conv3dBackpropFilterV2");let c=k.computeStrides(r.shape),u=k.computeStrides(s.shape),p=_.computeConv3DInfo(r.shape,l,i,1,o),d=p.strideDepth,h=p.strideHeight,m=p.strideWidth,f=p.filterDepth,g=p.filterHeight,y=p.filterWidth,b=new Ot(p.filterShape,"float32"),v=b.values,[x,N,T,C]=b.strides,$=n.data.get(s.dataId).values,[F,O,B,W]=u,G=n.data.get(r.dataId).values,[q,H,K,X]=c,ne=p.padInfo.front,J=p.padInfo.left,se=p.padInfo.top;for(let Q=0;Q<f;++Q){let oe=Math.max(0,Math.ceil((ne-Q)/d)),ae=Math.min(p.outDepth,(p.inDepth+ne-Q)/d),pe=Q*x;for(let ue=0;ue<g;++ue){let ge=Math.max(0,Math.ceil((se-ue)/h)),ye=Math.min(p.outHeight,(p.inHeight+se-ue)/h),ke=ue*N+pe;for(let Se=0;Se<y;++Se){let Ce=Math.max(0,Math.ceil((J-Se)/m)),Re=Math.min(p.outWidth,(p.inWidth+J-Se)/m),Ke=Se*T+ke;for(let Ve=0;Ve<p.inChannels;++Ve){let rt=Ve*C+Ke;for(let ot=0;ot<p.outChannels;++ot){let He=0;for(let ct=0;ct<p.batchSize;++ct){let pt=ct*q,Gn=ct*F;for(let Je=oe;Je<ae;++Je){let _n=(Q+Je*d-ne)*H+pt,Qt=Je*O+Gn;for(let En=ge;En<ye;++En){let na=(ue+En*h-se)*K+_n,xn=En*B+Qt;for(let ln=Ce;ln<Re;++ln){let aa=(Se+ln*m-J)*X+na,za=ln*W+xn;He+=G[aa+Ve]*$[za+ot]}}}}v[rt+ot]=He}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var XG={kernelName:Nd,backendName:"cpu",kernelFunc:KG};function YG(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a;we([r],"conv3dBackpropInputV2");let c=k.computeStrides(r.shape),u=k.computeStrides(s.shape),p=_.computeConv3DInfo(l,s.shape,o,1,i),d=new Ot(p.inShape,"float32"),h=d.values,[m,f,g,y]=d.strides,b=n.data.get(r.dataId).values,[v,x,N,T]=c,C=n.data.get(s.dataId).values,[$,F,O,B]=u,{batchSize:W,filterDepth:G,filterHeight:q,filterWidth:H,inChannels:K,inDepth:X,inHeight:ne,inWidth:J,outChannels:se,outDepth:Q,outHeight:oe,outWidth:ae,strideDepth:pe,strideHeight:ue,strideWidth:ge}=p,ye=G-1-p.padInfo.front,ke=q-1-p.padInfo.top,Se=H-1-p.padInfo.left;for(let Ce=0;Ce<W;++Ce)for(let Re=0;Re<K;++Re)for(let Ke=0;Ke<X;++Ke){let Ve=Ke-ye,rt=Math.max(0,Math.ceil(Ve/pe)),ot=Math.min(Q,(G+Ve)/pe);for(let He=0;He<ne;++He){let ct=He-ke,pt=Math.max(0,Math.ceil(ct/ue)),Gn=Math.min(oe,(q+ct)/ue);for(let Je=0;Je<J;++Je){let _n=Je-Se,Qt=Math.max(0,Math.ceil(_n/ge)),En=Math.min(ae,(H+_n)/ge),na=0;for(let xn=rt;xn<ot;++xn){let ln=xn*pe-Ve;for(let aa=pt;aa<Gn;++aa){let za=aa*ue-ct;for(let An=Qt;An<En;++An){let uo=An*ge-_n,zu=v*Ce+x*xn+N*aa+T*An,wa=$*(G-1-ln)+F*(q-1-za)+O*(H-1-uo)+B*Re;for(let ra=0;ra<se;++ra){let ka=b[zu+ra],co=C[wa+ra];na+=ka*co}}}}h[m*Ce+f*Ke+g*He+y*Je+Re]=na}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var ZG={kernelName:Td,backendName:"cpu",kernelFunc:YG},JG=it(Ds,e=>Math.cos(e)),QG={kernelName:Ds,backendName:"cpu",kernelFunc:JG},eH=it(Po,e=>Math.cosh(e)),tH={kernelName:Po,backendName:"cpu",kernelFunc:eH};function nH(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=a,[u,p,d,h]=r.shape,m=s.shape[0],[f,g]=o,y=Le([m,f,g,h],"float32"),b=n.data.get(s.dataId).values,v=n.data.get(i.dataId).values,x=n.data.get(r.dataId).values,N=k.computeStrides(r.shape),T=k.computeStrides(y.shape);for(let C=0;C<m;C++){let $=C*4,F=b[$],O=b[$+1],B=b[$+2],W=b[$+3],G=v[C];if(G>=u)continue;let q=f>1?(B-F)*(p-1)/(f-1):0,H=g>1?(W-O)*(d-1)/(g-1):0;for(let K=0;K<f;K++){let X=f>1?F*(p-1)+K*q:.5*(F+B)*(p-1);if(X<0||X>p-1){for(let ne=0;ne<g;ne++)for(let J=0;J<h;J++){let se=J+ne*T[2]+K*T[1]+C*T[0];y.values[se]=c}continue}if(l==="bilinear"){let ne=Math.floor(X),J=Math.ceil(X),se=X-ne;for(let Q=0;Q<g;Q++){let oe=g>1?O*(d-1)+Q*H:.5*(O+W)*(d-1);if(oe<0||oe>d-1){for(let ge=0;ge<h;ge++){let ye=ge+Q*T[2]+K*T[1]+C*T[0];y.values[ye]=c}continue}let ae=Math.floor(oe),pe=Math.ceil(oe),ue=oe-ae;for(let ge=0;ge<h;ge++){let ye=ge+ae*N[2]+ne*N[1]+G*N[0],ke=x[ye];ye=ge+pe*N[2]+ne*N[1]+G*N[0];let Se=x[ye];ye=ge+ae*N[2]+J*N[1]+G*N[0];let Ce=x[ye];ye=ge+pe*N[2]+J*N[1]+G*N[0];let Re=x[ye],Ke=ke+(Se-ke)*ue,Ve=Ce+(Re-Ce)*ue;ye=ge+Q*T[2]+K*T[1]+C*T[0],y.values[ye]=Ke+(Ve-Ke)*se}}}else for(let ne=0;ne<g;++ne){let J=g>1?O*(d-1)+ne*H:.5*(O+W)*(d-1);if(J<0||J>d-1){for(let oe=0;oe<h;oe++){let ae=oe+ne*T[2]+K*T[1]+C*T[0];y.values[ae]=c}continue}let se=Math.round(J),Q=Math.round(X);for(let oe=0;oe<h;oe++){let ae=oe+se*N[2]+Q*N[1]+G*N[0],pe=oe+ne*T[2]+K*T[1]+C*T[0];y.values[pe]=x[ae]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var aH={kernelName:Oo,backendName:"cpu",kernelFunc:nH};function rH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;we(r,"cumsum");let l=_.getAxesPermutation([s],r.shape.length),c=r;l!=null&&(c=ga({inputs:{x:r},backend:n,attrs:{perm:l}}));let u=_.getInnerMostAxes(1,r.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let p=la(c.dtype,"int32"),d=k.makeZerosTypedArray(k.sizeFromShape(c.shape),p),h=n.data.get(c.dataId).values,m=c.shape[c.shape.length-1],f=o?(y,b)=>y+m-b-1:(y,b)=>y+b;for(let y=0;y<h.length;y+=m)for(let b=0;b<m;b++){let v=f(y,b);if(b===0)d[v]=i?0:h[v];else{let x=f(y,b-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(c.shape,p,d);if(l!=null){let y=_.getUndoAxesPermutation(l),b=ga({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(c),b}return g}var sH={kernelName:Rs,backendName:"cpu",kernelFunc:rH};function iH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.data.get(r.dataId).values,c=n.data.get(s.dataId).values,u=mv(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=WN(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var oH={kernelName:Sd,backendName:"cpu",kernelFunc:iH};function lH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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pH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=a;we([r,s],"depthwiseConv2dNativeBackpropFilter");let p=_.computeConv2DInfo(r.shape,u,i,o,l,c,!0),{strideHeight:d,strideWidth:h,filterHeight:m,filterWidth:f}=p,g=new Ot(p.filterShape,"float32"),y=p.padInfo.left,b=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,N=new Ot(r.shape,r.dtype,x),T=n.data.get(s.dataId).values,C=new Ot(s.shape,s.dtype,T);for(let $=0;$<m;++$){let F=Math.max(0,Math.ceil((b-$)/d)),O=Math.min(p.outHeight,(p.inHeight+b-$)/d);for(let B=0;B<f;++B){let W=Math.max(0,Math.ceil((y-B)/h)),G=Math.min(p.outWidth,(p.inWidth+y-B)/h);for(let q=0;q<p.outChannels;++q){let H=Math.trunc(q/v),K=q%v,X=0;for(let ne=0;ne<p.batchSize;++ne)for(let J=F;J<O;++J){let se=$+J*d-b;for(let Q=W;Q<G;++Q){let oe=B+Q*h-y;X+=N.get(ne,se,oe,H)*C.get(ne,J,Q,q)}}g.set(X,$,B,H,K)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var dH={kernelName:Cd,backendName:"cpu",kernelFunc:pH};function hH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=a;we([r,s],"depthwiseConv2DNativeBackpropInput");let p=k.computeStrides(r.shape),d=k.computeStrides(s.shape),h=_.computeConv2DInfo(u,s.shape,i,o,l,c,!0),m=new Ot(h.inShape,"float32"),f=m.values,[g,y,b]=m.strides,v=n.data.get(r.dataId).values,[x,N,T]=p,C=n.data.get(s.dataId).values,[$,F,O]=d,{batchSize:B,filterHeight:W,filterWidth:G,inChannels:q,inHeight:H,inWidth:K,outChannels:X,outHeight:ne,outWidth:J,strideHeight:se,strideWidth:Q}=h,oe=W-1-h.padInfo.top,ae=G-1-h.padInfo.left,pe=X/q;for(let ue=0;ue<B;++ue)for(let ge=0;ge<q;++ge)for(let ye=0;ye<H;++ye){let ke=ye-oe,Se=Math.max(0,Math.ceil(ke/se)),Ce=Math.min(ne,(W+ke)/se);for(let Re=0;Re<K;++Re){let Ke=Re-ae,Ve=Math.max(0,Math.ceil(Ke/Q)),rt=Math.min(J,(G+Ke)/Q),ot=0;for(let He=Se;He<Ce;++He){let ct=He*se-ke;for(let pt=Ve;pt<rt;++pt){let 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a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,c=[r,s],u=k.sizeFromShape(c),p=k.getTypedArrayFromDType("float32",u),d=k.getTypedArrayFromDType("float32",u);for(let g=0;g<r;g++){let y=Vi({inputs:{x:o},backend:n,attrs:{begin:[g,0],size:[1,s]}}),b=Vi({inputs:{x:l},backend:n,attrs:{begin:[g,0],size:[1,s]}}),v=zn({inputs:{real:y,imag:b},backend:n}),{real:x,imag:N}=RH(v,t,n),T=_.mergeRealAndImagArrays(x,N);for(let C=0;C<s;C++){let $=_.getComplexWithIndex(T,C);p[g*s+C]=$.real,d[g*s+C]=$.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(v)}let h=n.makeTensorInfo(c,"float32",p),m=n.makeTensorInfo(c,"float32",d),f=zn({inputs:{real:h,imag:m},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),f}function RH(e,t,n){let a=k.sizeFromShape(e.shape),r=n.data.get(e.dataId),s=n.data.get(r.complexTensorInfos.real.dataId).values,i=n.data.get(r.complexTensorInfos.imag.dataId).values;if(MH(a)){let o=Sv(s,i,a,t,n),l=[e.shape[0],e.shape[1]];if(t){let c=n.makeTensorInfo(l,"float32",o.real),u=n.makeTensorInfo(l,"float32",o.imag),p=n.makeTensorInfo([],"float32",k.createScalarValue(a,"float32")),d=ar({inputs:{x:p},backend:n}),h=Tv.kernelFunc({inputs:{a:c,b:p},backend:n}),m=Tv.kernelFunc({inputs:{a:u,b:d},backend:n}),f=n.data.get(h.dataId).values,g=n.data.get(m.dataId).values;return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),{real:f,imag:g}}return o}else{let o=_.mergeRealAndImagArrays(s,i),l=PH(o,a,t);return _.splitRealAndImagArrays(l)}}function MH(e){return(e&e-1)==0}function Sv(e,t,n,a,r){if(n===1)return{real:e,imag:t};let s=_.mergeRealAndImagArrays(e,t),i=n/2,o=_.complexWithEvenIndex(s),l=o.real,c=o.imag,u=[l.length],p=r.makeTensorInfo(u,"float32",l),d=r.makeTensorInfo(u,"float32",c),h=zn({inputs:{real:p,imag:d},backend:r}),m=_.complexWithOddIndex(s),f=m.real,g=m.imag,y=[f.length],b=r.makeTensorInfo(y,"float32",f),v=r.makeTensorInfo(y,"float32",g),x=zn({inputs:{real:b,imag:v},backend:r}),N=Sv(l,c,i,a,r),T=N.real,C=N.imag,$=[T.length],F=r.makeTensorInfo($,"float32",T),O=r.makeTensorInfo($,"float32",C),B=zn({inputs:{real:F,imag:O},backend:r}),W=Sv(f,g,i,a,r),G=W.real,q=W.imag,H=[G.length],K=r.makeTensorInfo(H,"float32",G),X=r.makeTensorInfo(H,"float32",q),ne=zn({inputs:{real:K,imag:X},backend:r}),J=_.exponents(n,a),se=[J.real.length],Q=r.makeTensorInfo(se,"float32",J.real),oe=r.makeTensorInfo(se,"float32",J.imag),ae=zn({inputs:{real:Q,imag:oe},backend:r}),pe=vv({inputs:{a:ae,b:ne},backend:r}),ue=fp({inputs:{a:B,b:pe},backend:r}),ge=wv({inputs:{a:B,b:pe},backend:r}),ye=Wi({inputs:{input:ue},backend:r}),ke=Wi({inputs:{input:ge},backend:r}),Se=au({inputs:{input:ue},backend:r}),Ce=au({inputs:{input:ge},backend:r}),Re=ru({inputs:[ye,ke],backend:r,attrs:{axis:0}}),Ke=ru({inputs:[Se,Ce],backend:r,attrs:{axis:0}}),Ve=r.data.get(Re.dataId).values,rt=r.data.get(Ke.dataId).values;return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(b),r.disposeIntermediateTensorInfo(v),r.disposeIntermediateTensorInfo(x),r.disposeIntermediateTensorInfo(F),r.disposeIntermediateTensorInfo(O),r.disposeIntermediateTensorInfo(B),r.disposeIntermediateTensorInfo(K),r.disposeIntermediateTensorInfo(X),r.disposeIntermediateTensorInfo(ne),r.disposeIntermediateTensorInfo(Q),r.disposeIntermediateTensorInfo(oe),r.disposeIntermediateTensorInfo(ae),r.disposeIntermediateTensorInfo(pe),r.disposeIntermediateTensorInfo(ue),r.disposeIntermediateTensorInfo(ge),r.disposeIntermediateTensorInfo(ye),r.disposeIntermediateTensorInfo(Se),r.disposeIntermediateTensorInfo(ke),r.disposeIntermediateTensorInfo(Ce),r.disposeIntermediateTensorInfo(Re),r.disposeIntermediateTensorInfo(Ke),{real:Ve,imag:rt}}function PH(e,t,n){let a=new Float32Array(t*2);for(let r=0;r<t;r++){let s=0,i=0;for(let o=0;o<t;o++){let 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WH={kernelName:Go,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,r=n,s=k.getTypedArrayFromDType(a.dtype,k.sizeFromShape(a.shape)),[i,o,l,c]=a.shape,u=r.data.get(a.dataId).values;for(let p=0;p<i;p++){let d=p*l*o*c;for(let h=0;h<o;h++){let m=h*(l*c);for(let f=0;f<l;f++){let g=f*c;for(let y=0;y<c;y++){let b=[i,h,f,y][2],v=Math.round(l-b),x=d+m+g+y,N=u[x];if(v>=0&&v<l){let T=v*c,C=d+m+T+y;N=u[C]}s[x]=N}}}}return{dataId:r.write(s,a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},VH=Rt((e,t)=>Math.floor(e/t)),UH=Zt(zs,VH,null,"int32"),GH={kernelName:zs,backendName:"cpu",kernelFunc:UH};function HH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=xT({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d}});if(i){let g=f;f=fp({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=kv(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var jH={kernelName:yi,backendName:"cpu",kernelFunc:HH};function qH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=vT({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d}});if(i){let g=f;f=fp({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=kv(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var KH={kernelName:bi,backendName:"cpu",kernelFunc:qH};function XH(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=k.sizeFromShape(a.shape),i=r.shape,o=i[i.length-1],[l,c,u,p]=_.prepareAndValidate(a,r);if(c===0)return n.makeTensorInfo(l,a.dtype,[]);let 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i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=ya({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],_.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[u,p]=_.computeOutAndReduceShapes(l.shape,i),d=k.sizeFromShape(u),h=k.makeZerosTypedArray(d,"int32"),m=k.sizeFromShape(p),f=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*m,b=f[y],v=0;for(let x=0;x<m;++x){let N=f[y+x];N<b&&(b=N,v=x)}h[g]=v}return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var 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pe=ae*l-y,ue=pe;for(;ue<0;)ue+=p;let ge=Math.min(r.inWidth,m+pe),ye=oe+ae*F,ke=b,Se=0,Ce=0;for(let Ke=j;Ke<X;Ke+=c){let Ve=B+Ke*a[1];for(let rt=se;rt<Q;rt+=u){let ot=Ve+rt*a[2];for(let He=ue;He<ge;He+=p){let ct=ot+He*a[3],pt=e[ct+W];if(s==="max"&&pt>ke?ke=pt:s==="avg"&&(Se+=pt,Ce++),isNaN(ke))break}if(isNaN(ke))break}if(isNaN(ke))break}let Re=ye+W;x[Re]=s==="avg"?Se/Ce:ke}}}}return v}function NG(e,t){let n=Le(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f<t.batchSize;++f)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let b=y*a-d,v=b;for(;v<0;)v+=i;let x=Math.min(t.inDepth,c+b);for(let N=0;N<t.outHeight;++N){let T=N*r-h,C=T;for(;C<0;)C+=o;let F=Math.min(t.inHeight,u+T);for(let $=0;$<t.outWidth;++$){let P=$*s-m,B=P;for(;B<0;)B+=l;let W=Math.min(t.inWidth,p+P),G=Number.NEGATIVE_INFINITY,q=-1;for(let j=v;j<x;j+=i){let X=j-b;for(let K=C;K<F;K+=o){let ne=K-T;for(let J=B;J<W;J+=l){let se=J-P,Q=e.get(f,j,K,J,g);Q>=G&&(G=Q,q=X*u*p+ne*u+se)}}}n.set(q,f,y,N,$,g)}}}return n}function TG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;we(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. 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u=_.computePool3DInfo(s.shape,i,o,1,l,c),p=u.strideDepth,d=u.strideHeight,h=u.strideWidth,m=u.filterDepth,f=u.filterHeight,g=u.filterWidth,y=u.dilationDepth,b=u.dilationHeight,v=u.dilationWidth,x=u.effectiveFilterDepth,N=u.effectiveFilterHeight,T=u.effectiveFilterWidth,C=x-1-u.padInfo.front,F=T-1-u.padInfo.left,$=N-1-u.padInfo.top,P=Le(s.shape,"float32"),B=1/(m*f*g),W=n.bufferSync(r);for(let G=0;G<u.batchSize;++G)for(let q=0;q<u.inChannels;++q)for(let j=0;j<u.inDepth;++j)for(let X=0;X<u.inHeight;++X)for(let K=0;K<u.inWidth;++K){let ne=j-C,J=X-$,se=K-F,Q=0;for(let oe=0;oe<x;oe+=y){let ae=(ne+oe)/p;if(!(ae<0||ae>=u.outDepth||Math.floor(ae)!==ae))for(let pe=0;pe<N;pe+=b){let ue=(J+pe)/d;if(!(ue<0||ue>=u.outHeight||Math.floor(ue)!==ue))for(let ge=0;ge<T;ge+=v){let ye=(se+ge)/h;ye<0||ye>=u.outWidth||Math.floor(ye)!==ye||(Q+=W.get(G,ae,ue,ye,q))}}}P.set(Q*B,G,j,X,K,q)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var AG={kernelName:Id,backendName:"cpu",kernelFunc:EG};function FG(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;we([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=a,u=_.computePool2DInfo(i.shape,o,l,1,c),p=u.strideHeight,d=u.strideWidth,h=u.filterHeight,m=u.filterWidth,f=u.dilationHeight,g=u.dilationWidth,y=u.effectiveFilterHeight,b=u.effectiveFilterWidth,v=b-1-u.padInfo.left,x=y-1-u.padInfo.top,N=Le(i.shape,"float32"),T=1/(h*m),C=n.data.get(r.dataId).values,F=Le(r.shape,"float32",C);for(let $=0;$<u.batchSize;++$)for(let P=0;P<u.inChannels;++P)for(let B=0;B<u.inHeight;++B)for(let W=0;W<u.inWidth;++W){let G=B-x,q=W-v,j=0;for(let X=0;X<y;X+=f){let K=(G+X)/p;if(!(K<0||K>=u.outHeight||Math.floor(K)!==K))for(let ne=0;ne<b;ne+=g){let J=(q+ne)/d;J<0||J>=u.outWidth||Math.floor(J)!==J||(j+=F.get($,K,J,P))}}N.set(j*T,$,B,W,P)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}var $G={kernelName:kd,backendName:"cpu",kernelFunc:FG};function DG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;k.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),we([r,o,l,s,i],"batchNorm");let{varianceEpsilon:c}=a;c==null&&(c=.001);let u=n.data.get(r.dataId).values,p=n.data.get(o.dataId).values,d=n.data.get(l.dataId).values,h=s?n.data.get(s.dataId).values:new Float32Array([1]),m=i?n.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(u.length),g=m.length,y=h.length,b=d.length,v=p.length,x=0,N=0,T=0,C=0;for(let F=0;F<u.length;++F)f[F]=m[x++]+(u[F]-p[N++])*h[T++]/Math.sqrt(d[C++]+c),x>=g&&(x=0),N>=v&&(N=0),T>=y&&(T=0),C>=b&&(C=0);return n.makeTensorInfo(r.shape,r.dtype,f)}var RG={kernelName:Ws,backendName:"cpu",kernelFunc:DG};function MG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;we([r],"batchToSpaceND");let o=s.reduce((y,b)=>y*b),l=_.getReshaped(r.shape,s,o),c=_.getPermuted(l.length,s.length),u=_.getReshapedPermuted(r.shape,s,o),p=_.getSliceBeginCoords(i,s.length),d=_.getSliceSize(u,i,s.length),h=It({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ya({inputs:{x:h},backend:n,attrs:{perm:c}}),f=It({inputs:{x:m},backend:n,attrs:{shape:u}}),g=Gi({inputs:{x:f},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var PG={kernelName:uc,backendName:"cpu",kernelFunc:MG};function OG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,c=xv(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var LG={kernelName:Nd,backendName:"cpu",kernelFunc:OG},zG=it(Br,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),BG={kernelName:Br,backendName:"cpu",kernelFunc:zG},WG=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.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],p=l[c];a[c]=Math.hypot(u,p)}return n.makeOutput(a,t.shape,"float32")},VG={kernelName:cc,backendName:"cpu",kernelFunc:WG};function au(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.imag,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var UG={kernelName:Ld,backendName:"cpu",kernelFunc:au};function ru(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=_.computeOutShape(t.map(f=>f.shape),s);if(k.sizeFromShape(i)===0)return 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c.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var GG={kernelName:Po,backendName:"cpu",kernelFunc:ru};function TT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=a;we([r,s],"conv2d");let p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!1,p),h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.padInfo.left,b=d.padInfo.top,v=d.dataFormat==="channelsLast",x=new Ot(d.outShape,r.dtype),N=k.computeStrides(r.shape),T=k.computeStrides(s.shape),C=N[0],F=v?N[1]:N[2],$=v?N[2]:1,P=v?1:N[1],B=x.strides[0],W=v?x.strides[1]:x.strides[2],G=v?x.strides[2]:1,q=v?1:x.strides[1],j=n.data.get(r.dataId).values,X=n.data.get(s.dataId).values,K=x.values;for(let ne=0;ne<d.batchSize;++ne){let J=ne*C,se=ne*B;for(let Q=0;Q<d.outHeight;++Q){let oe=se+Q*W,ae=Q*d.strideHeight-b;for(let pe=0;pe<h;++pe){let ue=ae+pe*f;if(ue<0||ue>=d.inHeight)continue;let ge=pe*T[0],ye=J+ue*F;for(let 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XG={kernelName:Ds,backendName:"cpu",kernelFunc:KG};function YG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;we([r,s],"conv3d");let c=_.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=c,y=g.front,b=g.left,v=g.top,x=new Ot(c.outShape,r.dtype),N=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,C=x.values,F=k.computeStrides(r.shape),$=k.computeStrides(s.shape);for(let P=0;P<c.batchSize;++P){let B=P*F[0],W=P*x.strides[0];for(let G=0;G<c.outDepth;++G){let q=W+G*x.strides[1],j=G*c.strideDepth-y;for(let X=0;X<u;++X){let K=j+X*h;if(K<0||K>=c.inDepth)continue;let ne=X*$[0],J=B+K*F[1];for(let se=0;se<c.outHeight;++se){let Q=q+se*x.strides[2],oe=se*c.strideHeight-v;for(let ae=0;ae<p;++ae){let pe=oe+ae*m;if(pe<0||pe>=c.inHeight)continue;let ue=ne+ae*$[1],ge=J+pe*F[2];for(let ye=0;ye<c.outWidth;++ye){let ke=Q+ye*c.outChannels,Se=ye*c.strideWidth-b;for(let 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ue=0;ue<g;++ue){let ge=Math.max(0,Math.ceil((se-ue)/h)),ye=Math.min(p.outHeight,(p.inHeight+se-ue)/h),ke=ue*N+pe;for(let Se=0;Se<y;++Se){let Ce=Math.max(0,Math.ceil((J-Se)/m)),Re=Math.min(p.outWidth,(p.inWidth+J-Se)/m),Ke=Se*T+ke;for(let Ve=0;Ve<p.inChannels;++Ve){let rt=Ve*C+Ke;for(let ot=0;ot<p.outChannels;++ot){let He=0;for(let ct=0;ct<p.batchSize;++ct){let pt=ct*q,Gn=ct*$;for(let Je=oe;Je<ae;++Je){let En=(Q+Je*d-ne)*j+pt,Qt=Je*P+Gn;for(let An=ge;An<ye;++An){let na=(ue+An*h-se)*X+En,xn=An*B+Qt;for(let ln=Ce;ln<Re;++ln){let aa=(Se+ln*m-J)*K+na,Ba=ln*W+xn;He+=G[aa+Ve]*F[Ba+ot]}}}}v[rt+ot]=He}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var QG={kernelName:Cd,backendName:"cpu",kernelFunc:JG};function eH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a;we([r],"conv3dBackpropInputV2");let c=k.computeStrides(r.shape),u=k.computeStrides(s.shape),p=_.computeConv3DInfo(l,s.shape,o,1,i),d=new Ot(p.inShape,"float32"),h=d.values,[m,f,g,y]=d.strides,b=n.data.get(r.dataId).values,[v,x,N,T]=c,C=n.data.get(s.dataId).values,[F,$,P,B]=u,{batchSize:W,filterDepth:G,filterHeight:q,filterWidth:j,inChannels:X,inDepth:K,inHeight:ne,inWidth:J,outChannels:se,outDepth:Q,outHeight:oe,outWidth:ae,strideDepth:pe,strideHeight:ue,strideWidth:ge}=p,ye=G-1-p.padInfo.front,ke=q-1-p.padInfo.top,Se=j-1-p.padInfo.left;for(let Ce=0;Ce<W;++Ce)for(let Re=0;Re<X;++Re)for(let Ke=0;Ke<K;++Ke){let Ve=Ke-ye,rt=Math.max(0,Math.ceil(Ve/pe)),ot=Math.min(Q,(G+Ve)/pe);for(let He=0;He<ne;++He){let ct=He-ke,pt=Math.max(0,Math.ceil(ct/ue)),Gn=Math.min(oe,(q+ct)/ue);for(let Je=0;Je<J;++Je){let En=Je-Se,Qt=Math.max(0,Math.ceil(En/ge)),An=Math.min(ae,(j+En)/ge),na=0;for(let xn=rt;xn<ot;++xn){let ln=xn*pe-Ve;for(let aa=pt;aa<Gn;++aa){let Ba=aa*ue-ct;for(let Fn=Qt;Fn<An;++Fn){let po=Fn*ge-En,zu=v*Ce+x*xn+N*aa+T*Fn,ka=F*(G-1-ln)+$*(q-1-Ba)+P*(j-1-po)+B*Re;for(let ra=0;ra<se;++ra){let Ia=b[zu+ra],ho=C[ka+ra];na+=Ia*ho}}}}h[m*Ce+f*Ke+g*He+y*Je+Re]=na}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var tH={kernelName:_d,backendName:"cpu",kernelFunc:eH},nH=it(Rs,e=>Math.cos(e)),aH={kernelName:Rs,backendName:"cpu",kernelFunc:nH},rH=it(Oo,e=>Math.cosh(e)),sH={kernelName:Oo,backendName:"cpu",kernelFunc:rH};function iH(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=a,[u,p,d,h]=r.shape,m=s.shape[0],[f,g]=o,y=Le([m,f,g,h],"float32"),b=n.data.get(s.dataId).values,v=n.data.get(i.dataId).values,x=n.data.get(r.dataId).values,N=k.computeStrides(r.shape),T=k.computeStrides(y.shape);for(let C=0;C<m;C++){let F=C*4,$=b[F],P=b[F+1],B=b[F+2],W=b[F+3],G=v[C];if(G>=u)continue;let q=f>1?(B-$)*(p-1)/(f-1):0,j=g>1?(W-P)*(d-1)/(g-1):0;for(let X=0;X<f;X++){let K=f>1?$*(p-1)+X*q:.5*($+B)*(p-1);if(K<0||K>p-1){for(let ne=0;ne<g;ne++)for(let J=0;J<h;J++){let se=J+ne*T[2]+X*T[1]+C*T[0];y.values[se]=c}continue}if(l==="bilinear"){let ne=Math.floor(K),J=Math.ceil(K),se=K-ne;for(let Q=0;Q<g;Q++){let oe=g>1?P*(d-1)+Q*j:.5*(P+W)*(d-1);if(oe<0||oe>d-1){for(let ge=0;ge<h;ge++){let ye=ge+Q*T[2]+X*T[1]+C*T[0];y.values[ye]=c}continue}let ae=Math.floor(oe),pe=Math.ceil(oe),ue=oe-ae;for(let ge=0;ge<h;ge++){let ye=ge+ae*N[2]+ne*N[1]+G*N[0],ke=x[ye];ye=ge+pe*N[2]+ne*N[1]+G*N[0];let Se=x[ye];ye=ge+ae*N[2]+J*N[1]+G*N[0];let Ce=x[ye];ye=ge+pe*N[2]+J*N[1]+G*N[0];let Re=x[ye],Ke=ke+(Se-ke)*ue,Ve=Ce+(Re-Ce)*ue;ye=ge+Q*T[2]+X*T[1]+C*T[0],y.values[ye]=Ke+(Ve-Ke)*se}}}else for(let ne=0;ne<g;++ne){let J=g>1?P*(d-1)+ne*j:.5*(P+W)*(d-1);if(J<0||J>d-1){for(let oe=0;oe<h;oe++){let ae=oe+ne*T[2]+X*T[1]+C*T[0];y.values[ae]=c}continue}let se=Math.round(J),Q=Math.round(K);for(let oe=0;oe<h;oe++){let ae=oe+se*N[2]+Q*N[1]+G*N[0],pe=oe+ne*T[2]+X*T[1]+C*T[0];y.values[pe]=x[ae]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var oH={kernelName:Lo,backendName:"cpu",kernelFunc:iH};function lH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;we(r,"cumsum");let l=_.getAxesPermutation([s],r.shape.length),c=r;l!=null&&(c=ya({inputs:{x:r},backend:n,attrs:{perm:l}}));let u=_.getInnerMostAxes(1,r.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let p=ua(c.dtype,"int32"),d=k.makeZerosTypedArray(k.sizeFromShape(c.shape),p),h=n.data.get(c.dataId).values,m=c.shape[c.shape.length-1],f=o?(y,b)=>y+m-b-1:(y,b)=>y+b;for(let y=0;y<h.length;y+=m)for(let b=0;b<m;b++){let v=f(y,b);if(b===0)d[v]=i?0:h[v];else{let x=f(y,b-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(c.shape,p,d);if(l!=null){let y=_.getUndoAxesPermutation(l),b=ya({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(c),b}return g}var uH={kernelName:Ms,backendName:"cpu",kernelFunc:lH};function cH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.data.get(r.dataId).values,c=n.data.get(s.dataId).values,u=xv(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=qN(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${r.shape.length}.`)}var pH={kernelName:Ed,backendName:"cpu",kernelFunc:cH};function dH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=r.shape[1],c=r.shape[2],u=r.shape[3],p=l*s,d=c*s,h=u/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*p*d*h),g=0;for(let y=0;y<o;++y)for(let b=0;b<p;++b){let v=Math.floor(b/s),x=b%s;for(let N=0;N<d;++N){let T=Math.floor(N/s),C=N%s,F=(x*s+C)*h;for(let $=0;$<h;++$){let P=$+F+u*(T+c*(v+l*y));f[g++]=m[P]}}}return n.makeTensorInfo([o,p,d,h],r.dtype,f)}var hH={kernelName:zo,backendName:"cpu",kernelFunc:dH};function ST(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=a;we([r,s],"depthwiseConv2DNative");let u=k.computeStrides(r.shape),p=k.computeStrides(s.shape),d=l;d==null&&(d=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=_.computeConv2DInfo(r.shape,s.shape,i,d,o,c,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:b}=h,v=b.left,x=b.top,N=h.outChannels/h.inChannels,T=new Ot(h.outShape,r.dtype),C=n.data.get(r.dataId).values,F=n.data.get(s.dataId).values,$=T.values;for(let P=0;P<h.batchSize;++P){let B=P*u[0],W=P*T.strides[0];for(let G=0;G<h.outHeight;++G){let q=W+G*T.strides[1],j=G*h.strideHeight-v;for(let X=0;X<m;++X){let K=j+X*g;if(K<0||K>=h.inHeight)continue;let ne=X*p[0],J=B+K*u[1];for(let se=0;se<h.outWidth;++se){let Q=q+se*T.strides[2],oe=se*h.strideWidth-x;for(let ae=0;ae<f;++ae){let pe=oe+ae*y;if(pe<0||pe>=h.inWidth)continue;let ue=ne+ae*p[1],ge=J+pe*h.inChannels,ye=Q,ke=ue;for(let Se=0;Se<h.inChannels;++Se){let Ce=C[ge+Se];for(let Re=0;Re<N;++Re)$[ye+Re]+=Ce*F[ke+Re];ye+=N,ke+=N}}}}}}return n.makeTensorInfo(T.shape,T.dtype,T.values)}var mH={kernelName:Ps,backendName:"cpu",kernelFunc:ST};function fH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=a;we([r,s],"depthwiseConv2dNativeBackpropFilter");let p=_.computeConv2DInfo(r.shape,u,i,o,l,c,!0),{strideHeight:d,strideWidth:h,filterHeight:m,filterWidth:f}=p,g=new Ot(p.filterShape,"float32"),y=p.padInfo.left,b=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,N=new Ot(r.shape,r.dtype,x),T=n.data.get(s.dataId).values,C=new Ot(s.shape,s.dtype,T);for(let F=0;F<m;++F){let $=Math.max(0,Math.ceil((b-F)/d)),P=Math.min(p.outHeight,(p.inHeight+b-F)/d);for(let B=0;B<f;++B){let W=Math.max(0,Math.ceil((y-B)/h)),G=Math.min(p.outWidth,(p.inWidth+y-B)/h);for(let q=0;q<p.outChannels;++q){let j=Math.trunc(q/v),X=q%v,K=0;for(let ne=0;ne<p.batchSize;++ne)for(let J=$;J<P;++J){let se=F+J*d-b;for(let Q=W;Q<G;++Q){let oe=B+Q*h-y;K+=N.get(ne,se,oe,j)*C.get(ne,J,Q,q)}}g.set(K,F,B,j,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var gH={kernelName:Ad,backendName:"cpu",kernelFunc:fH};function yH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=a;we([r,s],"depthwiseConv2DNativeBackpropInput");let p=k.computeStrides(r.shape),d=k.computeStrides(s.shape),h=_.computeConv2DInfo(u,s.shape,i,o,l,c,!0),m=new Ot(h.inShape,"float32"),f=m.values,[g,y,b]=m.strides,v=n.data.get(r.dataId).values,[x,N,T]=p,C=n.data.get(s.dataId).values,[F,$,P]=d,{batchSize:B,filterHeight:W,filterWidth:G,inChannels:q,inHeight:j,inWidth:X,outChannels:K,outHeight:ne,outWidth:J,strideHeight:se,strideWidth:Q}=h,oe=W-1-h.padInfo.top,ae=G-1-h.padInfo.left,pe=K/q;for(let ue=0;ue<B;++ue)for(let ge=0;ge<q;++ge)for(let ye=0;ye<j;++ye){let ke=ye-oe,Se=Math.max(0,Math.ceil(ke/se)),Ce=Math.min(ne,(W+ke)/se);for(let Re=0;Re<X;++Re){let Ke=Re-ae,Ve=Math.max(0,Math.ceil(Ke/Q)),rt=Math.min(J,(G+Ke)/Q),ot=0;for(let He=Se;He<Ce;++He){let ct=He*se-ke;for(let pt=Ve;pt<rt;++pt){let Gn=pt*Q-Ke,Je=x*ue+N*He+T*pt,En=F*(W-1-ct)+$*(G-1-Gn)+P*ge;for(let Qt=0;Qt<pe;++Qt){let An=ge*pe+Qt,na=v[Je+An],xn=C[En+Qt];ot+=na*xn}}}f[g*ue+y*ye+b*Re+ge]=ot}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var bH={kernelName:Fd,backendName:"cpu",kernelFunc:yH};function xH(e){let{inputs:t,backend:n}=e,{x:a}=t,r=k.sizeFromShape(a.shape),s=n.data.get(a.dataId).values,i=Le([r,r],a.dtype),o=i.values;for(let c=0;c<s.length;c++)o[c*r+c]=s[c];let l=[...a.shape,...a.shape];return n.makeTensorInfo(l,i.dtype,i.values)}var vH={kernelName:$d,backendName:"cpu",kernelFunc:xH},wH={kernelName:dc,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,c=l.data.get(a.dataId).values,u=a.shape.length,p=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:b,padInfo:v,strideHeight:x,strideWidth:N,filterHeight:T,filterWidth:C,dilationHeight:F,dilationWidth:$,outShape:P}=_.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),B=k.sizeFromShape(P),W=P.length,G=k.getArrayFromDType(a.dtype,B);for(let q=0;q<h;++q)for(let j=0;j<y;++j){let X=j*x-v.top;for(let K=0;K<b;++K){let ne=K*N-v.left;for(let J=0;J<g;++J){let se=Number.MIN_SAFE_INTEGER;for(let oe=0;oe<T;++oe){let ae=X+oe*F;if(ae>=0&&ae<m)for(let pe=0;pe<C;++pe){let ue=ne+pe*$;if(ue>=0&&ue<f){let ge=k.locToIndex([q,ae,ue,J],u,k.computeStrides(a.shape)),ye=k.locToIndex([oe,pe,J],d,k.computeStrides(r.shape)),ke=c[ge]+p[ye];ke>se&&(se=ke)}}}let Q=k.locToIndex([q,j,K,J],W,k.computeStrides(P));G[Q]=se}}}return{dataId:l.write(k.toTypedArray(G,a.dtype),P,a.dtype),shape:P,dtype:a.dtype}}},kH={kernelName:Rd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=k.toNestedArray(a.shape,c.data.get(a.dataId).values),p=k.toNestedArray(r.shape,c.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:v,strideWidth:x,filterHeight:N,filterWidth:T,dilationHeight:C,dilationWidth:F,outShape:$}=_.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===$.length,()=>`Error in ${Rd}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let P=k.toNestedArray($,c.data.get(s.dataId).values),B=k.makeZerosNestedTypedArray(r.shape,r.dtype);for(let W=0;W<d;++W)for(let G=0;G<g;++G){let q=G*v-b.top;for(let j=0;j<y;++j){let X=j*x-b.left;for(let K=0;K<f;++K){let ne=Number.MIN_SAFE_INTEGER,J=0,se=0;for(let Q=0;Q<N;++Q){let oe=q+Q*C;if(oe>=0&&oe<h)for(let ae=0;ae<T;++ae){let pe=X+ae*F;if(pe>=0&&pe<m){let ue=u[W][oe][pe][K]+p[Q][ae][K];ue>ne&&(ne=ue,J=Q,se=ae)}}}B[J][se][K]+=P[W][G][j][K]}}}return{dataId:c.write(k.toTypedArray(B,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},IH={kernelName:Dd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=k.toNestedArray(a.shape,c.data.get(a.dataId).values),p=k.toNestedArray(r.shape,c.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:v,strideWidth:x,filterHeight:N,filterWidth:T,dilationHeight:C,dilationWidth:F,outShape:$}=_.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===$.length,()=>`Error in ${Dd}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let P=k.toNestedArray($,c.data.get(s.dataId).values),B=k.makeZerosNestedTypedArray(a.shape,a.dtype);for(let W=0;W<d;++W)for(let G=0;G<g;++G){let q=G*v-b.top;for(let j=0;j<y;++j){let X=j*x-b.left;for(let K=0;K<f;++K){let ne=Number.MIN_SAFE_INTEGER,J=q<0?0:q,se=X<0?0:X;for(let Q=0;Q<N;++Q){let oe=q+Q*C;if(oe>=0&&oe<h)for(let ae=0;ae<T;++ae){let pe=X+ae*F;if(pe>=0&&pe<m){let ue=u[W][oe][pe][K]+p[Q][ae][K];ue>ne&&(ne=ue,J=oe,se=pe)}}}B[W][J][se][K]+=P[W][G][j][K]}}}return{dataId:c.write(k.toTypedArray(B,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function NH(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;we([a,r],"eluGrad");let s=new Float32Array(k.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l<i.length;++l){let c=i[l];c>=1?s[l]=o[l]:s[l]=o[l]*(c+1)}return n.makeTensorInfo(r.shape,"float32",s)}var 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Q=v*x-1-m.get(P,ne,se,B),oe=K*x+J,ae=Q===oe?1:0;ae!==0&&(X+=$.get(P,ne,se,B)*ae)}}C.set(X,P,W,G,B)}return n.makeTensorInfo(C.shape,C.dtype,C.values)}var W6={kernelName:Wd,backendName:"cpu",kernelFunc:B6};function V6(e,t,n,a,r){let s=k.computeStrides(t),i=_v(e,t,n,s,r,"max"),o=IT(e,t,n,r,!0,a);return[i.values,o.values]}var U6={kernelName:Ud,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;we(a,"MaxPoolWithArgmax");let c=l.data.get(a.dataId).values,u=_.computePool2DInfo(a.shape,r,s,[1,1],i),[p,d]=V6(c,a.shape,a.dtype,o,u),h=l.write(p,u.outShape,a.dtype),m=l.write(d,u.outShape,a.dtype);return[{dataId:h,shape:u.outShape,dtype:a.dtype},{dataId:m,shape:u.outShape,dtype:"int32"}]}};function Pm(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;we(r,"sum");let o;r.dtype==="bool"?o=us({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=rr({inputs:{x:r},backend:n});let 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       bool isnan_custom(float val) {
         return (val > 0.0 || val < 0.0) ? false : val != 0.0;
       }
@@ -86,11 +86,11 @@ 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:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function Gi(e,t,n="index"){let a=k.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / ${r}`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function Pv(e){let t=k.computeStrides(e).map(n=>n.toString());return`
+    `),{version:e,attribute:t,varyingVs:n,varyingFs:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function ji(e,t,n="index"){let a=k.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / ${r}`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function Wv(e){let t=k.computeStrides(e).map(n=>n.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
-`}var DT=`
+`}var zT=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -129,9 +129,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
-`,u5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=gp.DENSE;let t=bp(e),n=hn();this.outputShape=e,this.userCode=`
+`,h5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=yp.DENSE;let t=xp(e),n=hn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${Gi(["r","c","d"],e)}
+        ${ji(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -150,9 +150,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = result;
       }
-    `}},c5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=gp.DENSE;let t=bp(e),n=hn();this.outputShape=e,this.userCode=`
+    `}},m5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=yp.DENSE;let t=xp(e),n=hn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${Gi(["r","c","d"],e)}
+        ${ji(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -171,23 +171,23 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = result;
       }
-    `}},p5=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Qn.DOWNLOAD;let t=hn();this.outputShape=e,this.userCode=`
-      ${DT}
+    `}},f5=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Qn.DOWNLOAD;let t=hn();this.outputShape=e,this.userCode=`
+      ${zT}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
-    `}},d5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Qn.DOWNLOAD;let t=hn();this.outputShape=e,this.userCode=`
-      ${DT}
+    `}},g5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Qn.DOWNLOAD;let t=hn();this.outputShape=e,this.userCode=`
+      ${zT}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
         ${t.output} = encode_float(x);
       }
-    `}},h5=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=hn(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
-      ${Pv(e)}
+    `}},y5=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=hn(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
+      ${Wv(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -216,7 +216,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${a.output} = vec4(${i}, 0., 0., 0.);
       }
-    `}},m5=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=hn(),[r,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+=`
+    `}},b5=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=hn(),[r,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] + ${c} < ${e[2]}) {
             localCoords[2] += ${c};
@@ -245,7 +245,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
             }
           }
         `}this.userCode=`
-      ${Pv(e)}
+      ${Wv(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -260,7 +260,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${a.output} = ${o};
       }
-    `}};function f5(e){let t=hn(),n=`${t.version}
+    `}};function x5(e){let t=hn(),n=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -269,22 +269,22 @@ 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 Lq(e,n)}function g5(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 Gq(e,t)}function y5(e){let t=new Uint16Array([0,1,2,2,1,3]);return Hq(e,t)}function vp(e,t,n,a,r,s){qq(t,n);let i=jq(e),o=e.TEXTURE_2D;return Ne(e,()=>e.bindTexture(o,i)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ne(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),Ne(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function RT(e){return e.internalFormatFloat}function b5(e,t,n,a){let[r,s]=yp(t,n);return vp(e,r,s,RT(a),a.textureFormatFloat,e.FLOAT)}function MT(e){return e.internalFormatHalfFloat}function x5(e,t,n,a){let[r,s]=yp(t,n);return vp(e,r,s,MT(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function PT(e){return e.downloadTextureFormat}function v5(e,t,n,a){let[r,s]=yp(t,n);return vp(e,r,s,PT(a),e.RGBA,e.UNSIGNED_BYTE)}function OT(e){return e.internalFormatPackedFloat}function w5(e,t,n,a){let[r,s]=su(t,n);return vp(e,r,s,OT(a),e.RGBA,e.FLOAT)}function LT(e){return e.internalFormatPackedHalfFloat}function k5(e,t,n,a){let[r,s]=su(t,n);return vp(e,r,s,LT(a),e.RGBA,a.textureTypeHalfFloat)}function I5(e,t,n){let a=0,r=3*4,s=3*4+2*4;return Ne(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),AT(e,t,"clipSpacePos",n,3,s,a)&&AT(e,t,"uv",n,2,s,r)}function N5(e,t,n,a,r,s){Ne(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),Ne(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),Ne(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function T5(e,t,n){Ne(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Ne(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ne(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ne(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function S5(e,t,n,a){let r=e.createBuffer();Ne(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return Ne(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),Ne(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ne(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function C5(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function _5(e,t,n,a){let[r,s]=yp(t,n),i=4,o=new Uint8Array(Fq(t*n,i));return Ne(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function E5(e,t,n,a,r,s,i,o){let l=e,c=new Float32Array($q(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 A5(e,t,n){let a=new Float32Array(t*n*4);return Ne(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var $5=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=ee().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,_q(t,e)):this.gl=rr(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(ee().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Mm(this.gl,r),ya(this.gl,s))this.textureHalfFloatExtension=Mm(this.gl,s);else if(ee().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),ya(this.gl,a))this.colorBufferHalfFloatExtension=Mm(this.gl,a);else if(ee().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",ya(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(ya(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=g5(this.gl),this.indexBuffer=y5(this.gl),this.framebuffer=Kq(this.gl),this.textureConfig=Ev(this.gl,this.textureHalfFloatExtension)}get debug(){return ee().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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a=this.gl;Fv(a,e,this.framebuffer),this.debug&&Pm(a),this.outputTexture=e,Ne(a,()=>a.viewport(0,0,t,n)),Ne(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),Ne(this.gl,()=>this.gl.scissor(e,t,n,a))}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 F5(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:zT}=_;function W5(e,t,n,a){let r=[];e.forEach(h=>{let m=k.sizeFromShape(h.shapeInfo.logicalShape);h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${m>1?`[${m}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`))});let s=r.join(`
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-`),o=t.texShape,l=hn(),c=P5(l),u,p,d=z5(l);return t.isPacked?(u=R5(t.logicalShape,o),p=L5(l)):(u=M5(t.logicalShape,o),p=O5(l)),a&&(d+=B5),[d,c,p,s,u,i,n].join(`
-`)}function lu(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return V5(e);case 1:return U5(e);case 2:return G5(e);case 3:return H5(e);case 4:return j5(e);case 5:return q5(e);case 6:return K5(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function BT(e){switch(e.shapeInfo.logicalShape.length){case 0:return X5(e);case 1:return Y5(e);case 2:return Z5(e);case 3:return J5(e);default:return Q5(e)}}function D5(e,t,n=!1){let a="";n?a+=BT(e):a+=lu(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=e8(e,t):a+=t8(e,t)),a}function R5(e,t){switch(e.length){case 0:return WT();case 1:return n8(e,t);case 2:return s8(e,t);case 3:return a8(e,t);default:return r8(e,t)}}function M5(e,t){switch(e.length){case 0:return WT();case 1:return i8(e,t);case 2:return p8(e,t);case 3:return o8(e,t);case 4:return l8(e,t);case 5:return u8(e,t);case 6:return c8(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function P5(e){return`
+    }`;return Vq(e,n)}function v5(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 Kq(e,t)}function w5(e){let t=new Uint16Array([0,1,2,2,1,3]);return Xq(e,t)}function wp(e,t,n,a,r,s){Zq(t,n);let i=Yq(e),o=e.TEXTURE_2D;return Ne(e,()=>e.bindTexture(o,i)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ne(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),Ne(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function BT(e){return e.internalFormatFloat}function k5(e,t,n,a){let[r,s]=bp(t,n);return wp(e,r,s,BT(a),a.textureFormatFloat,e.FLOAT)}function WT(e){return e.internalFormatHalfFloat}function I5(e,t,n,a){let[r,s]=bp(t,n);return wp(e,r,s,WT(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function VT(e){return e.downloadTextureFormat}function N5(e,t,n,a){let[r,s]=bp(t,n);return wp(e,r,s,VT(a),e.RGBA,e.UNSIGNED_BYTE)}function UT(e){return e.internalFormatPackedFloat}function T5(e,t,n,a){let[r,s]=su(t,n);return wp(e,r,s,UT(a),e.RGBA,e.FLOAT)}function GT(e){return e.internalFormatPackedHalfFloat}function S5(e,t,n,a){let[r,s]=su(t,n);return wp(e,r,s,GT(a),e.RGBA,a.textureTypeHalfFloat)}function C5(e,t,n){let a=0,r=3*4,s=3*4+2*4;return Ne(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),PT(e,t,"clipSpacePos",n,3,s,a)&&PT(e,t,"uv",n,2,s,r)}function _5(e,t,n,a,r,s){Ne(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),Ne(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),Ne(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function E5(e,t,n){Ne(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Ne(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ne(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ne(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function A5(e,t,n,a){let r=e.createBuffer();Ne(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return Ne(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),Ne(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ne(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function F5(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function $5(e,t,n,a){let[r,s]=bp(t,n),i=4,o=new Uint8Array(Mq(t*n,i));return Ne(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function D5(e,t,n,a,r,s,i,o){let l=e,c=new Float32Array(Pq(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function R5(e,t,n){let a=new Float32Array(t*n*4);return Ne(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var P5=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=ee().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,$q(t,e)):this.gl=sr(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(ee().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Lm(this.gl,r),ba(this.gl,s))this.textureHalfFloatExtension=Lm(this.gl,s);else if(ee().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),ba(this.gl,a))this.colorBufferHalfFloatExtension=Lm(this.gl,a);else if(ee().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",ba(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(ba(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=v5(this.gl),this.indexBuffer=w5(this.gl),this.framebuffer=Jq(this.gl),this.textureConfig=Rv(this.gl,this.textureHalfFloatExtension)}get debug(){return ee().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;Ne(e,()=>e.finish()),Ne(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ne(e,()=>e.deleteFramebuffer(this.framebuffer)),Ne(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ne(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ne(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),k5(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),I5(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),N5(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),E5(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),_5(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),S5(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),T5(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(OT(this.gl,this.framebuffer),this.outputTexture=null),Ne(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>$5(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return D5(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return F5(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=A5(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(ee().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=a.clientWaitSync(r,0,0);return s===a.ALREADY_SIGNALED||s===a.CONDITION_SATISFIED},t=r}else ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>R5(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=Gq(t,e),a=x5(t),r=jq(t);return Ne(t,()=>t.attachShader(r,a)),Ne(t,()=>t.attachShader(r,n)),qq(t,r),this.debug&&Mv(t,r),this.vertexAttrsAreBound||(this.setProgram(r),this.vertexAttrsAreBound=C5(t,this.program,this.vertexBuffer)),r}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ne(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Mv(this.gl,this.program),Ne(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?t5(this.gl,e,t):n5(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ne(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(),a5(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=su(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Mv(this.gl,this.program),zm(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ne(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ne(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Lm(this.gl,ee().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(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(ee().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 k.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,ee().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,a=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),a=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=M5(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)&&k.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Pv(this.gl,e,this.framebuffer),this.debug&&zm(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Pv(this.gl,this.outputTexture,this.framebuffer),this.debug&&zm(this.gl)):OT(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;Pv(a,e,this.framebuffer),this.debug&&zm(a),this.outputTexture=e,Ne(a,()=>a.viewport(0,0,t,n)),Ne(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),Ne(this.gl,()=>this.gl.scissor(e,t,n,a))}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 M5(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:HT}=_;function H5(e,t,n,a){let r=[];e.forEach(h=>{let m=k.sizeFromShape(h.shapeInfo.logicalShape);h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${m>1?`[${m}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`))});let s=r.join(`
+`),i=e.map(h=>O5(h,t,a)).join(`
+`),o=t.texShape,l=hn(),c=B5(l),u,p,d=U5(l);return t.isPacked?(u=L5(t.logicalShape,o),p=V5(l)):(u=z5(t.logicalShape,o),p=W5(l)),a&&(d+=G5),[d,c,p,s,u,i,n].join(`
+`)}function lu(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return j5(e);case 1:return q5(e);case 2:return K5(e);case 3:return X5(e);case 4:return Y5(e);case 5:return Z5(e);case 6:return J5(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function jT(e){switch(e.shapeInfo.logicalShape.length){case 0:return Q5(e);case 1:return e8(e);case 2:return t8(e);case 3:return n8(e);default:return a8(e)}}function O5(e,t,n=!1){let a="";n?a+=jT(e):a+=lu(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=r8(e,t):a+=s8(e,t)),a}function L5(e,t){switch(e.length){case 0:return qT();case 1:return i8(e,t);case 2:return u8(e,t);case 3:return o8(e,t);default:return l8(e,t)}}function z5(e,t){switch(e.length){case 0:return qT();case 1:return c8(e,t);case 2:return f8(e,t);case 3:return p8(e,t);case 4:return d8(e,t);case 5:return h8(e,t);case 6:return m8(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function B5(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
-  `}function O5(e){return`
+  `}function W5(e){return`
     void setOutput(float val) {
       ${e.output} = vec4(val, 0, 0, 0);
     }
-  `}function L5(e){return`
+  `}function V5(e){return`
     void setOutput(vec4 val) {
       ${e.output} = val;
     }
-  `}function z5(e){return`${e.version}
+  `}function U5(e){return`${e.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
@@ -339,10 +339,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       return fract((p3.x + p3.y) * p3.z);
     }
 
-    ${d8}
-    ${h8}
-    ${m8}
-  `}var d8=`
+    ${g8}
+    ${y8}
+    ${b8}
+  `}var g8=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -354,7 +354,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,h8=`
+`,y8=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -362,7 +362,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,m8=`
+`,b8=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -371,7 +371,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,B5=`
+`,G5=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -382,11 +382,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function WT(){return`
+`;function qT(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function n8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
+  `}function i8(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 * ${n[1]}.0);
       }
@@ -400,7 +400,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${n[0]}, ${n[1]}));
       return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
     }
-  `}function i8(e,t){return t[0]===1?`
+  `}function c8(e,t){return t[0]===1?`
       int getOutputCoords() {
         return int(resultUV.x * ${t[1]}.0);
       }
@@ -414,7 +414,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${t[0]}, ${t[1]}));
       return resTexRC.x * ${t[1]} + resTexRC.y;
     }
-  `}function a8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
+  `}function o8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${n[0]}, ${n[1]}));
@@ -428,7 +428,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function o8(e,t){let n=Gi(["r","c","d"],e);return`
+  `}function p8(e,t){let n=ji(["r","c","d"],e);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -436,7 +436,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec3(r, c, d);
     }
-  `}function r8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
+  `}function l8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
       int b${l} = index / ${s};
       index -= b${l} * ${s};
     `+i,o=`b${l}, `+o;return`
@@ -455,7 +455,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${e.length}(${o});
     }
-  `}function l8(e,t){let n=Gi(["r","c","d","d2"],e);return`
+  `}function d8(e,t){let n=ji(["r","c","d","d2"],e);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -463,7 +463,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec4(r, c, d, d2);
     }
-  `}function u8(e,t){let n=Gi(["r","c","d","d2","d3"],e);return`
+  `}function h8(e,t){let n=ji(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -475,7 +475,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function c8(e,t){let n=Gi(["r","c","d","d2","d3","d4"],e);return`
+  `}function m8(e,t){let n=ji(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -486,7 +486,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function s8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
+  `}function u8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
       ivec2 getOutputCoords() {
         return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
       }
@@ -501,7 +501,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec2(r, c);
     }
-  `}function p8(e,t){return k.arraysEqual(e,t)?`
+  `}function f8(e,t){return k.arraysEqual(e,t)?`
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
       }
@@ -528,26 +528,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function Hi(e){return`offset${e}`}function X5(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=hn();return`
+  `}function qi(e){return`offset${e}`}function Q5(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=hn();return`
     vec4 ${n}() {
       return ${a.texture2D}(${t}, halfCR);
     }
-  `}function V5(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[a,r]=e.shapeInfo.texShape;if(a===1&&r===1)return`
+  `}function j5(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[a,r]=e.shapeInfo.texShape;if(a===1&&r===1)return`
       float ${n}() {
         return sampleTexture(${t}, halfCR);
       }
-    `;let[s,i]=e.shapeInfo.texShape,o=Hi(t);return`
+    `;let[s,i]=e.shapeInfo.texShape,o=qi(t);return`
     float ${n}() {
       vec2 uv = uvFromFlat(${s}, ${i}, ${o});
       return sampleTexture(${t}, uv);
     }
-  `}function Y5(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=e.shapeInfo.texShape,r=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=hn();return`
+  `}function e8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=e.shapeInfo.texShape,r=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=hn();return`
     vec4 ${n}(int index) {
       vec2 uv = packedUVfrom1D(
         ${r[0]}, ${r[1]}, index);
       return ${s.texture2D}(${t}, uv);
     }
-  `}function U5(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
+  `}function q5(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
       float ${n}(int index) {
         ${uu(e)}
       }
@@ -555,7 +555,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       float ${n}(int index) {
         return sampleTexture(${t}, halfCR);
       }
-    `;let i=Hi(t);return s===1?`
+    `;let i=qi(t);return s===1?`
       float ${n}(int index) {
         vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${r}.0);
         return sampleTexture(${t}, uv);
@@ -570,7 +570,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${r}, ${s}, index + ${i});
       return sampleTexture(${t}, uv);
     }
-  `}function Z5(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=r[0],i=r[1],o=hn();if(r!=null&&k.arraysEqual(t,r))return`
+  `}function t8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=r[0],i=r[1],o=hn();if(r!=null&&k.arraysEqual(t,r))return`
       vec4 ${a}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
 
@@ -581,7 +581,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
       return ${o.texture2D}(${n}, uv);
     }
-  `}function G5(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape;if(r!=null&&k.arraysEqual(t,r)){let p=r[0],d=r[1];return`
+  `}function K5(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape;if(r!=null&&k.arraysEqual(t,r)){let p=r[0],d=r[1];return`
     float ${a}(int row, int col) {
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${p}.0);
       return sampleTexture(${n}, uv);
@@ -596,7 +596,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
         ${uu(e)}
       }
-    `;let l=r[0],c=r[1],u=Hi(n);return c===1?`
+    `;let l=r[0],c=r[1],u=qi(n);return c===1?`
     float ${a}(int row, int col) {
       float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
       vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
@@ -615,8 +615,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${l}, ${c}, index);
     return sampleTexture(${n}, uv);
   }
-`}function J5(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];if(t[0]===1){let p=t.slice(1),d=[1,2],h=cu(e,p),m=["b","row","col"];return`
-        ${BT(h)}
+`}function n8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];if(t[0]===1){let p=t.slice(1),d=[1,2],h=cu(e,p),m=["b","row","col"];return`
+        ${jT(h)}
         vec4 ${a}(int b, int row, int col) {
           return ${a}(${pu(m,d)});
         }
@@ -626,7 +626,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${i}, ${o}, ${c}, ${l}, b, row, col);
       return ${u.texture2D}(${n}, uv);
     }
-  `}function H5(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=k.squeezeShape(t),l=i;if(l.length<t.length){let m=cu(e,l),f=["row","col","depth"];return`
+  `}function X5(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=k.squeezeShape(t),l=i;if(l.length<t.length){let m=cu(e,l),f=["row","col","depth"];return`
         ${lu(m)}
         float ${a}(int row, int col, int depth) {
           return ${a}(${pu(f,o)});
@@ -652,14 +652,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${u}.0);
       return sampleTexture(${n}, uv);
     }
-  `;let h=Hi(n);return`
+  `;let h=qi(n);return`
       float ${a}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
         int index = row * ${r} + col * ${s} + depth + ${h};
         vec2 uv = uvFromFlat(${u}, ${p}, index);
         return sampleTexture(${n}, uv);
       }
-  `}function Q5(e){let t=e.shapeInfo.logicalShape,n=t.length,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.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),p="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let m=2;m<n-1;m++)p=`int b${m}, `+p,u*=t[n-m-1],d=`b${m} * ${u} + `+d;let h=hn();return`
+  `}function a8(e){let t=e.shapeInfo.logicalShape,n=t.length,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.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),p="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let m=2;m<n-1;m++)p=`int b${m}, `+p,u*=t[n-m-1],d=`b${m} * ${u} + `+d;let h=hn();return`
     vec4 ${r}(${p}) {
       int index = ${d};
       int texR = index / ${l};
@@ -667,7 +667,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
       return ${h.texture2D}(${a}, uv);
     }
-  `}function j5(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[3],s=t[2]*r,i=t[1]*s,{newShape:o,keptDims:l}=k.squeezeShape(t);if(o.length<t.length){let m=cu(e,o),f=["row","col","depth","depth2"];return`
+  `}function Y5(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[3],s=t[2]*r,i=t[1]*s,{newShape:o,keptDims:l}=k.squeezeShape(t);if(o.length<t.length){let m=cu(e,o),f=["row","col","depth","depth2"];return`
       ${lu(m)}
       float ${a}(int row, int col, int depth, int depth2) {
         return ${a}(${pu(f,l)});
@@ -697,7 +697,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${d}.0, ${p}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let h=Hi(n);return`
+    `;let h=qi(n);return`
     float ${a}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${i} + col * ${s} +
@@ -705,7 +705,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${p}, ${d}, index + ${h});
       return sampleTexture(${n}, uv);
     }
-  `}function q5(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=k.squeezeShape(t);if(l.length<t.length){let f=cu(e,l),g=["row","col","depth","depth2","depth3"];return`
+  `}function Z5(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=k.squeezeShape(t);if(l.length<t.length){let f=cu(e,l),g=["row","col","depth","depth2","depth3"];return`
       ${lu(f)}
       float ${a}(int row, int col, int depth, int depth2, int depth3) {
         return ${a}(${pu(g,c)});
@@ -738,7 +738,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${h}.0, ${d}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let m=Hi(n);return`
+    `;let m=qi(n);return`
     float ${a}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${o} + col * ${i} + depth * ${s} +
@@ -746,7 +746,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${d}, ${h}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function K5(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=k.squeezeShape(t);if(r.length<t.length){let g=cu(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
+  `}function J5(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=k.squeezeShape(t);if(r.length<t.length){let g=cu(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
       ${lu(g)}
       float ${a}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
@@ -787,7 +787,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${m}.0, ${h}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let f=Hi(n);return`
+    `;let f=qi(n);return`
     float ${a}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -802,7 +802,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         return ${t}[i];
       }
     }
-  `}function e8(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=zT(e.shapeInfo.logicalShape,t.logicalShape),l=ft(i),c=i-s,u,p=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(g=>`coords.${p[g+c]} = 0;`).join(`
+  `}function r8(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=HT(e.shapeInfo.logicalShape,t.logicalShape),l=ft(i),c=i-s,u,p=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(g=>`coords.${p[g+c]} = 0;`).join(`
 `);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((g,y)=>`coords.${p[y+c]}`).join(", ");let h="return outputValue;",m=k.sizeFromShape(e.shapeInfo.logicalShape)===1,f=k.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=`
       return vec4(outputValue.xy, outputValue.xy);
     `;else if(m&&!f)i===1?h=`
@@ -816,22 +816,22 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec4 outputValue = get${a}(${d});
       ${h}
     }
-  `}function t8(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"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&&k.arraysEqual(i,s))return`
+  `}function s8(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"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&&k.arraysEqual(i,s))return`
       float ${r}() {
         return sampleTexture(${n}, resultUV);
       }
-    `;let c=ft(l),u=zT(e.shapeInfo.logicalShape,t.logicalShape),p=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(f=>`coords.${h[f+p]} = 0;`).join(`
+    `;let c=ft(l),u=HT(e.shapeInfo.logicalShape,t.logicalShape),p=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(f=>`coords.${h[f+p]} = 0;`).join(`
 `);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+p]}`).join(", "),`
     float ${r}() {
       ${c} coords = getOutputCoords();
       ${d}
       return get${a}(${m});
     }
-  `}function ft(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 ${e} is not yet supported`)}function cu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function pu(e,t){return t.map(n=>e[n]).join(", ")}function f8(e,t,n,a){let r=t.userCode,s=n.map((h,m)=>{let f={logicalShape:h.shape,texShape:h.isUniform?null:h.texData.texShape,isUniform:h.isUniform,isPacked:h.isUniform?!1:h.texData.isPacked,flatOffset:null};return h.texData!=null&&h.texData.slice!=null&&h.texData.slice.flatOffset>0&&(f.flatOffset=h.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),i=s.map(h=>h.shapeInfo),o={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},l=W5(s,o,r,t.packedInputs),c=e.createProgram(l),u=null,p=e.getUniformLocation(c,"NAN",!1);ee().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let h=0;h<t.variableNames.length;h++){let m=t.variableNames[h],f=!1;d[m]=e.getUniformLocation(c,m,f),d[`offset${m}`]=e.getUniformLocation(c,`offset${m}`,f)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:p}}function VT(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,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!k.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!k.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function g8(e,t,n,a,r){VT(t.inShapeInfos,n),VT([t.outShapeInfo],[a]);let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),ee().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],p=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(k.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&&p!=null&&e.gl.uniform1i(p,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),r!=null&&r(e,t.webGLProgram),e.executeProgram()}function y8(e,t,n){let a="";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;a+=`${i.shape}_${l}_${o}`});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r,s}var{addImpl:b8,bincountImpl:UT,bincountReduceImpl:x8,ceilImpl:v8,concatImpl:w8,expImpl:k8,expm1Impl:I8,floorImpl:N8,gatherV2Impl:T8,greaterImpl:S8,lessImpl:C8,linSpaceImpl:_8,logImpl:E8,maxImpl:A8,maximumImpl:F8,minimumImpl:$8,multiplyImpl:D8,negImpl:R8,prodImpl:M8,rangeImpl:P8,rsqrtImpl:O8,simpleAbsImpl:GT,sliceImpl:L8,stridedSliceImpl:z8,subImpl:B8,tileImpl:W8,topKImpl:V8,transposeImpl:Ov,uniqueImpl:U8}=LN;function HT(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function mn(e,t){return t===1?[e]:HT(e,t)}function G8(e,t){if(e===1)return"rc";let n="";for(let a=0;a<e;a++)n+=t[a],a<e-1&&(n+=",");return n}var K8=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 ft(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 ${e} is not yet supported`)}function cu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function pu(e,t){return t.map(n=>e[n]).join(", ")}function x8(e,t,n,a){let r=t.userCode,s=n.map((h,m)=>{let f={logicalShape:h.shape,texShape:h.isUniform?null:h.texData.texShape,isUniform:h.isUniform,isPacked:h.isUniform?!1:h.texData.isPacked,flatOffset:null};return h.texData!=null&&h.texData.slice!=null&&h.texData.slice.flatOffset>0&&(f.flatOffset=h.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),i=s.map(h=>h.shapeInfo),o={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},l=H5(s,o,r,t.packedInputs),c=e.createProgram(l),u=null,p=e.getUniformLocation(c,"NAN",!1);ee().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let h=0;h<t.variableNames.length;h++){let m=t.variableNames[h],f=!1;d[m]=e.getUniformLocation(c,m,f),d[`offset${m}`]=e.getUniformLocation(c,`offset${m}`,f)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:p}}function KT(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,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!k.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!k.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function v8(e,t,n,a,r){KT(t.inShapeInfos,n),KT([t.outShapeInfo],[a]);let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),ee().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],p=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(k.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&&p!=null&&e.gl.uniform1i(p,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),r!=null&&r(e,t.webGLProgram),e.executeProgram()}function w8(e,t,n){let a="";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;a+=`${i.shape}_${l}_${o}`});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r,s}var{addImpl:k8,bincountImpl:XT,bincountReduceImpl:I8,ceilImpl:N8,concatImpl:T8,expImpl:S8,expm1Impl:C8,floorImpl:_8,gatherV2Impl:E8,greaterImpl:A8,lessImpl:F8,linSpaceImpl:$8,logImpl:D8,maxImpl:R8,maximumImpl:M8,minimumImpl:P8,multiplyImpl:O8,negImpl:L8,prodImpl:z8,rangeImpl:B8,rsqrtImpl:W8,simpleAbsImpl:YT,sliceImpl:V8,stridedSliceImpl:U8,subImpl:G8,tileImpl:H8,topKImpl:j8,transposeImpl:Vv,uniqueImpl:q8}=GN;function ZT(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function mn(e,t){return t===1?[e]:ZT(e,t)}function K8(e,t){if(e===1)return"rc";let n="";for(let a=0;a<e;a++)n+=t[a],a<e-1&&(n+=",");return n}var J8=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=mn("rc",t),a=ft(t),r=H8(t,e,n),s=j8(t,e[e.length-1],e[e.length-2],n),i=q8(e,n);this.userCode=`
+      `;else{let n=mn("rc",t),a=ft(t),r=X8(t,e,n),s=Y8(t,e[e.length-1],e[e.length-2],n),i=Z8(e,n);this.userCode=`
         void main() {
           ${a} rc = getOutputCoords();
 
@@ -843,7 +843,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             setOutput(vec4(${i}));
           }
         }
-      `}}};function X8(e,t){let n=[];for(let a=0;a<=1;a++)for(let r=0;r<=1;r++){let s=`${a===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function H8(e,t,n){if(e===1)return`rc > ${t[0]}`;let a="";for(let r=e-2;r<e;r++)a+=`${n[r]} >= ${t[r]}`,r<e-1&&(a+="||");return a}function j8(e,t,n,a){if(e===1)return"";let r=a.slice(-2);return`
+      `}}};function Q8(e,t){let n=[];for(let a=0;a<=1;a++)for(let r=0;r<=1;r++){let s=`${a===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function X8(e,t,n){if(e===1)return`rc > ${t[0]}`;let a="";for(let r=e-2;r<e;r++)a+=`${n[r]} >= ${t[r]}`,r<e-1&&(a+="||");return a}function Y8(e,t,n,a){if(e===1)return"";let r=a.slice(-2);return`
     int r = ${r[0]};
     int c = ${r[1]};
     int rp1 = r + 1;
@@ -851,12 +851,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
     bool cEdge = cp1 >= ${t};
     bool rEdge = rp1 >= ${n};
-  `}function q8(e,t){let n=e.length,a=X8(n,t);return n===1?`getA(rc),
+  `}function Z8(e,t){let n=e.length,a=Q8(n,t);return n===1?`getA(rc),
             rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
             0, 0`:`getA(${a[0]}),
           cEdge ? 0. : getA(${a[1]}),
           rEdge ? 0. : getA(${a[2]}),
-          rEdge || cEdge ? 0. : getA(${a[3]})`}var jT=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2==1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=`
+          rEdge || cEdge ? 0. : getA(${a[3]})`}var JT=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2==1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=`
         ${r}
         ${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -868,8 +868,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
         ${a>0?"}":""}
       `}this.userCode=`
-      ${Y8(t)}
-      ${Pv(e)}
+      ${eK(t)}
+      ${Wv(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -884,12 +884,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function Y8(e){return`
+    `}};function eK(e){return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${Gi(["r","c","d"],e)}
+      ${ji(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var Z8=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=KT(t,n),r=XT(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=qT(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===tn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===tn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===tn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===tn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===tn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=KT(n,a),s=XT(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=qT(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=ee().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 J8(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 qT(e,t,n,a,r){let s=Q8(t,a),i;if(r){let[l,c]=su(e[0],e[1]);i=l*c}else{let[l,c]=yp(e[0],e[1]);i=l*c}let o=J8(n,s);return i*o}function Q8(e,t){switch(e){case tn.PACKED_2X2_FLOAT32:return OT(t);case tn.PACKED_2X2_FLOAT16:return LT(t);case tn.UNPACKED_FLOAT32:return RT(t);case tn.UNPACKED_FLOAT16:return MT(t);case tn.PACKED_4X1_UNSIGNED_BYTE:return PT(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function eK(e){return ee().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?tn.PACKED_2X2_FLOAT32:tn.UNPACKED_FLOAT32:e?tn.PACKED_2X2_FLOAT16:tn.UNPACKED_FLOAT16}function KT(e,t){if(e===Qn.UPLOAD)return tn.PACKED_2X2_FLOAT32;if(e===Qn.RENDER||e==null)return eK(t);if(e===Qn.DOWNLOAD||e===Qn.PIXELS)return tn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function XT(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var cs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+  `}var tK=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=eS(t,n),r=tS(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=QT(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===tn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===tn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===tn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===tn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===tn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=eS(n,a),s=tS(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=QT(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=ee().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 nK(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 QT(e,t,n,a,r){let s=aK(t,a),i;if(r){let[l,c]=su(e[0],e[1]);i=l*c}else{let[l,c]=bp(e[0],e[1]);i=l*c}let o=nK(n,s);return i*o}function aK(e,t){switch(e){case tn.PACKED_2X2_FLOAT32:return UT(t);case tn.PACKED_2X2_FLOAT16:return GT(t);case tn.UNPACKED_FLOAT32:return BT(t);case tn.UNPACKED_FLOAT16:return WT(t);case tn.PACKED_4X1_UNSIGNED_BYTE:return VT(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function rK(e){return ee().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?tn.PACKED_2X2_FLOAT32:tn.UNPACKED_FLOAT32:e?tn.PACKED_2X2_FLOAT16:tn.UNPACKED_FLOAT16}function eS(e,t){if(e===Qn.UPLOAD)return tn.PACKED_2X2_FLOAT32;if(e===Qn.RENDER||e==null)return rK(t);if(e===Qn.DOWNLOAD||e===Qn.PIXELS)return tn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function tS(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var cs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -900,11 +900,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},Ra="if (isnan(x)) return x;",tK="return x;",YT="return abs(x);",nK="return (x >= 0.0) ? x : (exp(x) - 1.0);",aK=Ra+`
+    `}},Ma="if (isnan(x)) return x;",sK="return x;",nS="return abs(x);",iK="return (x >= 0.0) ? x : (exp(x) - 1.0);",oK=Ma+`
   return (x < 0.0) ? 0.0 : x;
-`,rK=Ra+`
+`,lK=Ma+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,zm="return x;",sK="return x;",iK=`
+`,Vm="return x;",uK="return x;",cK=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -913,7 +913,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,oK=`
+`,pK=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -923,7 +923,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,lK=`
+`,dK=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -944,14 +944,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},uK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=mn("rc",t),a=ft(t),r=G8(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
+    `}},hK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=mn("rc",t),a=ft(t),r=K8(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
       void main() {
         ${a} rc = getOutputCoords();
         vec4 packedInput = getA(${r});
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}},cK=Za.whereImpl,pK=1e-7,dK=1e-4,Lv={};function hK(e){return e in Lv||(Lv[e]={}),Lv[e]}var mK=128,fK=600;function gK(){return ee().global.screen==null?1024:ee().global.screen.height*ee().global.screen.width*window.devicePixelRatio*fK/1024/1024}var bK=class extends sc{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!ee().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=rr(ee().getNumber("WEBGL_VERSION"));this.binaryCache=hK(ee().getNumber("WEBGL_VERSION")),this.gpgpu=new $5(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 Z8(this.gpgpu),this.numMBBeforeWarning=gK(),this.texData=new hd(this,Kr())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((ee().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||ee().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 a={};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:Qn.UPLOAD,refCount:1,complexParentRefCount:0}),a}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--}}decComplexRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.complexParentRefCount>0&&t.refCount--}}move(e,t,n,a){if(ee().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:Qn.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(e){let t=e.dataId;if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let p;o?p=new du(i,zm):p=new cs(i,zm);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,c;l&&(c=k.now());let u;if(a==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);u=_.mergeRealAndImagArrays(p,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=k.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new du(a,zm):h=new cs(a,zm);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!ee().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&ee().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"&&ee().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...bp(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];u=_.mergeRealAndImagArrays(m,f)}else if(l==null)u=this.getValuesFromTexture(e);else{let h=k.sizeFromShape(a);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}c!=null&&this.disposeIntermediateTensorInfo(c);let p=this.convertAndCacheOnCPU(e,u),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),p}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!Oq(n))throw ee().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=k.sizeFromShape(t);if(ee().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...bp(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let s=ee().getBool("WEBGL_PACK")&&a===!0,i=s?$v(t):t,o=s?new d5(i):new p5(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,r);return this.disposeIntermediateTensorInfo(l),u}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.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 ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;if(this.texData.get(e).complexParentRefCount>0){this.texData.get(e).refCount--;return}this.releaseGPUData(e);let{complexTensorInfos:t}=this.texData.get(e);t!=null&&(this.texData.get(t.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.real),this.texData.get(t.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.imag)),this.texData.delete(e)}releaseGPUData(e){let{texture:t,dtype:n,texShape:a,usage:r,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(a,n),this.textureManager.releaseTexture(t,a,r,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 ee().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Kr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=mK){let n=this.getCPUBackend();return!ee().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(a=>this.texData.get(a.dataId).texture==null&&k.sizeFromShape(a.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){_.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return cK(e.shape,t)}packedUnaryOp(e,t,n){let a=new du(e.shape,t);return this.compileAndRun(a,[e],n)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=GT(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(ee().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,YT,e.dtype);let t=new cs(e.shape,YT);return this.compileAndRun(t,[e])}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:a}=this.makeTensorInfo(e,t,n);return Kr().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new uK(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new K8(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[iu(e.shape),...ou(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[iu(t),...ou(t)],s=new jT(r,n),i=!0,o=this.runWebGLProgram(s,[a],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:a,dtype:r}=t,s=$v(a),i;n?i=new c5(s):i=new u5(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:r,dataId:e}],r,null,o);return{dtype:r,shape:a,dataId:l.dataId}}runWebGLProgram(e,t,n,a,r=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===gp.DENSE){let m=bp(e.outputShape);i.texShape=m.map(f=>f*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),k.sizeFromShape(s.shape)===0)return i.values=k.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 f=this.texData.get(m.dataId);if(f.texture==null){if(!e.packedInputs&&k.sizeFromShape(m.shape)<=ee().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:f.values};e.packedInputs&&(f.isPacked=!0,f.shape=m.shape)}else if(!!f.isPacked!=!!e.packedInputs)m=f.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),f=this.texData.get(m.dataId);else if(f.isPacked&&!Lm(f.shape,m.shape)){let g=m,y=m.shape;m.shape=f.shape,m=this.packedReshape(m,y),o.push(m),f=this.texData.get(m.dataId),g.shape=y}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:f,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=y8(e,l,c),p=this.getAndSaveBinary(u,()=>f8(this.gpgpu,e,l,c)),d=this.activeTimers!=null,h;if(d&&(h=this.startTimer()),g8(this.gpgpu,p,l,c,a),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)})),!ee().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,a,r=!1){n=n||t[0].dtype;let s=this.runWebGLProgram(e,t,n,a,r);return Kr().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(ee().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=D(()=>{if(!ee().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=ee().getBool("DEBUG");ee().set("DEBUG",!1);let t=this.abs(de(1e-8)).dataSync()[0];if(ee().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?pK:dK}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=k.now());let u=t.texShape;if(u==null&&(u=t5(n,o),t.texShape=u),r!=null){let p=$v(n),d,h=u[1],m=u[0],f=r instanceof Uint8Array;o?([h,m]=su(u[0],u[1]),d=new m5(p,[m,h],f)):d=new h5(p,[m,h],f);let g=this.makeTensorInfo([m,h],a);f?this.texData.get(g.dataId).usage=Qn.PIXELS:this.texData.get(g.dataId).usage=Qn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,m,r);let y=!0,b=this.runWebGLProgram(d,[g],a,null,y),v=this.texData.get(b.dataId);t.texture=v.texture,t.texShape=v.texShape,t.isPacked=v.isPacked,t.usage=v.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(b.dataId),t.values=null,l&&(this.uploadWaitMs+=k.now()-c)}else{let p=this.acquireTexture(u,i,a,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=yK(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}};function yK(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 a=0;a<n.length;++a)n[a]=Math.round(e[a]);return n}else throw new Error(`Unknown dtype ${t}`)}var xK="3.0.0";eh.isBrowser()&&ih("webgl",()=>new bK,2);var ZT=`
+    `}},mK=Ja.whereImpl,fK=1e-7,gK=1e-4,Uv={};function yK(e){return e in Uv||(Uv[e]={}),Uv[e]}var bK=128,xK=600;function vK(){return ee().global.screen==null?1024:ee().global.screen.height*ee().global.screen.width*window.devicePixelRatio*xK/1024/1024}var Gv=class extends rc{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!ee().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=sr(ee().getNumber("WEBGL_VERSION"));this.binaryCache=yK(ee().getNumber("WEBGL_VERSION")),this.gpgpu=new P5(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 tK(this.gpgpu),this.numMBBeforeWarning=vK(),this.texData=new gd(this,Ha())}nextDataId(){return Gv.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((ee().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||ee().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 a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:Qn.UPLOAD,refCount:1}),a}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,a,r){if(ee().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:Qn.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let p;o?p=new du(i,Vm):p=new cs(i,Vm);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,c;l&&(c=k.now());let u;if(a==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);u=_.mergeRealAndImagArrays(p,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=k.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new du(a,Vm):h=new cs(a,Vm);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!ee().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&ee().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"&&ee().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...xp(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];u=_.mergeRealAndImagArrays(m,f)}else if(l==null)u=this.getValuesFromTexture(e);else{let h=k.sizeFromShape(a);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}c!=null&&this.disposeIntermediateTensorInfo(c);let p=this.convertAndCacheOnCPU(e,u),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ha().removeDataId(e,this),this.pendingDeletes--),p}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!Wq(n))throw ee().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=k.sizeFromShape(t);if(ee().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...xp(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let s=ee().getBool("WEBGL_PACK")&&a===!0,i=s?Ov(t):t,o=s?new g5(i):new f5(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${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 ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(ee().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:a,usage:r,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(a,n),this.textureManager.releaseTexture(t,a,r,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 ee().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Ha().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=bK){let n=this.getCPUBackend();return!ee().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(a=>this.texData.get(a.dataId).texture==null&&k.sizeFromShape(a.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){_.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return mK(e.shape,t)}packedUnaryOp(e,t,n){let a=new du(e.shape,t),r=this.compileAndRun(a,[e],n);return Ha().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let a=YT(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,a)}if(ee().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,nS,e.dtype);let t=new cs(e.shape,nS),n=this.compileAndRun(t,[e]);return Ha().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:a}=this.makeTensorInfo(e,t,n);return Ha().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new hK(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new J8(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[iu(e.shape),...ou(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[iu(t),...ou(t)],s=new JT(r,n),i=!0,o=this.runWebGLProgram(s,[a],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:a,dtype:r}=t,s=Ov(a),i;n?i=new m5(s):i=new h5(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:r,dataId:e}],r,null,o);return{dtype:r,shape:a,dataId:l.dataId}}runWebGLProgram(e,t,n,a,r=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===yp.DENSE){let m=xp(e.outputShape);i.texShape=m.map(f=>f*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),k.sizeFromShape(s.shape)===0)return i.values=k.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 f=this.texData.get(m.dataId);if(f.texture==null){if(!e.packedInputs&&k.sizeFromShape(m.shape)<=ee().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:f.values};e.packedInputs&&(f.isPacked=!0,f.shape=m.shape)}else if(!!f.isPacked!=!!e.packedInputs)m=f.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),f=this.texData.get(m.dataId);else if(f.isPacked&&!Wm(f.shape,m.shape)){let g=m,y=m.shape;m.shape=f.shape,m=this.packedReshape(m,y),o.push(m),f=this.texData.get(m.dataId),g.shape=y}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:f,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=w8(e,l,c),p=this.getAndSaveBinary(u,()=>x8(this.gpgpu,e,l,c)),d=this.activeTimers!=null,h;if(d&&(h=this.startTimer()),v8(this.gpgpu,p,l,c,a),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)})),!ee().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(ee().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=D(()=>{if(!ee().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=ee().getBool("DEBUG");ee().set("DEBUG",!1);let t=this.abs(de(1e-8)).dataSync()[0];if(ee().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?fK:gK}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=k.now());let u=t.texShape;if(u==null&&(u=s5(n,o),t.texShape=u),r!=null){let p=Ov(n),d,h=u[1],m=u[0],f=r instanceof Uint8Array;o?([h,m]=su(u[0],u[1]),d=new b5(p,[m,h],f)):d=new y5(p,[m,h],f);let g=this.makeTensorInfo([m,h],a);f?this.texData.get(g.dataId).usage=Qn.PIXELS:this.texData.get(g.dataId).usage=Qn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,m,r);let y=!0,b=this.runWebGLProgram(d,[g],a,null,y),v=this.texData.get(b.dataId);t.texture=v.texture,t.texShape=v.texShape,t.isPacked=v.isPacked,t.usage=v.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(b.dataId),t.values=null,l&&(this.uploadWaitMs+=k.now()-c)}else{let p=this.acquireTexture(u,i,a,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=wK(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}};Gv.nextDataId=0;function wK(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 a=0;a<n.length;++a)n[a]=Math.round(e[a]);return n}else throw new Error(`Unknown dtype ${t}`)}var kK="3.1.0";ah.isBrowser()&&uh("webgl",()=>new Gv,2);var aS=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `,hu=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.userCode=`
@@ -964,12 +964,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float b = getBAtOutCoords();
         setOutput(binaryOperation(a, b));
       }
-    `}},Bm=`
+    `}},Um=`
   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;
-`,wp=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=_.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length,s="";if(a)if(r===0||k.sizeFromShape(this.outputShape)===1)s=`
+`,kp=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=_.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length,s="";if(a)if(r===0||k.sizeFromShape(this.outputShape)===1)s=`
           result.y = 0.;
           result.z = 0.;
           result.w = 0.;
@@ -1001,21 +1001,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function Bn(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var vK={kernelName:Ko,backendName:"webgl",kernelFunc:Bn};function ps(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=Bn({inputs:{x:a},backend:n}),l=n.texData.get(o.dataId);l.complexParentRefCount++;let c=Bn({inputs:{x:r},backend:n}),u=n.texData.get(c.dataId);return u.complexParentRefCount++,i.complexTensorInfos={real:o,imag:c},s}var wK={kernelName:kd,backendName:"webgl",kernelFunc:ps},JT="return (a < 0.) ? b * a : a;",QT=`
+    `}};function Wn(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var IK={kernelName:Us,backendName:"webgl",kernelFunc:Wn};function ps(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=Wn({inputs:{x:a},backend:n}),l=Wn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var NK={kernelName:Td,backendName:"webgl",kernelFunc:ps},rS="return (a < 0.) ? b * a : a;",sS=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function kK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new wp(QT,r.shape,i.shape):new hu(JT,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),l}var IK={kernelName:Vs,backendName:"webgl",kernelFunc:kK},eS="return (a < 0.) ? b * a : a;",tS=`
+`;function TK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new kp(sS,r.shape,i.shape):new hu(rS,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),l}var SK={kernelName:Gs,backendName:"webgl",kernelFunc:TK},iS="return (a < 0.) ? b * a : a;",oS=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function NK(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new wp(tS,a.shape,r.shape):new hu(eS,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var TK={kernelName:ei,backendName:"webgl",kernelFunc:NK},nS="if (isnan(x)) return x;",SK=`
+`;function CK(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new kp(oS,a.shape,r.shape):new hu(iS,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var _K={kernelName:ni,backendName:"webgl",kernelFunc:CK},lS="if (isnan(x)) return x;",EK=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,CK=`
+`,AK=`
   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 Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,l);return o.makeTensorInfo(i.shape,l,d)}let c=ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new du(i.shape,t):u=new cs(i.shape,e),o.runWebGLProgram(u,[i],l)}}function nn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(a&&l.dtype==="complex64"){let m=u.texData.get(l.dataId),f=u.texData.get(c.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(v=>{let[x,N]=v,T={dataId:x.dataId,dtype:x.dtype,shape:l.shape},C={dataId:N.dataId,dtype:N.dtype,shape:c.shape},$=new hu(e,l.shape,c.shape);return u.runWebGLProgram($,[T,C],la(x.dtype,N.dtype))}),b=ps({inputs:{real:g,imag:y},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(y),b}let p=s||la(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&r!=null){let m=u.texData.get(l.dataId),f=u.texData.get(c.dataId),[g,y]=r(l.shape,c.shape,m.values,f.values,p),b=u.makeTensorInfo(y,p),v=u.texData.get(b.dataId);return v.values=g,b}let d=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new wp(t,l.shape,c.shape,n):h=new hu(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],p)}}function Wm(e,t=!1){if(e==="linear")return t?sK:tK;if(e==="relu")return t?oK:aK;if(e==="elu")return t?iK:nK;if(e==="relu6")return t?lK:rK;if(e==="prelu")return t?tS:eS;if(e==="leakyrelu")return t?QT:JT;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var aS=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=a?e[1]:e[2],u=Math.ceil(c/2),p=a?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) {
+`;function Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,l);return o.makeTensorInfo(i.shape,l,d)}let c=ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new du(i.shape,t):u=new cs(i.shape,e),o.runWebGLProgram(u,[i],l)}}function nn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(a&&l.dtype==="complex64"){let m=u.texData.get(l.dataId),f=u.texData.get(c.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(v=>{let[x,N]=v,T={dataId:x.dataId,dtype:x.dtype,shape:l.shape},C={dataId:N.dataId,dtype:N.dtype,shape:c.shape},F=new hu(e,l.shape,c.shape);return u.runWebGLProgram(F,[T,C],ua(x.dtype,N.dtype))}),b=ps({inputs:{real:g,imag:y},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(y),b}let p=s||ua(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&r!=null){let m=u.texData.get(l.dataId),f=u.texData.get(c.dataId),[g,y]=r(l.shape,c.shape,m.values,f.values,p),b=u.makeTensorInfo(y,p),v=u.texData.get(b.dataId);return v.values=g,b}let d=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new kp(t,l.shape,c.shape,n):h=new hu(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],p)}}function Gm(e,t=!1){if(e==="linear")return t?uK:sK;if(e==="relu")return t?pK:oK;if(e==="elu")return t?cK:iK;if(e==="relu6")return t?dK:lK;if(e==="prelu")return t?oS:iS;if(e==="leakyrelu")return t?sS:rS;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var uS=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=a?e[1]:e[2],u=Math.ceil(c/2),p=a?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:l?f=`vec4 activation(vec4 a) {
@@ -1054,7 +1054,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}},rS={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},sS=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.userCode=`
+    `}},cS={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},pS=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1067,7 +1067,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},iS="return a * b;";function oS(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=_.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),c=new sS(rS.REAL,a.shape,r.shape),u=new sS(rS.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=ps({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[c,u]=D8(a.shape,r.shape,o.values,l.values,s),p=n.makeTensorInfo(u,s),d=n.texData.get(p.dataId);return d.values=c,p}let i;return ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new wp(iS,a.shape,r.shape):i=new hu(iS,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var _K={kernelName:Ys,backendName:"webgl",kernelFunc:oS};function EK(e,t,n){let a=[iu(e.shape),...ou(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[iu(t),...ou(t)],i=new jT(s,a),o=!0,l=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function be(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=k.sizeFromShape(r.shape),l=k.inferFromImplicitShape(s,o),c=k.sizeFromShape(l);k.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(r.dataId);return u.isPacked&&!Lm(r.shape,l)&&!(u.texture!==null&&Lm(u.shape,l))?EK(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var AK={kernelName:dl,backendName:"webgl",kernelFunc:be},lS=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,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 * ${k.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";r%n>0&&(c=`
+    `}},dS="return a * b;";function hS(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=_.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),c=new pS(cS.REAL,a.shape,r.shape),u=new pS(cS.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=ps({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[c,u]=O8(a.shape,r.shape,o.values,l.values,s),p=n.makeTensorInfo(u,s),d=n.texData.get(p.dataId);return d.values=c,p}let i;return ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new kp(dS,a.shape,r.shape):i=new hu(dS,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var FK={kernelName:Js,backendName:"webgl",kernelFunc:hS};function $K(e,t,n){let a=[iu(e.shape),...ou(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[iu(t),...ou(t)],i=new JT(s,a),o=!0,l=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function be(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=k.sizeFromShape(r.shape),l=k.inferFromImplicitShape(s,o),c=k.sizeFromShape(l);k.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(r.dataId);return u.isPacked&&!Wm(r.shape,l)&&!(u.texture!==null&&Wm(u.shape,l))?$K(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var DK={kernelName:dl,backendName:"webgl",kernelFunc:be},mS=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,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 * ${k.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";r%n>0&&(c=`
         if (inIdx < 0 || inIdx >= ${r}) {
           return 0.0;
         }
@@ -1120,7 +1120,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(sumValue);
       }
-    `}},FK=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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,p=`
+    `}},RK=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,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}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,p=`
       if (${t==="sum"}) {
         sumValue += dot(values, ones);
       } else if (${t==="prod"}) {
@@ -1205,12 +1205,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${l});
       }
-    `}};function $K(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=_.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function ji(e,t,n,a){let r=$K(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:c}=r[i],u,p;n==="mean"?u=i===0?new lS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new lS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new FK({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),p=s,s=a.runWebGLProgram(u,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var RK=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 a=ft(this.rank),r=DK(t);this.userCode=`
+    `}};function MK(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=_.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function Ki(e,t,n,a){let r=MK(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:c}=r[i],u,p;n==="mean"?u=i===0?new mS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new mS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new RK({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),p=s,s=a.runWebGLProgram(u,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var OK=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 a=ft(this.rank),r=PK(t);this.userCode=`
     void main() {
       ${a} resRC = getOutputCoords();
       setOutput(getA(${r}));
     }
-    `}};function DK(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"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var MK=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 ${this.rank} is not yet supported.`);let a=ft(this.rank),r=HT("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=r[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
+    `}};function PK(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"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var LK=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 ${this.rank} is not yet supported.`);let a=ft(this.rank),r=ZT("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=r[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
     void main() {
       ${a} rc = getOutputCoords();
       vec4 result = vec4(0.);
@@ -1227,14 +1227,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function Vm(e,t,n){let a=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new MK(e.shape,t):new RK(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function PK(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,l=_.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=Vm(e,l,a),o=_.getInnerMostAxes(o.length,s)),_.assertAxesAreInnerMostDims("sum",o,s);let[p,d]=_.computeOutAndReduceShapes(u.shape,o),h=p;n&&(h=_.expandShapeToKeepDim(p,i));let m=k.sizeFromShape(d),f=k.sizeFromShape(e.shape)/m,g=be({inputs:{x:u},attrs:{shape:[f,m]},backend:a}),y=Qd(e.dtype),b=ji(g,y,"sum",a),v=be({inputs:{x:b},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(b),c&&a.disposeIntermediateTensorInfo(u),v}function zv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return PK(r,s,i,n)}var OK={kernelName:ci,backendName:"webgl",kernelFunc:zv};function Tn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=r.shape[s[u]];let c;if(i.shouldExecuteOnCPU([r])){let u=i.texData.get(r.dataId).values,p=Ov(u,r.shape,r.dtype,s,l);c=i.makeTensorInfo(l,r.dtype);let d=i.texData.get(c.dataId);d.values=p}else c=Vm(r,s,i);return c}var LK={kernelName:fi,backendName:"webgl",kernelFunc:Tn},uS=1e3;function Um({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,p=n?e.shape[c-2]:e.shape[c-1],d=a?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],m=a?t.shape[u-2]:t.shape[u-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=k.sizeFromShape(f),b=k.sizeFromShape(g),v=y===b||y===1||b===1;k.assert(c>=2&&u>=2&&v,()=>`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 (${f}) and (${g}).`);let x=(y>b?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);k.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let N=n?[y,p,h]:[y,h,p],T=a?[b,m,d]:[b,d,m],C=be({inputs:{x:e},backend:r,attrs:{shape:N}}),$=be({inputs:{x:t},backend:r,attrs:{shape:T}}),F=[C,$],O=Math.max(y,b),B=n?C.shape[1]:C.shape[2],W=s!=null,G=i!=null,q=l==="leakyrelu",H=l!=null?Wm(l,!0):null,K=W||G||q||H!=null,X;if((h===1||m===1)&&B>uS&&K===!1){let J=C,se=$;n&&(J=Tn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),F.push(J)),a&&(se=Tn({inputs:{x:$},backend:r,attrs:{perm:[0,2,1]}}),F.push(se));let Q=m!==1,oe=m===1,ae=J;Q&&(ae=be({inputs:{x:J},backend:r,attrs:{shape:[O,B,1]}}),F.push(ae));let pe=m===1?2:1,ue=se;oe&&(ue=be({inputs:{x:se},backend:r,attrs:{shape:[O,1,B]}}),F.push(ue));let ge=oS({inputs:{a:ae,b:ue},backend:r});X=zv({inputs:{x:ge},backend:r,attrs:{axis:pe,keepDims:!0}}),F.push(ge)}else{let J=la(e.dtype,t.dtype),se=new aS(N,T,[O,h,m],n,a,W,H,G,q),Q=[C,$];if(s!=null&&Q.push(s),G&&Q.push(i),q){let oe=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));Q.push(oe),F.push(oe)}X=r.runWebGLProgram(se,Q,J)}let ne=be({inputs:{x:X},backend:r,attrs:{shape:x}});F.push(X);for(let J of F)r.disposeIntermediateTensorInfo(J);return ne}function zK(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:p}=a;return Um({a:r,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:u})}var BK={kernelName:gi,backendName:"webgl",kernelFunc:zK},cS="return abs(x);";function WK(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=GT(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new du(a.shape,cS):r=new cs(a.shape,cS),n.runWebGLProgram(r,[a],a.dtype)}var VK={kernelName:So,backendName:"webgl",kernelFunc:WK},UK=Ra+`
+    `}};function Hm(e,t,n){let a=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new LK(e.shape,t):new OK(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function zK(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,l=_.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=Hm(e,l,a),o=_.getInnerMostAxes(o.length,s)),_.assertAxesAreInnerMostDims("sum",o,s);let[p,d]=_.computeOutAndReduceShapes(u.shape,o),h=p;n&&(h=_.expandShapeToKeepDim(p,i));let m=k.sizeFromShape(d),f=k.sizeFromShape(e.shape)/m,g=be({inputs:{x:u},attrs:{shape:[f,m]},backend:a}),y=nh(e.dtype),b=Ki(g,y,"sum",a),v=be({inputs:{x:b},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(b),c&&a.disposeIntermediateTensorInfo(u),v}function Hv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return zK(r,s,i,n)}var BK={kernelName:di,backendName:"webgl",kernelFunc:Hv};function Tn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=r.shape[s[u]];let c;if(i.shouldExecuteOnCPU([r])){let u=i.texData.get(r.dataId).values,p=Vv(u,r.shape,r.dtype,s,l);c=i.makeTensorInfo(l,r.dtype);let d=i.texData.get(c.dataId);d.values=p}else c=Hm(r,s,i);return c}var WK={kernelName:yi,backendName:"webgl",kernelFunc:Tn},fS=1e3;function jm({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,p=n?e.shape[c-2]:e.shape[c-1],d=a?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],m=a?t.shape[u-2]:t.shape[u-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=k.sizeFromShape(f),b=k.sizeFromShape(g),v=y===b||y===1||b===1;k.assert(c>=2&&u>=2&&v,()=>`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 (${f}) and (${g}).`);let x=(y>b?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);k.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let N=n?[y,p,h]:[y,h,p],T=a?[b,m,d]:[b,d,m],C=be({inputs:{x:e},backend:r,attrs:{shape:N}}),F=be({inputs:{x:t},backend:r,attrs:{shape:T}}),$=[C,F],P=Math.max(y,b),B=n?C.shape[1]:C.shape[2],W=s!=null,G=i!=null,q=l==="leakyrelu",j=l!=null?Gm(l,!0):null,X=W||G||q||j!=null,K;if((h===1||m===1)&&B>fS&&X===!1){let J=C,se=F;n&&(J=Tn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),$.push(J)),a&&(se=Tn({inputs:{x:F},backend:r,attrs:{perm:[0,2,1]}}),$.push(se));let Q=m!==1,oe=m===1,ae=J;Q&&(ae=be({inputs:{x:J},backend:r,attrs:{shape:[P,B,1]}}),$.push(ae));let pe=m===1?2:1,ue=se;oe&&(ue=be({inputs:{x:se},backend:r,attrs:{shape:[P,1,B]}}),$.push(ue));let ge=hS({inputs:{a:ae,b:ue},backend:r});K=Hv({inputs:{x:ge},backend:r,attrs:{axis:pe,keepDims:!0}}),$.push(ge)}else{let J=ua(e.dtype,t.dtype),se=new uS(N,T,[P,h,m],n,a,W,j,G,q),Q=[C,F];if(s!=null&&Q.push(s),G&&Q.push(i),q){let oe=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));Q.push(oe),$.push(oe)}K=r.runWebGLProgram(se,Q,J)}let ne=be({inputs:{x:K},backend:r,attrs:{shape:x}});$.push(K);for(let J of $)r.disposeIntermediateTensorInfo(J);return ne}function VK(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:p}=a;return jm({a:r,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:u})}var UK={kernelName:bi,backendName:"webgl",kernelFunc:VK},gS="return abs(x);";function GK(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=YT(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new du(a.shape,gS):r=new cs(a.shape,gS),n.runWebGLProgram(r,[a],a.dtype)}var HK={kernelName:_o,backendName:"webgl",kernelFunc:GK},jK=Ma+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,GK=Ze({opSnippet:UK}),HK={kernelName:Co,backendName:"webgl",kernelFunc:GK},jK=Ra+`
+`,qK=Ze({opSnippet:jK}),KK={kernelName:Eo,backendName:"webgl",kernelFunc:qK},XK=Ma+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,qK=Ze({opSnippet:jK}),KK={kernelName:_o,backendName:"webgl",kernelFunc:qK},pS="return a + b;",XK=nn({opSnippet:pS,packedOpSnippet:pS,supportsComplex:!0,cpuKernelImpl:b8}),YK={kernelName:Lr,backendName:"webgl",kernelFunc:XK},ZK=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,YK=Ze({opSnippet:XK}),ZK={kernelName:Ao,backendName:"webgl",kernelFunc:YK},yS="return a + b;",JK=nn({opSnippet:yS,packedOpSnippet:yS,supportsComplex:!0,cpuKernelImpl:k8}),QK={kernelName:zr,backendName:"webgl",kernelFunc:JK},eX=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1242,7 +1242,7 @@ return log(x + sqrt(x * x - 1.0));`,qK=Ze({opSnippet:jK}),KK={kernelName:_o,back
         float result = ${a};
         setOutput(result);
       }
-    `}},JK=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
+    `}},tX=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1250,7 +1250,7 @@ return log(x + sqrt(x * x - 1.0));`,qK=Ze({opSnippet:jK}),KK={kernelName:_o,back
         vec4 result = ${a};
         setOutput(result);
       }
-    `}};function Gm(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Bn({inputs:{x:a[0]},backend:n});if(a.length>ee().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Gm({inputs:a.slice(0,o),backend:n}),c=Gm({inputs:a.slice(o),backend:n});return Gm({inputs:[l,c],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>la(o,l)),s=a.map(o=>o.shape),i=ee().getBool("WEBGL_PACK")?new JK(a[0].shape,s):new ZK(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var QK={kernelName:Ss,backendName:"webgl",kernelFunc:Gm};function eX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=r;u!=null&&(p=Tn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,o)),_.assertAxesAreInnerMostDims("all",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=be({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=ji(f,f.dtype,"all",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=be({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var tX={kernelName:yd,backendName:"webgl",kernelFunc:eX};function nX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=r;u!=null&&(p=Tn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,o)),_.assertAxesAreInnerMostDims("any",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=be({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=ji(f,f.dtype,"any",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=be({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var aX={kernelName:bd,backendName:"webgl",kernelFunc:nX},rX=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function qm(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Wn({inputs:{x:a[0]},backend:n});if(a.length>ee().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=qm({inputs:a.slice(0,o),backend:n}),c=qm({inputs:a.slice(o),backend:n});return qm({inputs:[l,c],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ua(o,l)),s=a.map(o=>o.shape),i=ee().getBool("WEBGL_PACK")?new tX(a[0].shape,s):new eX(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var nX={kernelName:Ss,backendName:"webgl",kernelFunc:qm};function aX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=r;u!=null&&(p=Tn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,o)),_.assertAxesAreInnerMostDims("all",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=be({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Ki(f,f.dtype,"all",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=be({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var rX={kernelName:vd,backendName:"webgl",kernelFunc:aX};function sX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=r;u!=null&&(p=Tn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,o)),_.assertAxesAreInnerMostDims("any",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=be({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Ki(f,f.dtype,"any",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=be({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var iX={kernelName:wd,backendName:"webgl",kernelFunc:sX},oX=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,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];
@@ -1270,7 +1270,7 @@ return log(x + sqrt(x * x - 1.0));`,qK=Ze({opSnippet:jK}),KK={kernelName:_o,back
         }
         setOutput(float(bestIndex));
       }
-    `}},sX=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ft(o),c=mn("coords",o),u,p;if(s===1){p=o+1;let C=ft(p);u=`
+    `}},lX=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ft(o),c=mn("coords",o),u,p;if(s===1){p=o+1;let C=ft(p);u=`
         ${C} sourceLocR = ${C}(${c.join()}, 0);
         ++${c[o-1]};
         ${C} sourceLocG = ${C}(${c.join()}, 0);
@@ -1332,23 +1332,23 @@ return log(x + sqrt(x * x - 1.0));`,qK=Ze({opSnippet:jK}),KK={kernelName:_o,back
         }
         setOutput(bestIndex);
       }
-    `}};function dS(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=_.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new rX(o,n,a==null),c=[t];a!=null&&c.push(a);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let p=dS(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}function hS(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=_.computeOptimalWindowSize(s),o=new sX(r,i,n,a==null),l=a==null?[t]:[t,a],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=hS(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function mS(e,t,n,a){let r=[n];if(_.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!ee().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=_.computeOutAndReduceShapes(t.shape,r),l=k.sizeFromShape(o),c=be({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=dS(e,c,a);s.push(u);let p=be({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),p}return hS(e,t,a)}function iX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=Tn({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=mS(n,l,i[0],"max");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var oX={kernelName:Cs,backendName:"webgl",kernelFunc:iX};function lX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=Tn({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=mS(n,l,i[0],"min");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var uX={kernelName:lc,backendName:"webgl",kernelFunc:lX},cX=Ra+`
+    `}};function bS(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=_.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new oX(o,n,a==null),c=[t];a!=null&&c.push(a);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let p=bS(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}function xS(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=_.computeOptimalWindowSize(s),o=new lX(r,i,n,a==null),l=a==null?[t]:[t,a],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=xS(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function vS(e,t,n,a){let r=[n];if(_.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!ee().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=_.computeOutAndReduceShapes(t.shape,r),l=k.sizeFromShape(o),c=be({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=bS(e,c,a);s.push(u);let p=be({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),p}return xS(e,t,a)}function uX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=Tn({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=vS(n,l,i[0],"max");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var cX={kernelName:Cs,backendName:"webgl",kernelFunc:uX};function pX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=Tn({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=vS(n,l,i[0],"min");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var dX={kernelName:oc,backendName:"webgl",kernelFunc:pX},hX=Ma+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,pX=Ze({opSnippet:cX}),dX={kernelName:Eo,backendName:"webgl",kernelFunc:pX},hX=Ra+"return log(x + sqrt(x * x + 1.0));",mX=Ze({opSnippet:hX}),fX={kernelName:Ao,backendName:"webgl",kernelFunc:mX},gX=Ra+`
+`,mX=Ze({opSnippet:hX}),fX={kernelName:Fo,backendName:"webgl",kernelFunc:mX},gX=Ma+"return log(x + sqrt(x * x + 1.0));",yX=Ze({opSnippet:gX}),bX={kernelName:$o,backendName:"webgl",kernelFunc:yX},xX=Ma+`
   return atan(x);
-`,yX=Ze({opSnippet:gX}),bX={kernelName:Fo,backendName:"webgl",kernelFunc:yX},xX=SK+`
+`,vX=Ze({opSnippet:xX}),wX={kernelName:Do,backendName:"webgl",kernelFunc:vX},kX=EK+`
   return atan(a, b);
-`,vX=`
+`,IX=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+CK+`
+  `+AK+`
   return result;
-`,wX=nn({opSnippet:xX,packedOpSnippet:vX}),kX={kernelName:Do,backendName:"webgl",kernelFunc:wX},IX=Ra+`
+`,NX=nn({opSnippet:kX,packedOpSnippet:IX}),TX={kernelName:Mo,backendName:"webgl",kernelFunc:NX},SX=Ma+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelName:$o,backendName:"webgl",kernelFunc:NX},kp=class{constructor(e,t,n,a=!1,r=!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,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,CX=Ze({opSnippet:SX}),_X={kernelName:Ro,backendName:"webgl",kernelFunc:CX},Ip=class{constructor(e,t,n,a=!1,r=!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,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${o});
         const ivec2 pads = ivec2(${d}, ${h});
 
@@ -1489,7 +1489,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         }
         setOutput(${v});
       }
-    `}},Bv=class{constructor(e,t,n,a=!1,r=!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,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",v="0.0";if(b||(v="-1.0 / 1e-20"),n){let F=">=";this.userCode=`
+    `}},jv=class{constructor(e,t,n,a=!1,r=!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,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",v="0.0";if(b||(v="-1.0 / 1e-20"),n){let $=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${i}, ${o}, ${l});
         const ivec3 pads = ivec3(${f}, ${g}, ${y});
@@ -1540,7 +1540,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
                 // use the current value.
                 float currMinMaxValue = mix(
                     value, minMaxValue, minMaxValueFound);
-                if (value ${F} currMinMaxValue) {
+                if (value ${$} currMinMaxValue) {
                   minMaxValue = value;
                   minMaxValueFound = 1.0;
                   minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} +
@@ -1551,7 +1551,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
           }
           setOutput(float(minMaxPosition));
         }
-      `;return}let x="max",N=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(N="avgValue / count");let T=Math.floor(s/4)*4,C=s%4,$=`
+      `;return}let x="max",N=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(N="avgValue / count");let T=Math.floor(s/4)*4,C=s%4,F=`
       if (${b}) {
         avgValue += dot(values, ones);
       } else {
@@ -1616,7 +1616,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
                 getValue(batch, xD, xR, xC + 3 * ${p}, ch)
               );
 
-              ${$}
+              ${F}
             }
 
             int xC = xCCorner + ${T};
@@ -1628,7 +1628,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
                 initializationValue
               );
 
-              ${$}
+              ${F}
             } else if (${C===2}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
@@ -1637,7 +1637,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
                 initializationValue
               );
 
-              ${$}
+              ${F}
             } else if (${C===3}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
@@ -1646,13 +1646,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
                 initializationValue
               );
 
-              ${$}
+              ${F}
             }
           }
           setOutput(${N});
         }
       }
-    `}};function SX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;xp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.computePool2DInfo(r.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Bn({inputs:{x:r},backend:n});let p=new kp(u,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var CX={kernelName:_s,backendName:"webgl",kernelFunc:SX};function _X(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=a,u=[1,1,1],p=_.computePool3DInfo(r.shape,s,i,u,o,l,c),d=new Bv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var EX={kernelName:uc,backendName:"webgl",kernelFunc:_X},AX=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=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,p=1/(t*n);this.userCode=`
+    `}};function EX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;vp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.computePool2DInfo(r.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Wn({inputs:{x:r},backend:n});let p=new Ip(u,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var AX={kernelName:_s,backendName:"webgl",kernelFunc:EX};function FX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=a,u=[1,1,1],p=_.computePool3DInfo(r.shape,s,i,u,o,l,c),d=new jv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var $X={kernelName:lc,backendName:"webgl",kernelFunc:FX},DX=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`
       const ivec2 pads = ivec2(${c}, ${u});
       const float avgMultiplier = float(${p});
 
@@ -1694,7 +1694,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},FX=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=u-1-e.padInfo.front,m=p-1-e.padInfo.top,f=d-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
+    `}},RX=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=u-1-e.padInfo.front,m=p-1-e.padInfo.top,f=d-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
       const ivec3 pads = ivec3(${h}, ${m}, ${f});
       const float avgMultiplier = float(${g});
 
@@ -1750,7 +1750,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function $X(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=a,p=[1,1,1],d=_.computePool3DInfo(i.shape,o,l,p,c,u),h=new FX(d);return n.runWebGLProgram(h,[r],i.dtype)}var DX={kernelName:vd,backendName:"webgl",kernelFunc:$X};function RX(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;xp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=a,u=_.computePool2DInfo(i.shape,o,l,1,c),p=new AX(u);return n.runWebGLProgram(p,[r],i.dtype)}var MX={kernelName:xd,backendName:"webgl",kernelFunc:RX};function PX(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Um({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var OX={kernelName:Es,backendName:"webgl",kernelFunc:PX},LX=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function MX(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=a,p=[1,1,1],d=_.computePool3DInfo(i.shape,o,l,p,c,u),h=new RX(d);return n.runWebGLProgram(h,[r],i.dtype)}var PX={kernelName:Id,backendName:"webgl",kernelFunc:MX};function OX(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;vp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=a,u=_.computePool2DInfo(i.shape,o,l,1,c),p=new DX(u);return n.runWebGLProgram(p,[r],i.dtype)}var LX={kernelName:kd,backendName:"webgl",kernelFunc:OX};function zX(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return jm({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var BX={kernelName:Es,backendName:"webgl",kernelFunc:zX},WX=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -1760,7 +1760,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         float inv = scale * inversesqrt(variance + float(${s}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}},zX=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}},VX=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         vec4 offset = ${i};
         vec4 scale = ${o};
@@ -1773,7 +1773,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
 
         setOutput((x - mean) * inv + offset);
       }
-    `}},BX=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;k.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||r.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=[a,r,s],u=null;i!=null&&(u=i.shape,c.push(i));let p=null;o!=null&&(p=o.shape,c.push(o));let d=ee().getBool("WEBGL_PACK_NORMALIZATION")?new zX(a.shape,r.shape,s.shape,u,p,l):new LX(a.shape,r.shape,s.shape,u,p,l);return t.runWebGLProgram(d,c,c[0].dtype)},WX={kernelName:Bs,backendName:"webgl",kernelFunc:BX},UX=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ft(this.rank),n=`uniform int start[${this.rank}];`,a=VX(this.rank),r,s=e.map((i,o)=>`sourceLoc.${Wv[o]} = start[${o}] + coords.${Wv[o]};`);r=`
+    `}},UX=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;k.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||r.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=[a,r,s],u=null;i!=null&&(u=i.shape,c.push(i));let p=null;o!=null&&(p=o.shape,c.push(o));let d=ee().getBool("WEBGL_PACK_NORMALIZATION")?new VX(a.shape,r.shape,s.shape,u,p,l):new WX(a.shape,r.shape,s.shape,u,p,l);return t.runWebGLProgram(d,c,c[0].dtype)},GX={kernelName:Ws,backendName:"webgl",kernelFunc:UX},jX=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ft(this.rank),n=`uniform int start[${this.rank}];`,a=HX(this.rank),r,s=e.map((i,o)=>`sourceLoc.${qv[o]} = start[${o}] + coords.${qv[o]};`);r=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${s.join(`
@@ -1784,7 +1784,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         ${r}
         setOutput(getSource(${a}));
       }
-    `}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)}}},Wv=["x","y","z","w","u","v"];function VX(e){if(e===1)return"sourceLoc";if(e<=6)return Wv.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var GX=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ft(this.rank),n=mn("coords",this.rank),a=mn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${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)}}},qv=["x","y","z","w","u","v"];function HX(e){if(e===1)return"sourceLoc";if(e<=6)return qv.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var qX=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ft(this.rank),n=mn("coords",this.rank),a=mn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=`
       result.x = ${s};
       if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
         ++${a[this.rank-1]};
@@ -1814,7 +1814,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={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 HX(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.complexParentRefCount=0,i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=cn.computeFlatOffset(t,k.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Ip(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=cn.parseSliceParams(r,s,i);if(cn.assertParamsValid(r,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=L8(p.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}let{isPacked:c}=n.texData.get(r.dataId),u=cn.isSliceContinous(r.shape,o,l);if(c||!u){let p=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new GX(l):new UX(l),d=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),HX(r,o,l,n)}var jX={kernelName:gl,backendName:"webgl",kernelFunc:Ip},qX=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;k.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,v)=>b*v),l=_.getReshaped(r.shape,s,o),c=_.getPermuted(l.length,s.length),u=_.getReshapedPermuted(r.shape,s,o),p=_.getSliceBeginCoords(i,s.length),d=_.getSliceSize(u,i,s.length),h=[],m=be({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Tn({inputs:{x:m},backend:n,attrs:{perm:c}}),g=be({inputs:{x:f},backend:n,attrs:{shape:u}}),y=Ip({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y},KX={kernelName:cc,backendName:"webgl",kernelFunc:qX};function XX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),c=UT(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var YX={kernelName:wd,backendName:"webgl",kernelFunc:XX},ZX="return float(a != b);",fS=nn({opSnippet:ZX,dtype:"bool"}),JX={kernelName:rl,backendName:"webgl",kernelFunc:fS};function Np(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Bn({inputs:{x:r.complexTensorInfos.real},backend:n})}var QX={kernelName:Vd,backendName:"webgl",kernelFunc:Np},eY="return float(int(x));";function tY(e,t){let n=new cs(e.shape,eY),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function Vv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Bn({inputs:{x:r},backend:n});let i=vt(r.shape),o=Vv({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=ps({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Np({inputs:{input:r},backend:n}),o=Vv({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=Bn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return tY(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=fS({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var nY={kernelName:As,backendName:"webgl",kernelFunc:Vv},gS="return ceil(x);",aY=Ze({opSnippet:gS,packedOpSnippet:gS,cpuKernelImpl:v8}),rY={kernelName:Ro,backendName:"webgl",kernelFunc:aY},sY=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${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 KX(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=cn.computeFlatOffset(t,k.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Np(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=cn.parseSliceParams(r,s,i);if(cn.assertParamsValid(r,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=V8(p.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}let{isPacked:c}=n.texData.get(r.dataId),u=cn.isSliceContinous(r.shape,o,l);if(c||!u){let p=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qX(l):new jX(l),d=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),KX(r,o,l,n)}var XX={kernelName:gl,backendName:"webgl",kernelFunc:Np},YX=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;k.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,v)=>b*v),l=_.getReshaped(r.shape,s,o),c=_.getPermuted(l.length,s.length),u=_.getReshapedPermuted(r.shape,s,o),p=_.getSliceBeginCoords(i,s.length),d=_.getSliceSize(u,i,s.length),h=[],m=be({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Tn({inputs:{x:m},backend:n,attrs:{perm:c}}),g=be({inputs:{x:f},backend:n,attrs:{shape:u}}),y=Np({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y},ZX={kernelName:uc,backendName:"webgl",kernelFunc:YX};function JX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),c=XT(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var QX={kernelName:Nd,backendName:"webgl",kernelFunc:JX},eY="return float(a != b);",wS=nn({opSnippet:eY,dtype:"bool"}),tY={kernelName:rl,backendName:"webgl",kernelFunc:wS};function Tp(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Wn({inputs:{x:r.complexTensorInfos.real},backend:n})}var nY={kernelName:Hd,backendName:"webgl",kernelFunc:Tp},aY="return float(int(x));";function rY(e,t){let n=new cs(e.shape,aY),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function Kv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Wn({inputs:{x:r},backend:n});let i=vt(r.shape),o=Kv({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=ps({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Tp({inputs:{input:r},backend:n}),o=Kv({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=Wn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return rY(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=wS({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var sY={kernelName:As,backendName:"webgl",kernelFunc:Kv},kS="return ceil(x);",iY=Ze({opSnippet:kS,packedOpSnippet:kS,cpuKernelImpl:N8}),oY={kernelName:Fs,backendName:"webgl",kernelFunc:iY},lY=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1827,7 +1827,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},iY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},uY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1841,7 +1841,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function oY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;ee().getBool("WEBGL_PACK_CLIP")?o=new iY(r.shape):o=new sY(r.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,l)}var lY={kernelName:zr,backendName:"webgl",kernelFunc:oY},uY=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function cY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;ee().getBool("WEBGL_PACK_CLIP")?o=new uY(r.shape):o=new lY(r.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,l)}var pY={kernelName:Br,backendName:"webgl",kernelFunc:cY},dY=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -1854,7 +1854,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function yS(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function cY(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new uY(a.shape),i=[yS(a,r.complexTensorInfos.real),yS(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var pY={kernelName:pc,backendName:"webgl",kernelFunc:cY},dY=class{constructor(e){this.outputShape=[],this.outputShape=_.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-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
+    `}};function IS(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function hY(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new dY(a.shape),i=[IS(a,r.complexTensorInfos.real),IS(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var mY={kernelName:cc,backendName:"webgl",kernelFunc:hY},fY=class{constructor(e){this.outputShape=[],this.outputShape=_.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${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-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -1863,18 +1863,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         ${n.join(`
         `)}
       }
-    `}},hY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=_.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=ft(a),s=mn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],c=i.slice(-2),u=i.join(),p=`if (${l} < ${o[0]}) {
+    `}},gY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=_.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=ft(a),s=mn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],c=i.slice(-2),u=i.join(),p=`if (${l} < ${o[0]}) {
         return getChannel(
             getT0(${u}), vec2(${c.join()}));
         }`;for(let m=1;m<o.length;m++){let f=o[m-1];p+=`
         if (${l} < ${o[m]}  && ${l} >= ${o[m-1]}) {
           return getChannel(
-            getT${m}(${Hm(i,l,f)}),
-            vec2(${Hm(c,l,f)}));
+            getT${m}(${Km(i,l,f)}),
+            vec2(${Km(c,l,f)}));
         }`}let d=o.length,h=o[o.length-1];p+=`
         return getChannel(
-          getT${d}(${Hm(i,l,h)}),
-          vec2(${Hm(c,l,h)}));`,this.userCode=`
+          getT${d}(${Km(i,l,h)}),
+          vec2(${Km(c,l,h)}));`,this.userCode=`
       float getValue(${i.map(m=>"int "+m)}) {
         ${p}
       }
@@ -1900,7 +1900,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         }
         setOutput(result);
       }
-    `}};function Hm(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function jm(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Bn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var mY={kernelName:Md,backendName:"webgl",kernelFunc:jm};function mu(e,t,n){let a=e[0].dtype;if(a==="complex64"){let c=e.map(m=>Np({inputs:{input:m},backend:n})),u=e.map(m=>jm({inputs:{input:m},backend:n})),p=mu(c,t,n),d=mu(u,t,n),h=ps({inputs:{real:p,imag:d},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),u.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(a==="string"){let{tensors2D:c,outShape:u}=bS(e,t,n),p=c.map(g=>({vals:n.readSync(g.dataId),shape:g.shape})),d=c[0].shape[0]===1,h=w8(p,u,a,d),m=_.computeOutShape(e.map(g=>g.shape),t),f=n.makeTensorInfo(m,a,h);return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),f}if(e.length>ee().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=mu(e.slice(0,c),t,n),p=mu(e.slice(c),t,n),d=mu([u,p],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),d}if(ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new hY(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,a)}let{tensors2D:r,outShape:s}=bS(e,t,n),i=new dY(r.map(c=>c.shape)),o=n.runWebGLProgram(i,r,a);r.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=be({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function bS(e,t,n){let a=_.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>be({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function xS(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=_.computeOutShape(t.map(c=>c.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>k.sizeFromShape(c.shape)>0);if(o.length===1)return Bn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return _.assertParamsConsistent(l,s),mu(o,s,n)}var fY={kernelName:Mo,backendName:"webgl",kernelFunc:xS},vS=class{constructor(e,t=!1,n=null,a=!1,r=!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,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,b=f?3:1,v="",x="";n&&(a?v=`float activation(float a) {
+    `}};function Km(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function Xm(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Wn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var yY={kernelName:Ld,backendName:"webgl",kernelFunc:Xm};function mu(e,t,n){let a=e[0].dtype;if(a==="complex64"){let c=e.map(m=>Tp({inputs:{input:m},backend:n})),u=e.map(m=>Xm({inputs:{input:m},backend:n})),p=mu(c,t,n),d=mu(u,t,n),h=ps({inputs:{real:p,imag:d},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),u.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(a==="string"){let{tensors2D:c,outShape:u}=NS(e,t,n),p=c.map(g=>({vals:n.readSync(g.dataId),shape:g.shape})),d=c[0].shape[0]===1,h=T8(p,u,a,d),m=_.computeOutShape(e.map(g=>g.shape),t),f=n.makeTensorInfo(m,a,h);return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),f}if(e.length>ee().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=mu(e.slice(0,c),t,n),p=mu(e.slice(c),t,n),d=mu([u,p],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),d}if(ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new gY(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,a)}let{tensors2D:r,outShape:s}=NS(e,t,n),i=new fY(r.map(c=>c.shape)),o=n.runWebGLProgram(i,r,a);r.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=be({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function NS(e,t,n){let a=_.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>be({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function TS(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=_.computeOutShape(t.map(c=>c.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>k.sizeFromShape(c.shape)>0);if(o.length===1)return Wn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return _.assertParamsConsistent(l,s),mu(o,s,n)}var bY={kernelName:Po,backendName:"webgl",kernelFunc:TS},SS=class{constructor(e,t=!1,n=null,a=!1,r=!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,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,b=f?3:1,v="",x="";n&&(a?v=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:r?v=`float activation(float a) {
@@ -2034,7 +2034,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         ${x}
         setOutput(result);
       }
-    `}},gY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
+    `}},xY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
       const ivec3 strides = ivec3(${r}, ${s}, ${i});
       const ivec3 pads = ivec3(${t}, ${n}, ${a});
 
@@ -2122,7 +2122,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},yY=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:a,inChannels:r,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:p}=n,{left:d,top:h}=o,m=r*a,f=hn(),g=p==="channelsLast",y=g?0:1,b=g?1:2,v="";for(let x=0;x<=1;x++)for(let N=0;N<=1;N++)v+=`
+    `}},vY=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:a,inChannels:r,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:p}=n,{left:d,top:h}=o,m=r*a,f=hn(),g=p==="channelsLast",y=g?0:1,b=g?1:2,v="";for(let x=0;x<=1;x++)for(let N=0;N<=1;N++)v+=`
           blockIndex = rc.y + ${N};
           pos = rc.x + ${x};
 
@@ -2166,7 +2166,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
 
         ${f.output} = result;
       }
-    `}};function wS({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=a.texData.get(e.dataId),u=n.inChannels,p=l[0]*l[1]*l[2],d=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[],b=(p===1||d===1)&&u>uS,v=l[2]%2!=0&&!!c.isPacked;if(b||!ee().getBool("WEBGL_LAZILY_UNPACK")||!ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!v){let x=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],N=be({inputs:{x:e},backend:a,attrs:{shape:[1,x,n.inChannels]}}),T=be({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),C=Um({a:N,b:T,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=be({inputs:{x:C},backend:a,attrs:{shape:n.outShape}}),y.push(N),y.push(T),y.push(C)}else{let x=h?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),N={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k.assert(Lm(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);let C=be({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(C);let $=Um({a:N,b:C,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),F=a.texData.get($.dataId);k.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=T,F.shape=n.outShape,g=Bn({inputs:{x:$},backend:a}),g.shape=n.outShape,y.push($)}for(let x of y)a.disposeIntermediateTensorInfo(x);return g}function kS({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:p,outHeight:d,dataFormat:h}=n,m=h==="channelsLast",f=l*c*u,g=d*p,y=[f,g],b=!0,v=!1,x=[],N=be({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),T=be({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});x.push(N),x.push(T);let C=new yY(y,N.shape,n),$=a.runWebGLProgram(C,[N],"float32"),F=be({inputs:{x:$},backend:a,attrs:{shape:[1,y[0],y[1]]}});x.push($),x.push(F);let O=r!=null,B=s!=null,W=o==="leakyrelu",G=o?Wm(o,!0):null,q=new aS(F.shape,T.shape,[1,g,n.outChannels],b,v,O,G,B,W),H=[F,T];if(r&&H.push(r),B&&H.push(s),W){let J=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));H.push(J),x.push(J)}let K=a.runWebGLProgram(q,H,"float32"),X=m?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],ne=be({inputs:{x:K},backend:a,attrs:{shape:X}});x.push(K);for(let J of x)a.disposeIntermediateTensorInfo(J);return ne}function bY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=a,p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!1,p),h;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"))h=wS({x:r,filter:s,convInfo:d,backend:n});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=kS({x:r,filter:s,convInfo:d,backend:n});else{let f=new vS(d);h=n.runWebGLProgram(f,[r,s],"float32")}let m=be({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),m}var xY={kernelName:Fs,backendName:"webgl",kernelFunc:bY},vY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function CS({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=a.texData.get(e.dataId),u=n.inChannels,p=l[0]*l[1]*l[2],d=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[],b=(p===1||d===1)&&u>fS,v=l[2]%2!=0&&!!c.isPacked;if(b||!ee().getBool("WEBGL_LAZILY_UNPACK")||!ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!v){let x=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],N=be({inputs:{x:e},backend:a,attrs:{shape:[1,x,n.inChannels]}}),T=be({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),C=jm({a:N,b:T,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=be({inputs:{x:C},backend:a,attrs:{shape:n.outShape}}),y.push(N),y.push(T),y.push(C)}else{let x=h?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),N={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k.assert(Wm(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);let C=be({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(C);let F=jm({a:N,b:C,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),$=a.texData.get(F.dataId);k.assert($.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=T,$.shape=n.outShape,g=Wn({inputs:{x:F},backend:a}),g.shape=n.outShape,y.push(F)}for(let x of y)a.disposeIntermediateTensorInfo(x);return g}function _S({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:p,outHeight:d,dataFormat:h}=n,m=h==="channelsLast",f=l*c*u,g=d*p,y=[f,g],b=!0,v=!1,x=[],N=be({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),T=be({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});x.push(N),x.push(T);let C=new vY(y,N.shape,n),F=a.runWebGLProgram(C,[N],"float32"),$=be({inputs:{x:F},backend:a,attrs:{shape:[1,y[0],y[1]]}});x.push(F),x.push($);let P=r!=null,B=s!=null,W=o==="leakyrelu",G=o?Gm(o,!0):null,q=new uS($.shape,T.shape,[1,g,n.outChannels],b,v,P,G,B,W),j=[$,T];if(r&&j.push(r),B&&j.push(s),W){let J=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));j.push(J),x.push(J)}let X=a.runWebGLProgram(q,j,"float32"),K=m?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],ne=be({inputs:{x:X},backend:a,attrs:{shape:K}});x.push(X);for(let J of x)a.disposeIntermediateTensorInfo(J);return ne}function wY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=a,p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!1,p),h;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"))h=CS({x:r,filter:s,convInfo:d,backend:n});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=_S({x:r,filter:s,convInfo:d,backend:n});else{let f=new SS(d);h=n.runWebGLProgram(f,[r,s],"float32")}let m=be({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),m}var kY={kernelName:$s,backendName:"webgl",kernelFunc:wY},IY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2208,7 +2208,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},wY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,c=s?2:3,u=s?3:1;this.userCode=`
+    `}},NY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,c=s?2:3,u=s?3:1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -2261,7 +2261,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},kY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
+    `}},TY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -2303,7 +2303,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},IY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=a-1-e.padInfo.left;this.userCode=`
+    `}},SY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=a-1-e.padInfo.left;this.userCode=`
       const ivec3 pads = ivec3(${o}, ${l}, ${c});
 
       void main() {
@@ -2360,12 +2360,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function NY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=a,p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,u,i,1,o,c,!1,p),h=new vY(d);return n.runWebGLProgram(h,[r,s],"float32")}var TY={kernelName:Id,backendName:"webgl",kernelFunc:NY};function SY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=a,p=_.convertConv2DDataFormat(c),d=_.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),h=new wY(d);return n.runWebGLProgram(h,[r,s],"float32")}var CY={kernelName:$s,backendName:"webgl",kernelFunc:SY};function _Y(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=_.computeConv3DInfo(r.shape,s.shape,i,l,o),u=new gY(c);return n.runWebGLProgram(u,[r,s],"float32")}var EY={kernelName:dc,backendName:"webgl",kernelFunc:_Y};function AY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,c=_.computeConv3DInfo(r.shape,l,i,1,o),u=new kY(c);return n.runWebGLProgram(u,[r,s],"float32")}var FY={kernelName:Nd,backendName:"webgl",kernelFunc:AY};function $Y(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,c=_.computeConv3DInfo(l,s.shape,o,1,i),u=new IY(c);return n.runWebGLProgram(u,[r,s],"float32")}var DY={kernelName:Td,backendName:"webgl",kernelFunc:$Y},RY=nS+`
+    `}};function CY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=a,p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,u,i,1,o,c,!1,p),h=new IY(d);return n.runWebGLProgram(h,[r,s],"float32")}var _Y={kernelName:Sd,backendName:"webgl",kernelFunc:CY};function EY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=a,p=_.convertConv2DDataFormat(c),d=_.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),h=new NY(d);return n.runWebGLProgram(h,[r,s],"float32")}var AY={kernelName:Ds,backendName:"webgl",kernelFunc:EY};function FY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=_.computeConv3DInfo(r.shape,s.shape,i,l,o),u=new xY(c);return n.runWebGLProgram(u,[r,s],"float32")}var $Y={kernelName:pc,backendName:"webgl",kernelFunc:FY};function DY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,c=_.computeConv3DInfo(r.shape,l,i,1,o),u=new TY(c);return n.runWebGLProgram(u,[r,s],"float32")}var RY={kernelName:Cd,backendName:"webgl",kernelFunc:DY};function MY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,c=_.computeConv3DInfo(l,s.shape,o,1,i),u=new SY(c);return n.runWebGLProgram(u,[r,s],"float32")}var PY={kernelName:_d,backendName:"webgl",kernelFunc:MY},OY=lS+`
   return cos(x);
-`,MY=Ze({opSnippet:RY}),PY={kernelName:Ds,backendName:"webgl",kernelFunc:MY},OY=`
+`,LY=Ze({opSnippet:OY}),zY={kernelName:Rs,backendName:"webgl",kernelFunc:LY},BY=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,LY=Ze({opSnippet:OY}),zY={kernelName:Po,backendName:"webgl",kernelFunc:LY},BY=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,p]=n;this.outputShape=[c,u,p,l];let d=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,v,x]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
+`,WY=Ze({opSnippet:BY}),VY={kernelName:Oo,backendName:"webgl",kernelFunc:WY},UY=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,p]=n;this.outputShape=[c,u,p,l];let d=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,v,x]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
       const float height_ratio = float(${f});
       const float width_ratio = float(${b});
       void main() {
@@ -2426,21 +2426,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
           setOutput(newValue);
         }
       }
-    `}},WY=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=a,u=new BY(r.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[r,s,i],"float32")},VY={kernelName:Oo,backendName:"webgl",kernelFunc:WY},TS=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${IS(a,"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 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
+    `}},GY=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=a,u=new UY(r.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[r,s,i],"float32")},HY={kernelName:Lo,backendName:"webgl",kernelFunc:GY},FS=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${ES(a,"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() {
         ${ft(a)} coords = getOutputCoords();
-        int end = ${NS(a,"coords")};
+        int end = ${AS(a,"coords")};
         float val = ${r};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${NS(a,"coords")} = idx;
-          val += getX(${IS(a,"coords")});
+          ${AS(a,"coords")} = idx;
+          val += getX(${ES(a,"coords")});
         }
         setOutput(val);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function IS(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 ${e} is not yet supported`)}function NS(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function UY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,c=_.getAxesPermutation([s],l),u=r;c!=null&&(u=Tn({inputs:{x:r},backend:n,attrs:{perm:c}}));let p=_.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let d=r.shape[p],h=Bn({inputs:{x:u},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new TS(u.shape,!1,o),g=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new TS(u.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(c!=null){let m=_.getUndoAxesPermutation(c),f=Tn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),f}return h}var GY={kernelName:Rs,backendName:"webgl",kernelFunc:UY};function HY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(s.dataId),u=UT(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=x8(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var jY={kernelName:Sd,backendName:"webgl",kernelFunc:HY},qY=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 ES(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 ${e} is not yet supported`)}function AS(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function jY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,c=_.getAxesPermutation([s],l),u=r;c!=null&&(u=Tn({inputs:{x:r},backend:n,attrs:{perm:c}}));let p=_.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let d=u.shape[p],h=Wn({inputs:{x:u},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new FS(u.shape,!1,o),g=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new FS(u.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(c!=null){let m=_.getUndoAxesPermutation(c),f=Tn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),f}return h}var qY={kernelName:Ms,backendName:"webgl",kernelFunc:jY};function KY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(s.dataId),u=XT(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=I8(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var XY={kernelName:Ed,backendName:"webgl",kernelFunc:KY},YY=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];
@@ -2459,7 +2459,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={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 KY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=new qY(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var XY={kernelName:Lo,backendName:"webgl",kernelFunc:KY},SS=class{constructor(e,t=!1,n=null,a=!1,r=!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,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,g="",y="";n&&(a?g=`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 ZY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=new YY(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var JY={kernelName:zo,backendName:"webgl",kernelFunc:ZY},$S=class{constructor(e,t=!1,n=null,a=!1,r=!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,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,g="",y="";n&&(a?g=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:r?g=`float activation(float a) {
@@ -2515,7 +2515,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         ${y}
         setOutput(result);
       }
-    `}},CS=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=m,g="int xR; int xC; int xCOffset;";for(let x=0;x<h;x++)for(let N=0;N<m;N++)g+=`
+    `}},DS=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=m,g="int xR; int xC; int xCOffset;";for(let x=0;x<h;x++)for(let N=0;N<m;N++)g+=`
           vec4 xTexelR${x}C${N*2} = vec4(0.);
           vec4 wR${x}C${N} = vec4(0.);
           vec4 xR${x}C${N} = vec4(0.);`;for(let x=0;x<h;x++)for(let N=0;N<f;N++){let T=N*2;if(g+=`
@@ -2668,7 +2668,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         ${b}
         setOutput(result);
       }
-    `}};function YY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=a,u=l;u==null&&(u=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=_.computeConv2DInfo(r.shape,s.shape,i,u,o,c,!0),d;return ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?d=new CS(p):d=new SS(p),n.runWebGLProgram(d,[r,s],"float32")}var ZY={kernelName:Ms,backendName:"webgl",kernelFunc:YY},JY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
+    `}};function QY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=a,u=l;u==null&&(u=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=_.computeConv2DInfo(r.shape,s.shape,i,u,o,c,!0),d;return ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?d=new DS(p):d=new $S(p),n.runWebGLProgram(d,[r,s],"float32")}var e7={kernelName:Ps,backendName:"webgl",kernelFunc:QY},t7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2703,7 +2703,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},QY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
+    `}},n7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
       const ivec2 pads = ivec2(${s}, ${i});
 
       void main() {
@@ -2748,13 +2748,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function e7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=a,p=_.computeConv2DInfo(r.shape,u,i,o,l,c,!0),d=new JY(p);return n.runWebGLProgram(d,[r,s],"float32")}var t7={kernelName:Cd,backendName:"webgl",kernelFunc:e7};function n7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=a,p=_.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new QY(p);return n.runWebGLProgram(d,[r,s],"float32")}var a7={kernelName:_d,backendName:"webgl",kernelFunc:n7},r7=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function a7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=a,p=_.computeConv2DInfo(r.shape,u,i,o,l,c,!0),d=new t7(p);return n.runWebGLProgram(d,[r,s],"float32")}var r7={kernelName:Ad,backendName:"webgl",kernelFunc:a7};function s7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=a,p=_.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new n7(p);return n.runWebGLProgram(d,[r,s],"float32")}var i7={kernelName:Fd,backendName:"webgl",kernelFunc:s7},o7=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 s7(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=be({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new r7(s),l=n.runWebGLProgram(o,[i],i.dtype),c=be({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var i7={kernelName:Ed,backendName:"webgl",kernelFunc:s7},o7=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:p}=a;this.userCode=`
+    `}};function l7(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=be({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new o7(s),l=n.runWebGLProgram(o,[i],i.dtype),c=be({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var u7={kernelName:$d,backendName:"webgl",kernelFunc:l7},c7=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:p}=a;this.userCode=`
       const ivec2 strides = ivec2(${r}, ${s});
       const ivec2 pads = ivec2(${u}, ${p});
       const float neg_infinity = -3.4e38;
@@ -2792,7 +2792,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         float result = curVal;
         setOutput(result);
       }
-    `}};function l7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=_.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),u,p=new o7(c);u=n.runWebGLProgram(p,[r,s],"float32");let d=be({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var u7={kernelName:hc,backendName:"webgl",kernelFunc:l7},c7="return (x >= 0.0) ? x : (exp(x) - 1.0);",p7=`
+    `}};function p7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=_.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),u,p=new c7(c);u=n.runWebGLProgram(p,[r,s],"float32");let d=be({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var d7={kernelName:dc,backendName:"webgl",kernelFunc:p7},h7="return (x >= 0.0) ? x : (exp(x) - 1.0);",m7=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -2801,12 +2801,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,d7=Ze({opSnippet:c7,packedOpSnippet:p7}),h7={kernelName:zo,backendName:"webgl",kernelFunc:d7},m7="return (b >= 1.0) ? a : a * (b + 1.0);",f7=`
+`,f7=Ze({opSnippet:h7,packedOpSnippet:m7}),g7={kernelName:Bo,backendName:"webgl",kernelFunc:f7},y7="return (b >= 1.0) ? a : a * (b + 1.0);",b7=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,g7=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new wp(f7,a.shape,r.shape):new hu(m7,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},y7={kernelName:$d,backendName:"webgl",kernelFunc:g7},b7=`
+`,x7=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new kp(b7,a.shape,r.shape):new hu(y7,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},v7={kernelName:Md,backendName:"webgl",kernelFunc:x7},w7=`
   return vec4(equal(a, b));
-`,x7="return float(a == b);",v7=nn({opSnippet:x7,packedOpSnippet:b7,dtype:"bool"}),w7={kernelName:Wo,backendName:"webgl",kernelFunc:v7},k7=`
+`,k7="return float(a == b);",I7=nn({opSnippet:k7,packedOpSnippet:w7,dtype:"bool"}),N7={kernelName:Vo,backendName:"webgl",kernelFunc:I7},T7=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -2821,7 +2821,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
   x = abs(x);
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
-`,I7=Ze({opSnippet:k7}),N7={kernelName:Bo,backendName:"webgl",kernelFunc:I7},_S="return exp(x);",ES=Ze({opSnippet:_S,packedOpSnippet:_S,cpuKernelImpl:k8}),T7={kernelName:Os,backendName:"webgl",kernelFunc:ES};function Uv(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),be({inputs:{x:s},backend:a,attrs:{shape:o}})}var S7={kernelName:Vo,backendName:"webgl",kernelFunc:Uv},AS="return exp(x) - 1.0;",C7=Ze({opSnippet:AS,packedOpSnippet:AS,cpuKernelImpl:I8}),_7={kernelName:Uo,backendName:"webgl",kernelFunc:C7},FS=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.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 ${e}.`);this.userCode=`
+`,S7=Ze({opSnippet:T7}),C7={kernelName:Wo,backendName:"webgl",kernelFunc:S7},RS="return exp(x);",MS=Ze({opSnippet:RS,packedOpSnippet:RS,cpuKernelImpl:S8}),_7={kernelName:Ls,backendName:"webgl",kernelFunc:MS};function Xv(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),be({inputs:{x:s},backend:a,attrs:{shape:o}})}var E7={kernelName:Uo,backendName:"webgl",kernelFunc:Xv},PS="return exp(x) - 1.0;",A7=Ze({opSnippet:PS,packedOpSnippet:PS,cpuKernelImpl:C8}),F7={kernelName:Go,backendName:"webgl",kernelFunc:A7},OS=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.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 = ${r};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -2854,13 +2854,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function $S(e,t,n){let a=n.texData.get(e.dataId),r=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=be({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new FS("real",l,t),u=new FS("imag",l,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=ps({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let f=be({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function E7(e){let{inputs:t,backend:n}=e,{input:a}=t;return $S(a,!1,n)}var A7={kernelName:Dd,backendName:"webgl",kernelFunc:E7},F7=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function LS(e,t,n){let a=n.texData.get(e.dataId),r=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=be({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new OS("real",l,t),u=new OS("imag",l,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=ps({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let f=be({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function $7(e){let{inputs:t,backend:n}=e,{input:a}=t;return LS(a,!1,n)}var D7={kernelName:Pd,backendName:"webgl",kernelFunc:$7},R7=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 Gv(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||k.inferDtype(r),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new F7(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var $7={kernelName:mc,backendName:"webgl",kernelFunc:Gv},D7=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 Yv(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||k.inferDtype(r),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new R7(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var M7={kernelName:hc,backendName:"webgl",kernelFunc:Yv},P7=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];
@@ -2874,7 +2874,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
           }
           setOutput(outputValue);
         }
-    `}},R7={kernelName:Go,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new D7(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},DS="return floor(x);",M7=Ze({opSnippet:DS,packedOpSnippet:DS,cpuKernelImpl:N8}),P7={kernelName:Ls,backendName:"webgl",kernelFunc:M7},O7=`
+    `}},O7={kernelName:Ho,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new P7(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},zS="return floor(x);",L7=Ze({opSnippet:zS,packedOpSnippet:zS,cpuKernelImpl:_8}),z7={kernelName:zs,backendName:"webgl",kernelFunc:L7},B7=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -2884,7 +2884,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
   } else {
     return NAN;
   }
-`,L7=`
+`,W7=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -2905,7 +2905,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,z7=nn({opSnippet:O7,packedOpSnippet:L7,dtype:"int32"}),B7={kernelName:zs,backendName:"webgl",kernelFunc:z7},W7=class{constructor(e){this.variableNames=["A"];let t=hn(),[n,a]=e;this.outputShape=e,this.userCode=`
+`,V7=nn({opSnippet:B7,packedOpSnippet:W7,dtype:"int32"}),U7={kernelName:Bs,backendName:"webgl",kernelFunc:V7},G7=class{constructor(e){this.variableNames=["A"];let t=hn(),[n,a]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2927,7 +2927,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}},V7=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=hn(),[n,a]=e;this.outputShape=e,this.userCode=`
+    `}},H7=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=hn(),[n,a]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2961,7 +2961,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
 
         ${t.output} = result;
       }
-    `}},G7={kernelName:qd,backendName:"webgl",kernelFunc:U7},fu;function U7(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,c],d=[u,c,s];(o||i||l)&&(fu==null&&(fu=document.createElement("canvas").getContext("2d")),fu.canvas.width=c,fu.canvas.height=u,fu.drawImage(r,0,0,c,u),r=fu.canvas);let h=n.makeTensorInfo(p,"int32");n.texData.get(h.dataId).usage=Qn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(h.dataId),r);let m=ee().getBool("WEBGL_PACK")?new V7(d):new W7(d),f=n.runWebGLProgram(m,[h],"int32");return n.disposeData(h.dataId),f}function H7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=_.convertConv2DDataFormat(u),g=_.computeConv2DInfo(r.shape,s.shape,l,p,c,d,!1,f),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=wS({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=kS({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let x=i!=null,N=o!=null,T=h==="leakyrelu",C=h?Wm(h,!1):null,$=new vS(g,x,C,N,T),F=[r,s];if(i&&F.push(i),o&&F.push(o),T){let O=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));F.push(O),b.push(O)}y=n.runWebGLProgram($,F,"float32")}let v=be({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var j7={kernelName:yi,backendName:"webgl",kernelFunc:H7};function q7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=a,m=[],f=u;f==null&&(f=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=_.computeConv2DInfo(r.shape,s.shape,l,f,c,p,!0),y=ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=d?Wm(d,y):null,v=[r,s],x=i!=null,N=o!=null,T=d==="leakyrelu";if(x&&v.push(i),N&&v.push(o),T){let F=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));v.push(F),m.push(F)}let C;y?C=new CS(g,x,b,N,T):C=new SS(g,x,b,N,T);let $=n.runWebGLProgram(C,v,"float32");return m.forEach(F=>n.disposeIntermediateTensorInfo(F)),$}var K7={kernelName:bi,backendName:"webgl",kernelFunc:q7},X7=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=ft(t.length),r=ft(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}},q7={kernelName:Yd,backendName:"webgl",kernelFunc:j7},fu;function j7(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,c],d=[u,c,s];(o||i||l)&&(fu==null&&(fu=document.createElement("canvas").getContext("2d")),fu.canvas.width=c,fu.canvas.height=u,fu.drawImage(r,0,0,c,u),r=fu.canvas);let h=n.makeTensorInfo(p,"int32");n.texData.get(h.dataId).usage=Qn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(h.dataId),r);let m=ee().getBool("WEBGL_PACK")?new H7(d):new G7(d),f=n.runWebGLProgram(m,[h],"int32");return n.disposeData(h.dataId),f}function K7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=_.convertConv2DDataFormat(u),g=_.computeConv2DInfo(r.shape,s.shape,l,p,c,d,!1,f),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=CS({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=_S({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let x=i!=null,N=o!=null,T=h==="leakyrelu",C=h?Gm(h,!1):null,F=new SS(g,x,C,N,T),$=[r,s];if(i&&$.push(i),o&&$.push(o),T){let P=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));$.push(P),b.push(P)}y=n.runWebGLProgram(F,$,"float32")}let v=be({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var X7={kernelName:xi,backendName:"webgl",kernelFunc:K7};function Y7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=a,m=[],f=u;f==null&&(f=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=_.computeConv2DInfo(r.shape,s.shape,l,f,c,p,!0),y=ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=d?Gm(d,y):null,v=[r,s],x=i!=null,N=o!=null,T=d==="leakyrelu";if(x&&v.push(i),N&&v.push(o),T){let $=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));v.push($),m.push($)}let C;y?C=new DS(g,x,b,N,T):C=new $S(g,x,b,N,T);let F=n.runWebGLProgram(C,v,"float32");return m.forEach($=>n.disposeIntermediateTensorInfo($)),F}var Z7={kernelName:vi,backendName:"webgl",kernelFunc:Y7},J7=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=ft(t.length),r=ft(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${a} strides = ${a}(${this.strides});
          void main() {
           ${r} coords = getOutputCoords();
@@ -2972,21 +2972,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}};function Y7(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],[o,l,c,u]=_.prepareAndValidate(a,r),p=be({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),d=be({inputs:{x:a},backend:n,attrs:{shape:[k.sizeFromShape(a.shape)/c,c]}}),h=new X7(i,u,[l,c]),m=n.runWebGLProgram(h,[d,p],d.dtype),f=be({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var Z7={kernelName:jo,backendName:"webgl",kernelFunc:Y7},Q7=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ft(this.rank),a=J7(e,2);this.userCode=`
+      `}};function Q7(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],[o,l,c,u]=_.prepareAndValidate(a,r),p=be({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),d=be({inputs:{x:a},backend:n,attrs:{shape:[k.sizeFromShape(a.shape)/c,c]}}),h=new J7(i,u,[l,c]),m=n.runWebGLProgram(h,[d,p],d.dtype),f=be({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var eZ={kernelName:qo,backendName:"webgl",kernelFunc:Q7},nZ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ft(this.rank),a=tZ(e,2);this.userCode=`
       void main() {
         ${n} resRC = getOutputCoords();
         setOutput(getA(${a}));
       }
-    `}};function J7(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("int(getIndices(resRC.x, resRC.z))"):a.push(`${n[r]}`);return a.join()}function eZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=k.sizeFromShape(s.shape),p=[],d=be({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=be({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});p.push(d),p.push(h);let m=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.bufferSync(h),v=n.bufferSync(d),x=T8(v,b,m);return p.forEach(N=>n.disposeIntermediateTensorInfo(N)),n.makeTensorInfo(c.outputShape,x.dtype,x.values)}let f=new Q7(d.shape,m),g=n.runWebGLProgram(f,[d,h],d.dtype);p.push(g);let y=be({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var tZ={kernelName:Ho,backendName:"webgl",kernelFunc:eZ},nZ="return float(a > b);",aZ=`
+    `}};function tZ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("int(getIndices(resRC.x, resRC.z))"):a.push(`${n[r]}`);return a.join()}function aZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=k.sizeFromShape(s.shape),p=[],d=be({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=be({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});p.push(d),p.push(h);let m=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.bufferSync(h),v=n.bufferSync(d),x=E8(v,b,m);return p.forEach(N=>n.disposeIntermediateTensorInfo(N)),n.makeTensorInfo(c.outputShape,x.dtype,x.values)}let f=new nZ(d.shape,m),g=n.runWebGLProgram(f,[d,h],d.dtype);p.push(g);let y=be({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var rZ={kernelName:jo,backendName:"webgl",kernelFunc:aZ},sZ="return float(a > b);",iZ=`
   return vec4(greaterThan(a, b));
-`,rZ=nn({opSnippet:nZ,packedOpSnippet:aZ,cpuKernelImpl:S8,dtype:"bool"}),sZ={kernelName:qo,backendName:"webgl",kernelFunc:rZ},iZ="return float(a >= b);",oZ=`
+`,oZ=nn({opSnippet:sZ,packedOpSnippet:iZ,cpuKernelImpl:A8,dtype:"bool"}),lZ={kernelName:Ko,backendName:"webgl",kernelFunc:oZ},uZ="return float(a >= b);",cZ=`
   return vec4(greaterThanEqual(a, b));
-`,lZ=nn({opSnippet:iZ,packedOpSnippet:oZ,dtype:"bool"}),uZ={kernelName:Ws,backendName:"webgl",kernelFunc:lZ};function cZ(e){let{inputs:t,backend:n}=e,{input:a}=t;return $S(a,!0,n)}var pZ={kernelName:Rd,backendName:"webgl",kernelFunc:cZ},dZ="return float(!isnan(x) && !isinf(x));",hZ=Ze({opSnippet:dZ,dtype:"bool"}),mZ={kernelName:Xo,backendName:"webgl",kernelFunc:hZ},fZ="return float(isinf(x));",gZ=Ze({opSnippet:fZ,dtype:"bool"}),yZ={kernelName:Yo,backendName:"webgl",kernelFunc:gZ},bZ="return float(isnan(x));",xZ=Ze({opSnippet:bZ,dtype:"bool"}),vZ={kernelName:Zo,backendName:"webgl",kernelFunc:xZ},wZ="return float(a < b);",kZ=`
+`,pZ=nn({opSnippet:uZ,packedOpSnippet:cZ,dtype:"bool"}),dZ={kernelName:Vs,backendName:"webgl",kernelFunc:pZ};function hZ(e){let{inputs:t,backend:n}=e,{input:a}=t;return LS(a,!0,n)}var mZ={kernelName:Od,backendName:"webgl",kernelFunc:hZ},fZ="return float(!isnan(x) && !isinf(x));",gZ=Ze({opSnippet:fZ,dtype:"bool"}),yZ={kernelName:Xo,backendName:"webgl",kernelFunc:gZ},bZ="return float(isinf(x));",xZ=Ze({opSnippet:bZ,dtype:"bool"}),vZ={kernelName:Yo,backendName:"webgl",kernelFunc:xZ},wZ="return float(isnan(x));",kZ=Ze({opSnippet:wZ,dtype:"bool"}),IZ={kernelName:Zo,backendName:"webgl",kernelFunc:kZ},NZ="return float(a < b);",TZ=`
   return vec4(lessThan(a, b));
-`,IZ=nn({opSnippet:wZ,packedOpSnippet:kZ,cpuKernelImpl:C8,dtype:"bool"}),NZ={kernelName:Jo,backendName:"webgl",kernelFunc:IZ},TZ="return float(a <= b);",SZ=`
+`,SZ=nn({opSnippet:NZ,packedOpSnippet:TZ,cpuKernelImpl:F8,dtype:"bool"}),CZ={kernelName:Jo,backendName:"webgl",kernelFunc:SZ},_Z="return float(a <= b);",EZ=`
   return vec4(lessThanEqual(a, b));
-`,CZ=nn({opSnippet:TZ,packedOpSnippet:SZ,dtype:"bool"}),_Z={kernelName:Qo,backendName:"webgl",kernelFunc:CZ};function EZ(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=_8(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var AZ={kernelName:Pd,backendName:"webgl",kernelFunc:EZ},FZ=`if (x < 0.0) return NAN;
-  return log(x);`,$Z=`
+`,AZ=nn({opSnippet:_Z,packedOpSnippet:EZ,dtype:"bool"}),FZ={kernelName:Qo,backendName:"webgl",kernelFunc:AZ};function $Z(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=$8(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var DZ={kernelName:zd,backendName:"webgl",kernelFunc:$Z},RZ=`if (x < 0.0) return NAN;
+  return log(x);`,MZ=`
   vec4 result = log(x);
   vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
   result.r = isNaN.r == 1.0 ? NAN : result.r;
@@ -2995,16 +2995,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
   result.a = isNaN.a == 1.0 ? NAN : result.a;
 
   return result;
-`,DZ=Ze({opSnippet:FZ,packedOpSnippet:$Z,cpuKernelImpl:E8}),RZ={kernelName:Us,backendName:"webgl",kernelFunc:DZ},MZ="return log(1.0 + x);",PZ=Ze({opSnippet:MZ}),OZ={kernelName:el,backendName:"webgl",kernelFunc:PZ},LZ="return float(a >= 1.0 && b >= 1.0);",zZ=`
+`,PZ=Ze({opSnippet:RZ,packedOpSnippet:MZ,cpuKernelImpl:D8}),OZ={kernelName:Hs,backendName:"webgl",kernelFunc:PZ},LZ="return log(1.0 + x);",zZ=Ze({opSnippet:LZ}),BZ={kernelName:el,backendName:"webgl",kernelFunc:zZ},WZ="return float(a >= 1.0 && b >= 1.0);",VZ=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,BZ=nn({opSnippet:LZ,packedOpSnippet:zZ,dtype:"bool"}),WZ={kernelName:tl,backendName:"webgl",kernelFunc:BZ},VZ="return float(!(x >= 1.0));",UZ=Ze({opSnippet:VZ}),GZ={kernelName:fc,backendName:"webgl",kernelFunc:UZ},HZ="return float(a >= 1.0 || b >= 1.0);",jZ=`
+`,UZ=nn({opSnippet:WZ,packedOpSnippet:VZ,dtype:"bool"}),GZ={kernelName:tl,backendName:"webgl",kernelFunc:UZ},HZ="return float(!(x >= 1.0));",jZ=Ze({opSnippet:HZ}),qZ={kernelName:mc,backendName:"webgl",kernelFunc:jZ},KZ="return float(a >= 1.0 || b >= 1.0);",XZ=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,qZ=nn({opSnippet:HZ,packedOpSnippet:jZ,dtype:"bool"}),KZ={kernelName:gc,backendName:"webgl",kernelFunc:qZ},XZ=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
+`,YZ=nn({opSnippet:KZ,packedOpSnippet:XZ,dtype:"bool"}),ZZ={kernelName:fc,backendName:"webgl",kernelFunc:YZ},JZ=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3023,7 +3023,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         float val = x * ${o};
         setOutput(val);
       }
-    `}},YZ=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
+    `}},QZ=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -3085,7 +3085,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         vec4 result = xAtOutputCoords * ${o};
         setOutput(result);
       }
-    `}},ZZ=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,c=ee().getBool("WEBGL_PACK_NORMALIZATION")?new YZ(r.shape,s,i,o,l):new XZ(r.shape,s,i,o,l);return n.runWebGLProgram(c,[r],r.dtype)},JZ={kernelName:yc,backendName:"webgl",kernelFunc:ZZ},QZ=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=`
+    `}},e9=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,c=ee().getBool("WEBGL_PACK_NORMALIZATION")?new QZ(r.shape,s,i,o,l):new JZ(r.shape,s,i,o,l);return n.runWebGLProgram(c,[r],r.dtype)},t9={kernelName:gc,backendName:"webgl",kernelFunc:e9},n9=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3140,14 +3140,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
       }
       setOutput(result);
       }
-    `}},e9=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=a,p=new QZ(r.shape,o,l,c,u);return n.runWebGLProgram(p,[r,s,i],r.dtype)},t9={kernelName:Od,backendName:"webgl",kernelFunc:e9};function n9(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=ji(i,e.dtype,"max",a),l=be({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function RS(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=u!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let b=n.texData.get(h.dataId).values,v=new Array(o);for(let T=0;T<v.length;T++)v[T]=r.shape[u[T]];let x=Ov(b,r.shape,r.dtype,u,v);h=n.makeTensorInfo(v,r.dtype);let N=n.texData.get(h.dataId);N.values=x}else h=Vm(r,u,n);c=_.getInnerMostAxes(c.length,o)}_.assertAxesAreInnerMostDims("max",c,o);let[m,f]=_.computeOutAndReduceShapes(h.shape,c),g=m;i&&(g=_.expandShapeToKeepDim(m,l));let y;if(d){let b=n.texData.get(h.dataId).values,v=A8(b,k.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let x=n.texData.get(y.dataId);x.values=v}else y=n9(h,f,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var a9={kernelName:Gs,backendName:"webgl",kernelFunc:RS},r9=ZT+`
+    `}},a9=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=a,p=new n9(r.shape,o,l,c,u);return n.runWebGLProgram(p,[r,s,i],r.dtype)},r9={kernelName:Bd,backendName:"webgl",kernelFunc:a9};function s9(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Ki(i,e.dtype,"max",a),l=be({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function BS(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=u!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let b=n.texData.get(h.dataId).values,v=new Array(o);for(let T=0;T<v.length;T++)v[T]=r.shape[u[T]];let x=Vv(b,r.shape,r.dtype,u,v);h=n.makeTensorInfo(v,r.dtype);let N=n.texData.get(h.dataId);N.values=x}else h=Hm(r,u,n);c=_.getInnerMostAxes(c.length,o)}_.assertAxesAreInnerMostDims("max",c,o);let[m,f]=_.computeOutAndReduceShapes(h.shape,c),g=m;i&&(g=_.expandShapeToKeepDim(m,l));let y;if(d){let b=n.texData.get(h.dataId).values,v=R8(b,k.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let x=n.texData.get(y.dataId);x.values=v}else y=s9(h,f,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var i9={kernelName:js,backendName:"webgl",kernelFunc:BS},o9=aS+`
   return max(a, b);
-`,s9=`
+`,l9=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+Bm+`
+  `+Um+`
   return result;
-`,i9=nn({opSnippet:r9,packedOpSnippet:s9,cpuKernelImpl:F8}),o9={kernelName:Hs,backendName:"webgl",kernelFunc:i9};function l9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;xp(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.computePool2DInfo(r.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Bn({inputs:{x:r},backend:n});let p=new kp(u,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var u9={kernelName:js,backendName:"webgl",kernelFunc:l9};function c9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=a,u=[1,1,1],p=_.computePool3DInfo(r.shape,s,i,u,o,c,l),d=new Bv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var p9={kernelName:bc,backendName:"webgl",kernelFunc:c9},d9=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=`
+`,u9=nn({opSnippet:o9,packedOpSnippet:l9,cpuKernelImpl:M8}),c9={kernelName:qs,backendName:"webgl",kernelFunc:u9};function p9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;vp(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.computePool2DInfo(r.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Wn({inputs:{x:r},backend:n});let p=new Ip(u,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var d9={kernelName:Ks,backendName:"webgl",kernelFunc:p9};function h9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=a,u=[1,1,1],p=_.computePool3DInfo(r.shape,s,i,u,o,c,l),d=new jv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var m9={kernelName:yc,backendName:"webgl",kernelFunc:h9},f9=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -3193,7 +3193,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},h9=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,p=l-1-e.padInfo.top,d=c-1-e.padInfo.left,h=o*l*c-1;this.userCode=`
+    `}},g9=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,p=l-1-e.padInfo.top,d=c-1-e.padInfo.left,h=o*l*c-1;this.userCode=`
       const ivec3 pads = ivec3(${u}, ${p}, ${d});
 
       void main() {
@@ -3257,14 +3257,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function m9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=a,p=[1,1,1],d=_.computePool3DInfo(i.shape,o,l,p,c,u),h=new Bv(d,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new h9(d),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var f9={kernelName:zd,backendName:"webgl",kernelFunc:m9};function g9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;xp([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:p}=a,d=_.computePool2DInfo(o.shape,l,c,1,u,p),h=!0,m=new kp(d,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new d9(d),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var y9={kernelName:Ld,backendName:"webgl",kernelFunc:g9};function b9(e,t,n,a){let r=new kp(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new kp(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var x9={kernelName:Bd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;k.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let c=[1,1];k.assert(_.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=_.computePool2DInfo(a.shape,r,s,c,i),[p,d]=b9(a,o,u,l);return[p,d]}};function v9(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=ji(i,"float32","mean",a),l=be({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var w9={kernelName:qs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=_.getAxesPermutation(c,o),p=u!=null,d=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(d){let v=i.texData.get(m.dataId).values,x=new Array(o);for(let C=0;C<x.length;C++)x[C]=a.shape[u[C]];let N=Ov(v,a.shape,a.dtype,u,x);m=i.makeTensorInfo(x,a.dtype);let T=i.texData.get(m.dataId);T.values=N}else m=Vm(a,u,i);h.push(m),c=_.getInnerMostAxes(c.length,o)}_.assertAxesAreInnerMostDims("sum",c,o);let[f,g]=_.computeOutAndReduceShapes(m.shape,c),y=f;r&&(y=_.expandShapeToKeepDim(f,l));let b=v9(m,g,y,i);for(let v of h)i.disposeIntermediateTensorInfo(v);return b}};function k9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=r;u!=null&&(p=Tn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,r.shape.length)),_.assertAxesAreInnerMostDims("min",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=be({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=ji(f,f.dtype,"min",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=be({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var I9={kernelName:Ks,backendName:"webgl",kernelFunc:k9},N9=ZT+`
+    `}};function y9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=a,p=[1,1,1],d=_.computePool3DInfo(i.shape,o,l,p,c,u),h=new jv(d,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new g9(d),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var b9={kernelName:Vd,backendName:"webgl",kernelFunc:y9};function x9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;vp([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:p}=a,d=_.computePool2DInfo(o.shape,l,c,1,u,p),h=!0,m=new Ip(d,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new f9(d),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var v9={kernelName:Wd,backendName:"webgl",kernelFunc:x9};function w9(e,t,n,a){let r=new Ip(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Ip(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var k9={kernelName:Ud,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;k.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let c=[1,1];k.assert(_.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=_.computePool2DInfo(a.shape,r,s,c,i),[p,d]=w9(a,o,u,l);return[p,d]}};function I9(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Ki(i,"float32","mean",a),l=be({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var N9={kernelName:Xs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=_.getAxesPermutation(c,o),p=u!=null,d=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(d){let v=i.texData.get(m.dataId).values,x=new Array(o);for(let C=0;C<x.length;C++)x[C]=a.shape[u[C]];let N=Vv(v,a.shape,a.dtype,u,x);m=i.makeTensorInfo(x,a.dtype);let T=i.texData.get(m.dataId);T.values=N}else m=Hm(a,u,i);h.push(m),c=_.getInnerMostAxes(c.length,o)}_.assertAxesAreInnerMostDims("sum",c,o);let[f,g]=_.computeOutAndReduceShapes(m.shape,c),y=f;r&&(y=_.expandShapeToKeepDim(f,l));let b=I9(m,g,y,i);for(let v of h)i.disposeIntermediateTensorInfo(v);return b}};function T9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=r;u!=null&&(p=Tn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,r.shape.length)),_.assertAxesAreInnerMostDims("min",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=be({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Ki(f,f.dtype,"min",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=be({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var S9={kernelName:Ys,backendName:"webgl",kernelFunc:T9},C9=aS+`
   return min(a, b);
-`,T9=`
+`,_9=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+Bm+`
+  `+Um+`
   return result;
-`,S9=nn({opSnippet:N9,packedOpSnippet:T9,cpuKernelImpl:$8}),C9={kernelName:Xs,backendName:"webgl",kernelFunc:S9},_9=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let a=e.length,r=ft(a),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,a),l=n==="reflect"?0:1;if(a===1){this.userCode=`
+`,E9=nn({opSnippet:C9,packedOpSnippet:_9,cpuKernelImpl:P8}),A9={kernelName:Zs,backendName:"webgl",kernelFunc:E9},F9=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let a=e.length,r=ft(a),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,a),l=n==="reflect"?0:1;if(a===1){this.userCode=`
         int start = ${s};
         int end = ${i};
 
@@ -3293,7 +3293,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         ${r} coords = outC - start;
         setOutput(getX(${o}));
       }
-    `}},E9=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=ft(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=mn("rc",a),l=mn("source",a),c=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(a===1){let h=`
+    `}},$9=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=ft(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=mn("rc",a),l=mn("source",a),c=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(a===1){let h=`
         ${r} source = rc;
         if (source < start) {
           source = start * 2 - source - ${p};
@@ -3349,13 +3349,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={kernelNam
         ${d}
         setOutput(result);
       }
-    `}},A9=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new E9(a.shape,r,s):new _9(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},F9={kernelName:xc,backendName:"webgl",kernelFunc:A9},$9=`if (b == 0.0) return NAN;
-  return mod(a, b);`,D9=`
+    `}},D9=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $9(a.shape,r,s):new F9(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},R9={kernelName:bc,backendName:"webgl",kernelFunc:D9},M9=`if (b == 0.0) return NAN;
+  return mod(a, b);`,P9=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
-  `+Bm+`
+  `+Um+`
   return result;
-`,R9=nn({opSnippet:$9,packedOpSnippet:D9}),M9={kernelName:nl,backendName:"webgl",kernelFunc:R9},P9=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
+`,O9=nn({opSnippet:M9,packedOpSnippet:P9}),L9={kernelName:nl,backendName:"webgl",kernelFunc:O9},z9=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
       uniform float seed;
 
       void main() {
@@ -3377,11 +3377,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NX=Ze({opSnippet:IX}),TX={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)}}},O9=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},B9=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,L9=`
+return a / b;`,W9=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3399,14 +3399,14 @@ return a / b;`,L9=`
   }
 
   return result;
-`,MS=nn({opSnippet:O9,packedOpSnippet:L9,checkOutOfBounds:!0}),z9={kernelName:Ps,backendName:"webgl",kernelFunc:MS},PS="return a - b;",OS=nn({opSnippet:PS,packedOpSnippet:PS,supportsComplex:!0,cpuKernelImpl:B8}),B9={kernelName:hi,backendName:"webgl",kernelFunc:OS};function LS(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=RS({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=_.expandShapeToKeepDim(o.shape,i),c=be({inputs:{x:o},backend:n,attrs:{shape:l}}),u=OS({inputs:{a:r,b:c},backend:n}),p=ES({inputs:{x:u},backend:n}),d=zv({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=be({inputs:{x:d},backend:n,attrs:{shape:l}}),m=MS({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}var W9={kernelName:pi,backendName:"webgl",kernelFunc:LS};function V9(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:LS({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],p=new P9(c,u,s),d=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),h}var U9={kernelName:Wd,backendName:"webgl",kernelFunc:V9},zS="return -x;";function G9(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=R8(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new du(a.shape,zS):r=new cs(a.shape,zS),n.runWebGLProgram(r,[a],a.dtype)}var H9={kernelName:al,backendName:"webgl",kernelFunc:G9},j9=Za.nonMaxSuppressionV3Impl;function q9(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,c=n.readSync(r.dataId),u=n.readSync(s.dataId),{selectedIndices:p}=j9(c,u,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var K9={kernelName:sl,backendName:"webgl",kernelFunc:q9},X9=Za.nonMaxSuppressionV4Impl;function Y9(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d,validOutputs:h}=X9(u,p,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Z9={kernelName:il,backendName:"webgl",kernelFunc:Y9},J9=Za.nonMaxSuppressionV5Impl;function Q9(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=i,h=o,m=l,f=c,{selectedIndices:g,selectedScores:y}=J9(u,p,d,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var eJ={kernelName:ol,backendName:"webgl",kernelFunc:Q9},tJ=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`,WS=nn({opSnippet:B9,packedOpSnippet:W9,checkOutOfBounds:!0}),V9={kernelName:Os,backendName:"webgl",kernelFunc:WS},VS="return a - b;",US=nn({opSnippet:VS,packedOpSnippet:VS,supportsComplex:!0,cpuKernelImpl:G8}),U9={kernelName:fi,backendName:"webgl",kernelFunc:US};function GS(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=BS({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=_.expandShapeToKeepDim(o.shape,i),c=be({inputs:{x:o},backend:n,attrs:{shape:l}}),u=US({inputs:{a:r,b:c},backend:n}),p=MS({inputs:{x:u},backend:n}),d=Hv({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=be({inputs:{x:d},backend:n,attrs:{shape:l}}),m=WS({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}var G9={kernelName:hi,backendName:"webgl",kernelFunc:GS};function H9(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:GS({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],p=new z9(c,u,s),d=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),h}var j9={kernelName:Gd,backendName:"webgl",kernelFunc:H9},HS="return -x;";function q9(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=L8(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new du(a.shape,HS):r=new cs(a.shape,HS),n.runWebGLProgram(r,[a],a.dtype)}var K9={kernelName:al,backendName:"webgl",kernelFunc:q9},X9=Ja.nonMaxSuppressionV3Impl;function Y9(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,c=n.readSync(r.dataId),u=n.readSync(s.dataId),{selectedIndices:p}=X9(c,u,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var Z9={kernelName:sl,backendName:"webgl",kernelFunc:Y9},J9=Ja.nonMaxSuppressionV4Impl;function Q9(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d,validOutputs:h}=J9(u,p,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var eJ={kernelName:il,backendName:"webgl",kernelFunc:Q9},tJ=Ja.nonMaxSuppressionV5Impl;function nJ(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=i,h=o,m=l,f=c,{selectedIndices:g,selectedScores:y}=tJ(u,p,d,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var aJ={kernelName:ol,backendName:"webgl",kernelFunc:nJ},rJ=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${a}), float(${n}),
                       float(index == coords.y)));
       }
-    `}},nJ=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=k.sizeFromShape(r.shape),c=new tJ(l,s,i,o),u=be({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let d=[...r.shape,s],h=be({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},aJ={kernelName:Zs,backendName:"webgl",kernelFunc:nJ};function qm(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Np({inputs:{input:a},backend:n}),s=qm({inputs:{x:r},backend:n}),i=jm({inputs:{input:a},backend:n}),o=qm({inputs:{x:i},backend:n}),l=ps({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Gv({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var rJ={kernelName:Tl,backendName:"webgl",kernelFunc:qm};function BS(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=Np({inputs:{input:a},backend:n}),s=BS({inputs:{x:r},backend:n}),i=jm({inputs:{input:a},backend:n}),o=qm({inputs:{x:i},backend:n}),l=ps({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Gv({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var sJ={kernelName:ll,backendName:"webgl",kernelFunc:BS};function iJ(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Uv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{k.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let p=Uv({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),c=xS({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var oJ={kernelName:ul,backendName:"webgl",kernelFunc:iJ},lJ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let a=e.length,r=ft(a),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,a);if(a===1){this.userCode=`
+    `}},sJ=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=k.sizeFromShape(r.shape),c=new rJ(l,s,i,o),u=be({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let d=[...r.shape,s],h=be({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},iJ={kernelName:Qs,backendName:"webgl",kernelFunc:sJ};function Ym(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Tp({inputs:{input:a},backend:n}),s=Ym({inputs:{x:r},backend:n}),i=Xm({inputs:{input:a},backend:n}),o=Ym({inputs:{x:i},backend:n}),l=ps({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Yv({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var oJ={kernelName:Tl,backendName:"webgl",kernelFunc:Ym};function jS(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=Tp({inputs:{input:a},backend:n}),s=jS({inputs:{x:r},backend:n}),i=Xm({inputs:{input:a},backend:n}),o=Ym({inputs:{x:i},backend:n}),l=ps({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Yv({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var lJ={kernelName:ll,backendName:"webgl",kernelFunc:jS};function uJ(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Xv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{k.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let p=Xv({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),c=TS({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var cJ={kernelName:ul,backendName:"webgl",kernelFunc:uJ},pJ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let a=e.length,r=ft(a),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,a);if(a===1){this.userCode=`
         int start = ${s};
         int end = ${i};
 
@@ -3431,7 +3431,7 @@ return a / b;`,L9=`
           setOutput(getX(${o}));
         }
       }
-    `}},uJ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ft(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=mn("rc",a),l=mn("source",a),c=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
+    `}},dJ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ft(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=mn("rc",a),l=mn("source",a),c=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
        if(${c}) {
       `,a===1?"":`}
        rc = outputLoc;
@@ -3455,7 +3455,7 @@ return a / b;`,L9=`
         ${h}
         setOutput(result);
       }
-    `}},WS=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new uJ(r.shape,s,i):new lJ(r.shape,s,i);return n.runWebGLProgram(o,[r],r.dtype)},cJ={kernelName:Js,backendName:"webgl",kernelFunc:WS},pJ=`
+    `}},qS=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new dJ(r.shape,s,i):new pJ(r.shape,s,i);return n.runWebGLProgram(o,[r],r.dtype)},hJ={kernelName:ei,backendName:"webgl",kernelFunc:qS},mJ=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3464,7 +3464,7 @@ return a / b;`,L9=`
   }
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,dJ=`
+`,fJ=`
   // 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);
@@ -3478,11 +3478,11 @@ return a / b;`,L9=`
   result.a = isExpZero.a ? 1.0 : result.a;
 
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
-  `+Bm+`
+  `+Um+`
   return result;
-`,hJ=nn({opSnippet:pJ,packedOpSnippet:dJ}),mJ={kernelName:Qs,backendName:"webgl",kernelFunc:hJ};function fJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],c=k.parseAxisParam(s,r.shape),u=c,p=_.getAxesPermutation(u,o),d=r;p!=null&&(d=Tn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=_.getInnerMostAxes(u.length,o),l.push(d)),_.assertAxesAreInnerMostDims("prod",u,o);let h;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:g,outDtype:y}=M8(d.shape,d.dtype,m,u);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=_.computeOutAndReduceShapes(d.shape,u),g=k.sizeFromShape(f),y=be({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),b=Qd(r.dtype),v=ji(y,b,"prod",n);h=be({inputs:{x:v},backend:n,attrs:{shape:m}}),l.push(y),l.push(v)}if(i){l.push(h);let m=_.expandShapeToKeepDim(h.shape,c);h=be({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var gJ={kernelName:cl,backendName:"webgl",kernelFunc:fJ},VS=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=P8(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},yJ={kernelName:vc,backendName:"webgl",kernelFunc:VS},bJ="return 1.0 / x;",xJ=Ze({opSnippet:bJ}),vJ={kernelName:pl,backendName:"webgl",kernelFunc:xJ},wJ=Ra+`
+`,gJ=nn({opSnippet:mJ,packedOpSnippet:fJ}),yJ={kernelName:ti,backendName:"webgl",kernelFunc:gJ};function bJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],c=k.parseAxisParam(s,r.shape),u=c,p=_.getAxesPermutation(u,o),d=r;p!=null&&(d=Tn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=_.getInnerMostAxes(u.length,o),l.push(d)),_.assertAxesAreInnerMostDims("prod",u,o);let h;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:g,outDtype:y}=z8(d.shape,d.dtype,m,u);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=_.computeOutAndReduceShapes(d.shape,u),g=k.sizeFromShape(f),y=be({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),b=nh(r.dtype),v=Ki(y,b,"prod",n);h=be({inputs:{x:v},backend:n,attrs:{shape:m}}),l.push(y),l.push(v)}if(i){l.push(h);let m=_.expandShapeToKeepDim(h.shape,c);h=be({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var xJ={kernelName:cl,backendName:"webgl",kernelFunc:bJ},KS=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=B8(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},vJ={kernelName:xc,backendName:"webgl",kernelFunc:KS},wJ="return 1.0 / x;",kJ=Ze({opSnippet:wJ}),IJ={kernelName:pl,backendName:"webgl",kernelFunc:kJ},NJ=Ma+`
   return (x < 0.0) ? 0.0 : x;
-`,kJ=`
+`,TJ=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3492,9 +3492,9 @@ return a / b;`,L9=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,IJ=Ze({opSnippet:wJ,packedOpSnippet:kJ}),NJ={kernelName:ti,backendName:"webgl",kernelFunc:IJ},TJ=Ra+`
+`,SJ=Ze({opSnippet:NJ,packedOpSnippet:TJ}),CJ={kernelName:ai,backendName:"webgl",kernelFunc:SJ},_J=Ma+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,SJ=`
+`,EJ=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3504,7 +3504,7 @@ return a / b;`,L9=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,CJ=Ze({opSnippet:TJ,packedOpSnippet:SJ}),_J={kernelName:ai,backendName:"webgl",kernelFunc:CJ},EJ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+`,AJ=Ze({opSnippet:_J,packedOpSnippet:EJ}),FJ={kernelName:si,backendName:"webgl",kernelFunc:AJ},$J=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${c[0]/u[0]},
           ${c[1]/u[1]});
@@ -3537,7 +3537,7 @@ return a / b;`,L9=`
 
         setOutput(newValue);
       }
-    `}},AJ=class{constructor(e,t,n,a,r){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=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},DJ=class{constructor(e,t,n,a,r){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=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${c[0]/u[0]},
           ${c[1]/u[1]},
@@ -3614,7 +3614,7 @@ return a / b;`,L9=`
 
         setOutput(newValue);
       }
-    `}};function FJ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=ee().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new AJ(r.shape,l,c,s,i):new EJ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],"float32")}var $J={kernelName:ni,backendName:"webgl",kernelFunc:FJ},DJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],p=1/c,d=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
+    `}};function RJ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=ee().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new DJ(r.shape,l,c,s,i):new $J(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],"float32")}var MJ={kernelName:ri,backendName:"webgl",kernelFunc:RJ},PJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],p=1/c,d=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3695,7 +3695,7 @@ return a / b;`,L9=`
 
         setOutput(accumulator);
       }
-    `}};function RJ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new DJ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var MJ={kernelName:Gd,backendName:"webgl",kernelFunc:RJ},PJ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function OJ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new PJ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var LJ={kernelName:qd,backendName:"webgl",kernelFunc:OJ},zJ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",d;r?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]});
@@ -3717,7 +3717,7 @@ return a / b;`,L9=`
 
         setOutput(newValue);
       }
-    `}};function OJ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=new PJ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],r.dtype)}var LJ={kernelName:wc,backendName:"webgl",kernelFunc:OJ},zJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],p=1/c,d=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
+    `}};function BJ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=new zJ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],r.dtype)}var WJ={kernelName:vc,backendName:"webgl",kernelFunc:BJ},VJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],p=1/c,d=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3787,7 +3787,7 @@ return a / b;`,L9=`
 
         setOutput(accumulator);
       }
-    `}};function BJ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new zJ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var WJ={kernelName:Ud,backendName:"webgl",kernelFunc:BJ},VJ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
+    `}};function UJ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new VJ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var GJ={kernelName:jd,backendName:"webgl",kernelFunc:UJ},HJ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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));
@@ -3797,7 +3797,7 @@ return a / b;`,L9=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${r}));
       }
-    `}},UJ=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-${n} tensor is not yet supported`);this.outputShape=e;let a=mn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ft(n);n===1?this.userCode=`
+    `}},jJ=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let a=mn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ft(n);n===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
@@ -3825,7 +3825,7 @@ return a / b;`,L9=`
           }
           setOutput(result);
         }
-    `;function o(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((y,b)=>d(b,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function d(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function GJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return Bn({inputs:{x:r},backend:n});let l=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new UJ(r.shape,o):new VJ(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var HJ={kernelName:ri,backendName:"webgl",kernelFunc:GJ},jJ=class{constructor(e,t,n,a){this.variableNames=["Image"],this.outputShape=[];let r=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,c]=_.getImageCenter(a,r,s),u=l.toFixed(3),p=c.toFixed(3),d="";typeof n=="number"?d=`float outputValue = ${n.toFixed(2)};`:d=`
+    `;function o(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((y,b)=>d(b,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function d(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function qJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return Wn({inputs:{x:r},backend:n});let l=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new jJ(r.shape,o):new HJ(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var KJ={kernelName:ii,backendName:"webgl",kernelFunc:qJ},XJ=class{constructor(e,t,n,a){this.variableNames=["Image"],this.outputShape=[];let r=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,c]=_.getImageCenter(a,r,s),u=l.toFixed(3),p=c.toFixed(3),d="";typeof n=="number"?d=`float outputValue = ${n.toFixed(2)};`:d=`
         vec3 fill = vec3(${n.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         void main() {
@@ -3842,7 +3842,7 @@ return a / b;`,L9=`
           }
           setOutput(outputValue);
         }
-    `}},qJ={kernelName:Sl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new jJ(a.shape,r,s,i);return o.runWebGLProgram(l,[a],a.dtype)}},KJ=`
+    `}},YJ={kernelName:Sl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new XJ(a.shape,r,s,i);return o.runWebGLProgram(l,[a],a.dtype)}},ZJ=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -3857,7 +3857,7 @@ return a / b;`,L9=`
       return base + 1.0;
     }
   }
-`,XJ=Ze({opSnippet:KJ}),YJ={kernelName:si,backendName:"webgl",kernelFunc:XJ},ZJ="return inversesqrt(x);",JJ=Ze({opSnippet:ZJ,cpuKernelImpl:O8}),QJ={kernelName:ii,backendName:"webgl",kernelFunc:JJ},US=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ft(r.length),l=ft(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
+`,JJ=Ze({opSnippet:ZJ}),QJ={kernelName:oi,backendName:"webgl",kernelFunc:JJ},eQ="return inversesqrt(x);",tQ=Ze({opSnippet:eQ,cpuKernelImpl:W8}),nQ={kernelName:li,backendName:"webgl",kernelFunc:tQ},XS=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ft(r.length),l=ft(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
         ${o} strides = ${o}(${r});
 
         void main() {
@@ -3877,7 +3877,7 @@ return a / b;`,L9=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function eQ(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:p}=_.calculateShapes(s,r,i),d=[p/c,c];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=be({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=be({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new US(l,o,h.shape.length,m.shape.length,u,d),y=n.runWebGLProgram(g,[m,h,f],m.dtype),b=be({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),b}var tQ={kernelName:hl,backendName:"webgl",kernelFunc:eQ},nQ=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);a=o.join(),r=l.join()}let s=ft(n);this.userCode=`
+      `}};function aQ(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:p}=_.calculateShapes(s,r,i),d=[p/c,c];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=be({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=be({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new XS(l,o,h.shape.length,m.shape.length,u,d),y=n.runWebGLProgram(g,[m,h,f],m.dtype),b=be({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),b}var rQ={kernelName:hl,backendName:"webgl",kernelFunc:aQ},sQ=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);a=o.join(),r=l.join()}let s=ft(n);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         float cVal = getC(${a});
@@ -3887,21 +3887,21 @@ return a / b;`,L9=`
           setOutput(getB(${r}));
         }
       }
-    `}};function aQ(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new nQ(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],la(r.dtype,s.dtype))}var rQ={kernelName:ml,backendName:"webgl",kernelFunc:aQ},sQ=`
+    `}};function iQ(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new sQ(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],ua(r.dtype,s.dtype))}var oQ={kernelName:ml,backendName:"webgl",kernelFunc:iQ},lQ=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
   float scaleAlpha = ${_.SELU_SCALEALPHA};
   float scale = ${_.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,iQ=Ze({opSnippet:sQ}),oQ={kernelName:fl,backendName:"webgl",kernelFunc:iQ},lQ="return 1.0 / (1.0 + exp(-1.0 * x));",uQ=Ze({opSnippet:lQ}),cQ={kernelName:li,backendName:"webgl",kernelFunc:uQ},pQ=`
+`,uQ=Ze({opSnippet:lQ}),cQ={kernelName:fl,backendName:"webgl",kernelFunc:uQ},pQ="return 1.0 / (1.0 + exp(-1.0 * x));",dQ=Ze({opSnippet:pQ}),hQ={kernelName:ci,backendName:"webgl",kernelFunc:dQ},mQ=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,dQ=Ze({opSnippet:pQ}),hQ={kernelName:bl,backendName:"webgl",kernelFunc:dQ},mQ=nS+`
+`,fQ=Ze({opSnippet:mQ}),gQ={kernelName:bl,backendName:"webgl",kernelFunc:fQ},yQ=lS+`
   return sin(x);
-`,fQ=Ze({opSnippet:mQ}),gQ={kernelName:oi,backendName:"webgl",kernelFunc:fQ},yQ=`
+`,bQ=Ze({opSnippet:yQ}),xQ={kernelName:ui,backendName:"webgl",kernelFunc:bQ},vQ=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,bQ=Ze({opSnippet:yQ}),xQ={kernelName:yl,backendName:"webgl",kernelFunc:bQ},vQ=`
+`,wQ=Ze({opSnippet:vQ}),kQ={kernelName:yl,backendName:"webgl",kernelFunc:wQ},IQ=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -3921,9 +3921,9 @@ return a / b;`,L9=`
     result = log(exp_x + 1.0);
   }
   return result;
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@@ -3931,15 +3931,15 @@ return a / b;`,L9=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${i}));
       }
-    `}};function BQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:p,shrinkAxisMask:d}=a,{nonStrided:h,$begin:m,$strides:f,size:g,newShape:y,outShape:b}=cn.sliceInfo(r.shape,s,i,o,l,c,u,p,d),v=be({inputs:{x:r},backend:n,attrs:{shape:y}}),x;if(h){let T=Ip({inputs:{x:v},backend:n,attrs:{begin:m,size:g}});x=be({inputs:{x:T},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(T)}else if(b.some(T=>T===0))x=n.makeTensorInfo(b,r.dtype,[]);else if(n.shouldExecuteOnCPU([v])){let T=n.texData.get(v.dataId).values,C=Le(v.shape,v.dtype,T),$=z8(b,C,f,m);x=n.makeTensorInfo(b,v.dtype,$.values)}else{let T=new zQ(m,f,b);x=n.runWebGLProgram(T,[v],v.dtype)}let N=be({inputs:{x},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(v),n.disposeIntermediateTensorInfo(x),N}var WQ={kernelName:wl,backendName:"webgl",kernelFunc:BQ},VQ="return tan(x);",UQ=Ze({opSnippet:VQ}),GQ={kernelName:kl,backendName:"webgl",kernelFunc:UQ},HQ=`
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         if (inIdx < 0 || inIdx >= ${r}) {
@@ -4045,7 +4045,7 @@ return a / b;`,L9=`
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-    `}};function ree(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],c=0,u=_.getAxesPermutation([c],o),p=r;u!=null&&(p=Tn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(p),c=_.getInnerMostAxes(1,o)[0]);let d=_.segment_util.computeOutShape(p.shape,c,i),h=k.sizeFromShape([p.shape[c]]),m=be({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=Qd(r.dtype),g=(x,N,T,C,$)=>{let F=x.shape[0],O=x.shape[1],B=_.segment_util.segOpComputeOptimalWindowSize(O,$),W={windowSize:B,inSize:O,batchSize:F,numSegments:$},G=new aee(W,N),q=n.compileAndRun(G,[x,T],C);if(l.push(q),q.shape[1]===$)return q;let H=VS({backend:n,attrs:{start:0,stop:$,step:1,dtype:"float32"}}),K=HS({inputs:{x:H},backend:n,attrs:{reps:[O/B]}});return l.push(H),l.push(K),g(q,N,K,C,$)},y=g(m,"unsortedSegmentSum",s,f,i),b=be({inputs:{x:y},backend:n,attrs:{shape:d}}),v=b;if(u!=null){l.push(b);let x=_.getUndoAxesPermutation(u);v=Tn({inputs:{x:v},backend:n,attrs:{perm:x}})}return 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Please use 'channelsLast'.`);let W=a.makeOutput(m.outShape,"float32"),G=a.dataIdMap.get(W.dataId).id;return e2(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,b,v,x,B,N,T,C,$,F,O,G),W}var Bee={kernelName:Fs,backendName:"wasm",setupFunc:Lee,kernelFunc:zee},t2;function Wee(e){t2=e.wasm.cwrap($s,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Vee(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,inputShape:u}=a,p=1,d=_.convertConv2DDataFormat(l),h=_.computeConv2DInfo(u,s.shape,i,p,o,c,!1,d),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:b,inWidth:v,outChannels:x,outHeight:N,outWidth:T,strideHeight:C,strideWidth:$}=h,F=f-1-h.padInfo.top,O=g-1-h.padInfo.left,B=h.dataFormat==="channelsLast",W=k.computeStrides(h.inShape),G=k.computeStrides(r.shape),[q,H,K]=k.computeStrides(s.shape),X=W[0],ne=B?W[1]:W[2],J=B?W[2]:1,se=B?1:W[1],Q=G[0],oe=B?G[1]:G[2],ae=B?G[2]:1,pe=B?1:G[1],ue=t.makeOutput(h.inShape,"float32"),ge=t.dataIdMap.get(ue.dataId).id,ye=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(s.dataId).id;return t2(ye,ke,m,f,g,b,v,y,N,T,x,C,$,F,O,q,H,K,X,ne,J,se,Q,oe,ae,pe,ge),ue}var Uee={kernelName:$s,backendName:"wasm",setupFunc:Wee,kernelFunc:Vee},Gee=Vn(Ds),Hv;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Hv||(Hv={}));var n2;function Hee(e){n2=e.wasm.cwrap(Oo,null,["number","number","number","number","array","number","number","number","number","number"])}function jee(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:c}=n,u=l.shape[0],[p,d]=i,h=[u,p,d,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=Ym({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(l.dataId).id,b=t.dataIdMap.get(c.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,N=new Uint8Array(new Int32Array(o.shape).buffer);return n2(g,y,b,u,N,p,d,Hv[r],s,x),f!=null&&t.disposeData(f.dataId),v}var qee={kernelName:Oo,backendName:"wasm",setupFunc:Hee,kernelFunc:jee},a2;function Kee(e){a2=e.wasm.cwrap(Rs,null,["number","number","number","number","number","number"])}function Xee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;k.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=_.getAxesPermutation([s],l),u=r;c!==null&&(u=Xm({inputs:{x:r},attrs:{perm:c},backend:n}));let p=_.getInnerMostAxes(1,l)[0];_.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(u.shape,u.dtype),h=u.shape[p],m=n.dataIdMap.get(u.dataId).id,f=n.dataIdMap.get(d.dataId).id;a2(m,i?1:0,o?1:0,h,f,Wn[r.dtype]);let g=d;if(c!==null){let y=_.getUndoAxesPermutation(c);g=Xm({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(d.dataId)}return g}var Yee={kernelName:Rs,backendName:"wasm",setupFunc:Kee,kernelFunc:Xee},r2;function Zee(e){r2=e.wasm.cwrap(Lo,null,["number","number","number","array","number","array","array","number","number"])}function Jee(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),v=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),x=t.dataIdMap.get(f.dataId).id;return r2(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,v,m.length,x),f}var Qee={kernelName:Lo,backendName:"wasm",setupFunc:Zee,kernelFunc:Jee},s2;function ete(e){s2=e.wasm.cwrap(Ms,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function tte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:p}=n,d=c==null?[1,1]:c,h=_.computeConv2DInfo(r.shape,s.shape,l,d,u,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,N=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,$=h.inChannels,F=h.outChannels,O=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let B=a.makeOutput(h.outShape,"float32"),W=a.dataIdMap.get(B.dataId).id;return s2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,v,O,x,N,T,C,$,F,W),B}var nte={kernelName:Ms,backendName:"wasm",setupFunc:ete,kernelFunc:tte},ate=!1,rte=fn(Wo,ate,"bool"),ste=Vn(Os);function jv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Ma({inputs:{x:r},backend:a,attrs:{shape:o}})}var ite={kernelName:Vo,backendName:"wasm",kernelFunc:jv};function ote(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var lte={kernelName:mc,backendName:"wasm",kernelFunc:ote},i2;function ute(e){i2=e.wasm.cwrap(Go,null,["number","number","number","number","number","number"])}function cte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return i2(s,o,l,c,u,i),r}var pte={kernelName:Go,backendName:"wasm",kernelFunc:cte,setupFunc:ute},dte=Vn(Ls),hte=!1,mte=fn(zs,hte),o2;function fte(e){o2=e.wasm.cwrap(Bs,null,["number","number","number","number","number","number","number"])}function gte(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return o2(u,p,d,h,m,r,g),f}var yte={kernelName:Bs,backendName:"wasm",setupFunc:fte,kernelFunc:gte},l2;function bte(e){l2=e.wasm.cwrap(yi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function xte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Tp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,$=f.padInfo.right,F=f.padInfo.bottom,O=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,H=f.inChannels,K=f.padInfo.type==="SAME"?1:0,X=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return l2(y,X,ne,J,b,N,T,x,C,$,F,O,K,B,W,G,q,H,v,g,oe,m||0,Q),se}var vte={kernelName:yi,backendName:"wasm",setupFunc:bte,kernelFunc:xte},u2;function wte(e){u2=e.wasm.cwrap(bi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d,!0),g=Tp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,$=f.padInfo.right,F=f.padInfo.bottom,O=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,H=f.inChannels,K=f.padInfo.type==="SAME"?1:0,X=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return u2(y,X,ne,J,b,N,T,x,C,$,F,O,K,B,W,G,q,H,v,g,oe,m||0,Q),se}var Ite={kernelName:bi,backendName:"wasm",setupFunc:wte,kernelFunc:kte},c2;function Nte(e){c2=e.wasm.cwrap(jo,null,["number","number","number","number","number","number","array","number"])}function Tte(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=fy.prepareAndValidate(a,r),c=t.makeOutput(s,a.dtype);if(i===0)return c;let u=r.shape,p=u[u.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(c.dataId).id;return c2(d,Wn[a.dtype],h,i,p,o,m,f),c}var Ste={kernelName:jo,backendName:"wasm",setupFunc:Nte,kernelFunc:Tte},p2;function Cte(e){p2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function _te(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=Ma({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=Ma({inputs:{x:s},attrs:{shape:[c.batchSize,p/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize],m=t.makeOutput(h,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=u.shape.length-1,g=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return p2(g,Wn[r.dtype],v,f,y,c.batchSize,x,b),m.shape=c.outputShape,m}var Ete={kernelName:Ho,backendName:"wasm",setupFunc:Cte,kernelFunc:_te},Ate=!1,Fte=fn(qo,Ate,"bool"),$te=!1,Dte=fn(Ws,$te,"bool"),d2;function Rte(e){d2=e.wasm.cwrap(Vs,null,["number","number","number"])}function Mte(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;d2(r,n,i)}return s}var Pte={kernelName:Vs,backendName:"wasm",setupFunc:Rte,kernelFunc:Mte},Ote=!1,Lte=fn(Jo,Ote,"bool"),zte=!1,Bte=fn(Qo,zte,"bool"),Wte=Vn(Us),Vte=!1,Ute=fn(tl,Vte,"bool"),h2;function Gte(e){h2=e.wasm.cwrap(Gs,null,["number, number, number"])}function Hte(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:p,inputWasTransposed:d}=gu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;_.assertAxesAreInnerMostDims("max",u,h);let[m,f]=_.computeOutAndReduceShapes(l.shape,u),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;h2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var jte={kernelName:Gs,backendName:"wasm",setupFunc:Gte,kernelFunc:Hte},qte=!1,Kte=fn(Hs,qte),m2;function Xte(e){m2=e.wasm.cwrap(js,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Yte(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=_.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,b=u.dilationWidth,v=u.strideHeight,x=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(u.outShape,"float32"),$=a.dataIdMap.get(C.dataId).id;return m2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,v,x,N,T,$),C}var Zte={kernelName:js,backendName:"wasm",setupFunc:Xte,kernelFunc:Yte},f2;function Jte(e){f2=e.wasm.cwrap(qs,null,["number, number, number"])}function Qte(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=gu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=Ym({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let v=t.makeOutput(f,"float32");if(k.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(v.dataId).id;f2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var ene={kernelName:qs,backendName:"wasm",setupFunc:Jte,kernelFunc:Qte},g2;function tne(e){g2=e.wasm.cwrap(Ks,null,["number, number, number"])}function nne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=gu(i,r,t);if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v)}let m=c.shape.length;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.computeOutAndReduceShapes(c.shape,p),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;g2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var ane={kernelName:Ks,backendName:"wasm",setupFunc:tne,kernelFunc:nne},rne=!1,sne=fn(Xs,rne),ine=!0,one=fn(Ys,ine),lne=Vn(al);function qv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var y2;function une(e){y2=e.wasm.cwrap(sl,"number",["number","number","number","number","number"])}function cne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,c=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(l.dataId).id,p=y2(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=qv(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var pne={kernelName:sl,backendName:"wasm",setupFunc:une,kernelFunc:cne},b2;function dne(e){b2=e.wasm.cwrap(il,"number",["number","number","number","number","number","bool"])}function hne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=b2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=qv(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var mne={kernelName:il,backendName:"wasm",setupFunc:dne,kernelFunc:hne},x2;function fne(e){x2=e.wasm.cwrap(ol,"number",["number","number","number","number","number","number"])}function gne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=x2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=qv(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var yne={kernelName:ol,backendName:"wasm",setupFunc:fne,kernelFunc:gne},bne=!1,xne=fn(rl,bne,"bool"),v2;function vne(e){v2=e.wasm.cwrap(Zs,null,["number","number","number","number","number"])}function wne(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),c=n.dataIdMap.get(l.dataId).id,u=n.dataIdMap.get(r.dataId).id;return v2(u,s,i,o,c),l}var kne={kernelName:Zs,backendName:"wasm",setupFunc:vne,kernelFunc:wne};function Ine(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Nne={kernelName:ll,backendName:"wasm",kernelFunc:Ine};function Tne(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return jv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(l=>{k.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===l.dtype,()=>"All tensors passed to stack must have matching 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One={kernelName:cl,backendName:"wasm",setupFunc:Mne,kernelFunc:Pne},Lne=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=bv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},zne={kernelName:vc,backendName:"wasm",kernelFunc:Lne},Bne=!0,Wne=fn(Ps,Bne),Vne=Vn(ti),Une=Vn(ai),N2;function Gne(e){N2=e.wasm.cwrap(ni,null,["number","number","number","number","number","number","number","number","number","number"])}function Hne(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,[u,p,d,h]=r.shape,m=[u,l,c,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=Ym({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return b;let v=t.dataIdMap.get(b.dataId).id;return N2(y,u,p,d,h,l,c,s?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),b}var jne={kernelName:ni,backendName:"wasm",setupFunc:Gne,kernelFunc:Hne},T2;function qne(e){T2=e.wasm.cwrap(ri,null,["number","array","number","array","number","number"])}function Kne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=k.parseAxisParam(s,r.shape);if(r.shape.length===0)return Km({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);return T2(l,u,i.length,p,r.shape.length,c),Ma({inputs:{x:o},attrs:{shape:r.shape},backend:n})}var Xne={kernelName:ri,backendName:"wasm",kernelFunc:Kne,setupFunc:qne},S2;function Yne(e){S2=e.wasm.cwrap(Sl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function 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xae={kernelName:vl,backendName:"wasm",kernelFunc:bae},vae=Vn(ui),wae=Vn(Ic),kae=!0,Iae=fn(di,kae),F2;function Nae(e){F2=e.wasm.cwrap(Wr,null,["number","number","number"])}function Tae(e){let{backend:t,inputs:n,attrs:a}=e,{alpha:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return F2(i,r,l),o}var Sae={kernelName:Wr,backendName:"wasm",setupFunc:Nae,kernelFunc:Tae},$2;function Cae(e){$2=e.wasm.cwrap(wl,null,["number","array","number","array","array","array","array","array","number","number"])}function _ae(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{begin:s,end:i,strides:o}=a;o==null&&(o=new Array(s.length));let{beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:p,shrinkAxisMask:d}=a,h=_.slice_util.maskToAxes(u);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(u!==0&&p!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(u!==0&&d!==0)throw new Error("Using both 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Er;(function(e){e.FEMALE="female",e.MALE="male"})(Er||(Er={}));var ff=class extends an{constructor(t=new Iw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof _r?this.faceFeatureExtractor.forwardInput(t):t,r=Xn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Op(r,n.fc.age).as1D(),i=Op(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return D(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:Na(a)}})}async forward(t){return this.forwardInput(await gt(t))}async predictAgeAndGender(t){let n=await gt(t),a=await this.forwardInput(n),r=dt(a.age),s=dt(a.gender),i=r.map((l,c)=>({ageTensor:l,genderTensor:s[c]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:c})=>{let u=(await l.data())[0],p=(await c.data())[0],d=p>.5,h=d?Er.MALE:Er.FEMALE,m=d?p:1-p;return 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vC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=bn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=Pre(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,v]=a,x=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),C=c(m,f,"conv3"),$=c(f,g,"conv4"),F=c(g,y,"conv5"),O=b?c(y,b,"conv6"):void 0,B=v?c(b,v,"conv7"):void 0,W=o(v||b||y,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:B,conv8:W}}else{let[p,d,h,m,f,g,y,b,v]=a,x=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),$=l(f,g,"conv4"),F=l(g,y,"conv5"),O=l(y,b,"conv6"),B=l(b,v,"conv7"),W=o(v,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:B,conv8:W}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function Ore(e,t){let n=Un(e,t);function a(o){let l=n(`${o}/sub`,1),c=n(`${o}/truediv`,1);return{sub:l,truediv:c}}function r(o){let l=n(`${o}/filters`,4),c=n(`${o}/bias`,1);return{filters:l,bias:c}}function s(o){let l=r(`${o}/conv`),c=a(`${o}/bn`);return{conv:l,bn:c}}let i=Su(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function wC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Ore(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return yn(e,n),{params:i,paramMappings:n}}var lr=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var _w=class extends an{constructor(t){super("TinyYolov2");Cw(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Ar(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=Ar(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Ar(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Ar(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Ar(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Ar(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=Ar(a,n.conv6),a=Ar(a,n.conv7),eo(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Au(eo(t,n.conv0,"valid",!1)):Fr(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=n.conv6?Fr(a,n.conv6):a,a=n.conv7?Fr(a,n.conv7):a,eo(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return D(()=>{let r=ce(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?Oa(r,this.config.meanRgb):r,r=r.div(de(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await gt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new lr(n),s=await gt(t),i=await this.forwardInput(s,a),o=D(()=>dt(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},c=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let u=c.map(g=>g.box),p=c.map(g=>g.score),d=c.map(g=>g.classScore),h=c.map(g=>this.config.classes[g.label]);return nw(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new ds(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return wC(t,this.config)}extractParams(t){let n=this.config.filterSizes||_w.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return vC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?Na(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):de(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;y<c;y++)for(let b=0;b<c;b++)for(let v=0;v<u;v++){let x=Ep(f[y][b][v][0]);if(!a||x>a){let N=(b+Ep(g[y][b][v][0]))/c*o,T=(y+Ep(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,$=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,F=N-C/2,O=T-$/2,B={row:y,col:b,anchor:v},{classScore:W,label:G}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new bu(F,O,F+C,O+$),score:x,classScore:x*W,label:G,...B})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Fu=_w;Fu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var $u=class extends Fu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:mC,classes:["face"],...t?{anchors:gC,meanRgb:yC}:{anchors:fC,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new bt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?xC:bC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Lre(e,t=!0){let n=new $u(t);return n.extractWeights(e),n}var kf=class extends lr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var va=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function ro(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>to(l)?r(l):l.detection),i=a||(t instanceof Ae?await Iu(t,s):await ku(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ae&&l.dispose()),o}async function Du(e,t,n,a,r){return ro([e],t,async s=>n(s[0]),a,r)}var kC=.4,IC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],NC=[117.001,114.697,97.404];var Ru=class extends Fu{constructor(){let t={withSeparableConvs:!0,iouThreshold:kC,classes:["face"],anchors:IC,meanRgb:NC,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new bt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var nt={ssdMobilenetv1:new ao,tinyFaceDetector:new Ru,tinyYolov2:new $u,faceLandmark68Net:new _u,faceLandmark68TinyNet:new gf,faceRecognitionNet:new Eu,faceExpressionNet:new hf,ageGenderNet:new ff},TC=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),zre=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),Bre=(e,t)=>nt.tinyYolov2.locateFaces(e,t),SC=e=>nt.faceLandmark68Net.detectLandmarks(e),Wre=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),Vre=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),Ure=e=>nt.faceExpressionNet.predictExpressions(e),Gre=e=>nt.ageGenderNet.predictAgeAndGender(e),CC=e=>nt.ssdMobilenetv1.load(e),Hre=e=>nt.tinyFaceDetector.load(e),jre=e=>nt.tinyYolov2.load(e),qre=e=>nt.faceLandmark68Net.load(e),Kre=e=>nt.faceLandmark68TinyNet.load(e),Xre=e=>nt.faceRecognitionNet.load(e),Yre=e=>nt.faceExpressionNet.load(e),Zre=e=>nt.ageGenderNet.load(e),Jre=CC,Qre=TC,ese=SC;var Ew=class extends va{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ou=class extends Ew{async run(){let t=await this.parentTask,n=await ro(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>mf(a,n[r]))}withAgeAndGender(){return new Mu(this,this.input)}},Lu=class extends Ew{async run(){let t=await this.parentTask;if(!t)return;let n=await Du(t,this.input,a=>nt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return mf(t,n)}withAgeAndGender(){return new Pu(this,this.input)}},oo=class extends Ou{withAgeAndGender(){return new so(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},lo=class extends Lu{withAgeAndGender(){return new io(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Aw=class extends va{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Mu=class extends Aw{async run(){let t=await this.parentTask,n=await ro(t,this.input,async a=>Promise.all(a.map(r=>nt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return xf(vf(a,i,o),s)})}withFaceExpressions(){return new Ou(this,this.input)}},Pu=class extends Aw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Du(t,this.input,s=>nt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return xf(vf(t,a,r),n)}withFaceExpressions(){return new Lu(this,this.input)}},so=class extends Mu{withFaceExpressions(){return new oo(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},io=class extends Pu{withFaceExpressions(){return new lo(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var If=class extends va{constructor(t,n){super();this.parentTask=t;this.input=n}},fs=class extends If{async run(){let t=await this.parentTask;return(await ro(t,this.input,a=>Promise.all(a.map(r=>nt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>bf(t[r],a))}withFaceExpressions(){return new oo(this,this.input)}withAgeAndGender(){return new so(this,this.input)}},gs=class extends If{async run(){let t=await this.parentTask;if(!t)return;let n=await Du(t,this.input,a=>nt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return bf(t,n)}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}};var Nf=class extends va{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},Tf=class extends Nf{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ae?await Iu(this.input,n):await ku(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ae&&s.dispose()),t.map((s,i)=>Cu(s,r[i]))}withFaceExpressions(){return new oo(this,this.input)}withAgeAndGender(){return new so(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},Sf=class extends Nf{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ae?await Iu(this.input,[n]):await ku(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ae&&s.dispose()),Cu(t,r)}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Cf=class extends va{constructor(t,n=new xa){super();this.input=t;this.options=n}},Wp=class extends Cf{async run(){let{input:t,options:n}=this,a=n instanceof kf?r=>nt.tinyFaceDetector.locateFaces(r,n):n instanceof xa?r=>nt.ssdMobilenetv1.locateFaces(r,n):n instanceof lr?r=>nt.tinyYolov2.locateFaces(r,n):null;if(!a)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return a(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>Yi({},a)))})}withFaceLandmarks(t=!1){return new Tf(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ou(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Mu(this.runAndExtendWithFaceDetections(),this.input)}},_f=class extends Cf{async run(){let t=await new Wp(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Yi({},n):void 0)})}withFaceLandmarks(t=!1){return new Sf(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Lu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Pu(this.runAndExtendWithFaceDetection(),this.input)}};function tse(e,t=new xa){return new _f(e,t)}function Ef(e,t=new xa){return new Wp(e,t)}async function _C(e,t){return Ef(e,new xa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function nse(e,t={}){return Ef(e,new lr(t)).withFaceLandmarks().withFaceDescriptors()}var ase=_C;function Fw(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var Af=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof Cr)return i;if(i instanceof Float32Array)return new Cr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Cr(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>Fw(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Ap(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distance<a.distance?n:a)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this.distanceThreshold?n:new Ap("unknown",n.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Cr.fromJSON(a));return new Af(n,t.distanceThreshold)}};function rse(e){let t=new Ru;return t.extractWeights(e),t}function EC(e,t){let{width:n,height:a}=new gn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>EC(r,{width:n,height:a}));if(to(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Cu(Yi(e,r),s)}return ir(e)?Yi(e,e.detection.forSize(n,a)):e instanceof ta||e instanceof bt?e.forSize(n,a):e}var sse=typeof process!="undefined",ise=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",ose={faceapi:q2,node:sse,browser:ise};export{ff as AgeGenderNet,bu as BoundingBox,ut as Box,va as ComposableTask,fs as ComputeAllFaceDescriptorsTask,If as ComputeFaceDescriptorsTaskBase,gs as ComputeSingleFaceDescriptorTask,Tf as DetectAllFaceLandmarksTask,Wp as DetectAllFacesTask,Nf as DetectFaceLandmarksTaskBase,Cf as DetectFacesTaskBase,Sf as DetectSingleFaceLandmarksTask,_f as DetectSingleFaceTask,gn as Dimensions,yw as FACE_EXPRESSION_LABELS,bt as FaceDetection,hC as FaceDetectionNet,hf as FaceExpressionNet,ms as FaceExpressions,_u as FaceLandmark68Net,gf as FaceLandmark68TinyNet,tC as FaceLandmarkNet,ta as FaceLandmarks,z2 as FaceLandmarks5,vu as FaceLandmarks68,Ap as FaceMatch,Af as FaceMatcher,Eu as FaceRecognitionNet,Er as Gender,Fp as LabeledBox,Cr as LabeledFaceDescriptors,_r as NetInput,an as NeuralNetwork,ds as ObjectDetection,De as Point,B2 as PredictedBox,xu as Rect,ao as SsdMobilenetv1,xa as SsdMobilenetv1Options,Ru as TinyFaceDetector,kf as TinyFaceDetectorOptions,$u as TinyYolov2,lr as TinyYolov2Options,ase as allFaces,_C as allFacesSsdMobilenetv1,nse as allFacesTinyYolov2,pw as awaitMediaLoaded,dw as bufferToImage,Vre as computeFaceDescriptor,wu as createCanvas,Rp as createCanvasFromMedia,Mre as createFaceDetectionNet,Nre as createFaceRecognitionNet,dC as createSsdMobilenetv1,rse as createTinyFaceDetector,Lre as createTinyYolov2,Ef as detectAllFaces,SC as detectFaceLandmarks,Wre as detectFaceLandmarksTiny,ese as detectLandmarks,tse as detectSingleFace,ww as draw,st as env,Fw as euclideanDistance,xf as extendWithAge,bf as extendWithFaceDescriptor,Yi as extendWithFaceDetection,mf as extendWithFaceExpressions,Cu as extendWithFaceLandmarks,vf as extendWithGender,Iu as extractFaceTensors,ku as extractFaces,mre as fetchImage,fw as fetchJson,fre as fetchNetWeights,Qi as fetchOrThrow,Sn as getContext2dOrThrow,Ji as getMediaDimensions,hw as imageTensorToCanvas,mw as imageToSquare,sre as inverseSigmoid,ew as iou,af as isMediaElement,Dp as isMediaLoaded,Tre as isWithAge,ir as isWithFaceDetection,bw as isWithFaceExpressions,to as isWithFaceLandmarks,Sre as isWithGender,Zre as loadAgeGenderModel,Jre as loadFaceDetectionModel,Yre as loadFaceExpressionModel,qre as loadFaceLandmarkModel,Kre as loadFaceLandmarkTinyModel,Xre as loadFaceRecognitionModel,CC as loadSsdMobilenetv1Model,Hre as loadTinyFaceDetectorModel,jre as loadTinyYolov2Model,gw as loadWeightMap,Qre as locateFaces,gre as matchDimensions,tw as minBbox,nt as nets,nw as nonMaxSuppression,Oa as normalize,aw as padToSquare,Gre as predictAgeAndGender,Ure as recognizeFaceExpressions,EC as resizeResults,Zi as resolveInput,rre as shuffleArray,Ep as sigmoid,TC as ssdMobilenetv1,Bg as tf,zre as tinyFaceDetector,Bre as tinyYolov2,gt as toNetInput,Yv as utils,Cw as validateConfig,ose as version};
+    `}};function oee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],c=0,u=_.getAxesPermutation([c],o),p=r;u!=null&&(p=Tn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(p),c=_.getInnerMostAxes(1,o)[0]);let d=_.segment_util.computeOutShape(p.shape,c,i),h=k.sizeFromShape([p.shape[c]]),m=be({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=nh(r.dtype),g=(x,N,T,C,F)=>{let $=x.shape[0],P=x.shape[1],B=_.segment_util.segOpComputeOptimalWindowSize(P,F),W={windowSize:B,inSize:P,batchSize:$,numSegments:F},G=new iee(W,N),q=n.compileAndRun(G,[x,T],C);if(l.push(q),q.shape[1]===F)return q;let j=KS({backend:n,attrs:{start:0,stop:F,step:1,dtype:"float32"}}),X=ZS({inputs:{x:j},backend:n,attrs:{reps:[P/B]}});return l.push(j),l.push(X),g(q,N,X,C,F)},y=g(m,"unsortedSegmentSum",s,f,i),b=be({inputs:{x:y},backend:n,attrs:{shape:d}}),v=b;if(u!=null){l.push(b);let x=_.getUndoAxesPermutation(u);v=Tn({inputs:{x:v},backend:n,attrs:{perm:x}})}return 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Please use 'channelsLast'.`);let W=a.makeOutput(m.outShape,"float32"),G=a.dataIdMap.get(W.dataId).id;return i2(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,b,v,x,B,N,T,C,F,$,P,G),W}var Gee={kernelName:$s,backendName:"wasm",setupFunc:Vee,kernelFunc:Uee},o2;function Hee(e){o2=e.wasm.cwrap(Ds,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function jee(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,inputShape:u}=a,p=1,d=_.convertConv2DDataFormat(l),h=_.computeConv2DInfo(u,s.shape,i,p,o,c,!1,d),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:b,inWidth:v,outChannels:x,outHeight:N,outWidth:T,strideHeight:C,strideWidth:F}=h,$=f-1-h.padInfo.top,P=g-1-h.padInfo.left,B=h.dataFormat==="channelsLast",W=k.computeStrides(h.inShape),G=k.computeStrides(r.shape),[q,j,X]=k.computeStrides(s.shape),K=W[0],ne=B?W[1]:W[2],J=B?W[2]:1,se=B?1:W[1],Q=G[0],oe=B?G[1]:G[2],ae=B?G[2]:1,pe=B?1:G[1],ue=t.makeOutput(h.inShape,"float32"),ge=t.dataIdMap.get(ue.dataId).id,ye=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(s.dataId).id;return o2(ye,ke,m,f,g,b,v,y,N,T,x,C,F,$,P,q,j,X,K,ne,J,se,Q,oe,ae,pe,ge),ue}var qee={kernelName:Ds,backendName:"wasm",setupFunc:Hee,kernelFunc:jee},Kee=Sn(Rs),Zv;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Zv||(Zv={}));var l2;function Xee(e){l2=e.wasm.cwrap(Lo,null,["number","number","number","number","array","number","number","number","number","number"])}function Yee(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:c}=n,u=l.shape[0],[p,d]=i,h=[u,p,d,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=Qm({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(l.dataId).id,b=t.dataIdMap.get(c.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,N=new Uint8Array(new Int32Array(o.shape).buffer);return l2(g,y,b,u,N,p,d,Zv[r],s,x),f!=null&&t.disposeData(f.dataId),v}var Zee={kernelName:Lo,backendName:"wasm",setupFunc:Xee,kernelFunc:Yee},u2;function Jee(e){u2=e.wasm.cwrap(Ms,null,["number","number","number","number","number","number"])}function Qee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;k.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=_.getAxesPermutation([s],l),u=r;c!==null&&(u=Jm({inputs:{x:r},attrs:{perm:c},backend:n}));let p=_.getInnerMostAxes(1,l)[0];_.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(u.shape,u.dtype),h=u.shape[p],m=n.dataIdMap.get(u.dataId).id,f=n.dataIdMap.get(d.dataId).id;u2(m,i?1:0,o?1:0,h,f,Vn[r.dtype]);let g=d;if(c!==null){let y=_.getUndoAxesPermutation(c);g=Jm({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(d.dataId)}return g}var ete={kernelName:Ms,backendName:"wasm",setupFunc:Jee,kernelFunc:Qee},c2;function tte(e){c2=e.wasm.cwrap(zo,null,["number","number","number","array","number","array","array","number","number"])}function nte(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),v=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),x=t.dataIdMap.get(f.dataId).id;return c2(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,v,m.length,x),f}var ate={kernelName:zo,backendName:"wasm",setupFunc:tte,kernelFunc:nte},p2;function rte(e){p2=e.wasm.cwrap(Ps,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ste(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:p}=n,d=c==null?[1,1]:c,h=_.computeConv2DInfo(r.shape,s.shape,l,d,u,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,N=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,F=h.inChannels,$=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let B=a.makeOutput(h.outShape,"float32"),W=a.dataIdMap.get(B.dataId).id;return p2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,v,P,x,N,T,C,F,$,W),B}var ite={kernelName:Ps,backendName:"wasm",setupFunc:rte,kernelFunc:ste},ote=!1,lte=fn(Vo,ote,"bool"),ute=Sn(Ls);function Jv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Pa({inputs:{x:r},backend:a,attrs:{shape:o}})}var cte={kernelName:Uo,backendName:"wasm",kernelFunc:Jv};function pte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var dte={kernelName:hc,backendName:"wasm",kernelFunc:pte},d2;function hte(e){d2=e.wasm.cwrap(Ho,null,["number","number","number","number","number","number"])}function mte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return d2(s,o,l,c,u,i),r}var fte={kernelName:Ho,backendName:"wasm",kernelFunc:mte,setupFunc:hte},gte=Sn(zs),yte=!1,bte=fn(Bs,yte),h2;function xte(e){h2=e.wasm.cwrap(Ws,null,["number","number","number","number","number","number","number"])}function vte(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return h2(u,p,d,h,m,r,g),f}var wte={kernelName:Ws,backendName:"wasm",setupFunc:xte,kernelFunc:vte},m2;function kte(e){m2=e.wasm.cwrap(xi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ite(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Sp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return m2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var Nte={kernelName:xi,backendName:"wasm",setupFunc:kte,kernelFunc:Ite},f2;function Tte(e){f2=e.wasm.cwrap(vi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ste(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d,!0),g=Sp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return f2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var Cte={kernelName:vi,backendName:"wasm",setupFunc:Tte,kernelFunc:Ste},g2;function _te(e){g2=e.wasm.cwrap(qo,null,["number","number","number","number","number","number","array","number"])}function Ete(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=by.prepareAndValidate(a,r),c=t.makeOutput(s,a.dtype);if(i===0)return c;let u=r.shape,p=u[u.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(c.dataId).id;return g2(d,Vn[a.dtype],h,i,p,o,m,f),c}var Ate={kernelName:qo,backendName:"wasm",setupFunc:_te,kernelFunc:Ete},y2;function Fte(e){y2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function $te(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=Pa({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=Pa({inputs:{x:s},attrs:{shape:[c.batchSize,p/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize],m=t.makeOutput(h,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=u.shape.length-1,g=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return y2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),t.disposeData(u.dataId),t.disposeData(d.dataId),m.shape=c.outputShape,m}var Dte={kernelName:jo,backendName:"wasm",setupFunc:Fte,kernelFunc:$te},Rte=!1,Mte=fn(Ko,Rte,"bool"),Pte=!1,Ote=fn(Vs,Pte,"bool"),b2;function Lte(e){b2=e.wasm.cwrap(Gs,null,["number","number","number"])}function zte(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;b2(r,n,i)}return s}var Bte={kernelName:Gs,backendName:"wasm",setupFunc:Lte,kernelFunc:zte},Wte=!1,Vte=fn(Jo,Wte,"bool"),Ute=!1,Gte=fn(Qo,Ute,"bool"),Hte=Sn(Hs),jte=!1,qte=fn(tl,jte,"bool"),x2;function Kte(e){x2=e.wasm.cwrap(js,null,["number, number, number"])}function Xte(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:p,inputWasTransposed:d}=gu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;_.assertAxesAreInnerMostDims("max",u,h);let[m,f]=_.computeOutAndReduceShapes(l.shape,u),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;x2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Yte={kernelName:js,backendName:"wasm",setupFunc:Kte,kernelFunc:Xte},Zte=!1,Jte=fn(qs,Zte),v2;function Qte(e){v2=e.wasm.cwrap(Ks,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ene(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=_.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,b=u.dilationWidth,v=u.strideHeight,x=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(u.outShape,"float32"),F=a.dataIdMap.get(C.dataId).id;return v2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,v,x,N,T,F),C}var tne={kernelName:Ks,backendName:"wasm",setupFunc:Qte,kernelFunc:ene},w2;function nne(e){w2=e.wasm.cwrap(Xs,null,["number, number, number"])}function ane(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=gu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=Qm({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let v=t.makeOutput(f,"float32");if(k.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(v.dataId).id;w2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var rne={kernelName:Xs,backendName:"wasm",setupFunc:nne,kernelFunc:ane},k2;function sne(e){k2=e.wasm.cwrap(Ys,null,["number, number, number"])}function ine(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=gu(i,r,t);if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v)}let m=c.shape.length;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.computeOutAndReduceShapes(c.shape,p),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;k2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var one={kernelName:Ys,backendName:"wasm",setupFunc:sne,kernelFunc:ine},lne=!1,une=fn(Zs,lne),cne=!0,pne=fn(Js,cne),dne=Sn(al);function Qv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var I2;function hne(e){I2=e.wasm.cwrap(sl,"number",["number","number","number","number","number"])}function mne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,c=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(l.dataId).id,p=I2(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=Qv(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var fne={kernelName:sl,backendName:"wasm",setupFunc:hne,kernelFunc:mne},N2;function gne(e){N2=e.wasm.cwrap(il,"number",["number","number","number","number","number","bool"])}function yne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=N2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Qv(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var bne={kernelName:il,backendName:"wasm",setupFunc:gne,kernelFunc:yne},T2;function xne(e){T2=e.wasm.cwrap(ol,"number",["number","number","number","number","number","number"])}function vne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=T2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Qv(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var wne={kernelName:ol,backendName:"wasm",setupFunc:xne,kernelFunc:vne},kne=!1,Ine=fn(rl,kne,"bool"),S2;function Nne(e){S2=e.wasm.cwrap(Qs,null,["number","number","number","number","number"])}function Tne(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),c=n.dataIdMap.get(l.dataId).id,u=n.dataIdMap.get(r.dataId).id;return S2(u,s,i,o,c),l}var Sne={kernelName:Qs,backendName:"wasm",setupFunc:Nne,kernelFunc:Tne};function Cne(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var _ne={kernelName:ll,backendName:"wasm",kernelFunc:Cne};function Ene(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Jv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(l=>{k.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=t.map(l=>Jv({inputs:{input:l},backend:n,attrs:{dim:r}}));return s2({inputs:o,backend:n,attrs:{axis:r}})}var Ane={kernelName:ul,backendName:"wasm",kernelFunc:Ene},C2;function Fne(e){C2=e.wasm.cwrap(ei,null,["number","array","number","number","array","array","number","number"])}function $ne(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,constantValue:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=a.map(m=>m[0]),p=a.map(m=>m[1]),d=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(p).buffer);return C2(i,c,t.shape.length,Vn[t.dtype],d,h,r,l),o}var Dne={kernelName:ei,backendName:"wasm",kernelFunc:$ne,setupFunc:Fne},Rne=!1,Mne=fn(ti,Rne),_2;function Pne(e){_2=e.wasm.cwrap(ni,null,["number","number","number"])}function One(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=n.makeOutput(a.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return _2(s,i,l),o}var Lne={kernelName:ni,backendName:"wasm",setupFunc:Pne,kernelFunc:One},E2;function zne(e){E2=e.wasm.cwrap(cl,null,["number","number","number","number"])}function Bne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=gu(i,r,t),m=p;if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("prod",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;E2(l,y,Vn[b.dtype],v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var Wne={kernelName:cl,backendName:"wasm",setupFunc:zne,kernelFunc:Bne},Vne=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=Iv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Une={kernelName:xc,backendName:"wasm",kernelFunc:Vne},Gne=!0,Hne=fn(Os,Gne),jne=Sn(ai),qne=Sn(si),A2;function Kne(e){A2=e.wasm.cwrap(ri,null,["number","number","number","number","number","number","number","number","number","number"])}function Xne(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,[u,p,d,h]=r.shape,m=[u,l,c,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=Qm({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return b;let v=t.dataIdMap.get(b.dataId).id;return A2(y,u,p,d,h,l,c,s?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),b}var Yne={kernelName:ri,backendName:"wasm",setupFunc:Kne,kernelFunc:Xne},F2;function Zne(e){F2=e.wasm.cwrap(ii,null,["number","array","number","array","number","number"])}function Jne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=k.parseAxisParam(s,r.shape);if(r.shape.length===0)return Zm({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);F2(l,u,i.length,p,r.shape.length,c);let d=Pa({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var Qne={kernelName:ii,backendName:"wasm",kernelFunc:Jne,setupFunc:Zne},$2;function eae(e){$2=e.wasm.cwrap(Sl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function tae(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(l.dataId).id,[p,d,h,m]=r.shape,[f,g]=_.getImageCenter(o,d,h),y=i===0,b=255,v=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],x=new Uint8Array(new Int32Array(v).buffer);return $2(c,p,d,h,m,s,f,g,x,v.length,u),l}var nae={kernelName:Sl,backendName:"wasm",kernelFunc:tae,setupFunc:eae},aae=Sn(oi),rae=Sn(li),D2;function sae(e){D2=e.wasm.cwrap(hl,null,["number","number","number","number","number","number","array","number","number"])}function iae(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(k.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:c,sliceSize:u,strides:p,outputSize:d}=xy.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(p).buffer),g=t.dataIdMap.get(o.dataId).id;return D2(h,m,Vn[s.dtype],l,c,u,f,d,g),o}var oae={kernelName:hl,backendName:"wasm",setupFunc:sae,kernelFunc:iae},R2;function lae(e){R2=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function uae(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(s.dataId).id,c=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(c.dataId).id,p=a.shape.length,d=r.shape.length,h=p===0||p>1||d===1?1:k.sizeFromShape(r.shape.slice(1));return R2(i,o,l,h,u),c}var cae={kernelName:ml,backendName:"wasm",kernelFunc:uae,setupFunc:lae},M2;function pae(e){M2=e.wasm.cwrap(ci,null,["number","number"])}function dae(e){let{backend:t,inputs:{x:n}}=e,a=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(r.dataId).id;return k.sizeFromShape(r.shape)===0||M2(a,s),r}var hae={kernelName:"Sigmoid",backendName:"wasm",setupFunc:pae,kernelFunc:dae},mae=Sn(ui);function ef(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=cn.parseSliceParams(t,n,a),o=cn.isSliceContinous(t.shape,s,i),l=r.readSync(t.dataId),c=r.makeOutput(i,t.dtype),u=k.computeStrides(t.shape),p=r.dataIdMap.get(c.dataId);if(o){let m=cn.computeFlatOffset(s,u);return t.dtype==="string"?p.stringBytes=l.slice(m,m+k.sizeFromShape(i)):r.typedArrayFromHeap(c).set(l.subarray(m,m+k.sizeFromShape(i))),c}if(t.dtype==="string"){let m=Dm(l,s,i,t.shape,t.dtype);return p.stringBytes=m,c}let d=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)fae(l,u[0],d,s,i);else if(h===3)gae(l,u[0],u[1],d,s,i);else if(h===4)yae(l,u[0],u[1],u[2],d,s,i);else{let m=Dm(l,s,i,t.shape,t.dtype);d.set(m)}return c}function fae(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let c=i;c<l;c++){let u=c*t+o;n.set(e.subarray(u,u+r[1]),s),s+=r[1]}}function gae(e,t,n,a,r,s){let i=0,o=r[0],l=r[1],c=r[2],u=o+s[0],p=l+s[1];for(let d=o;d<u;d++)for(let h=l;h<p;h++){let m=d*t+h*n+c;a.set(e.subarray(m,m+s[2]),i),i+=s[2]}}function 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a=n/Math.max(t,e);return new gn(Math.round(e*a),Math.round(t*a))}function Zi(e){return e.reduce((t,n)=>t.add(n),new De(0,0)).div(new De(e.length,e.length))}function ir(e,t,n){return Array(e).fill(0).map((a,r)=>t+r*n)}function Oa(e){return!!e&&e!==Infinity&&e!==-Infinity&&!Number.isNaN(e)||e===0}function yu(e){return Oa(e)&&e>=0&&e<=1}var De=class{constructor(t,n){this._x=t,this._y=n}get x(){return this._x}get y(){return this._y}add(t){return new De(this.x+t.x,this.y+t.y)}sub(t){return new De(this.x-t.x,this.y-t.y)}mul(t){return new De(this.x*t.x,this.y*t.y)}div(t){return new De(this.x/t.x,this.y/t.y)}abs(){return new De(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(this.x**2+this.y**2)}floor(){return new De(Math.floor(this.x),Math.floor(this.y))}};var ut=class{static isRect(t){return!!t&&[t.x,t.y,t.width,t.height].every(Oa)}static assertIsValidBox(t,n,a=!1){if(!ut.isRect(t))throw new Error(`${n} - invalid box: ${JSON.stringify(t)}, expected object with properties x, 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De(this.left,this.bottom)}get bottomRight(){return new De(this.right,this.bottom)}round(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.round(s));return new ut({x:t,y:n,width:a,height:r})}floor(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.floor(s));return new ut({x:t,y:n,width:a,height:r})}toSquare(){let{x:t,y:n,width:a,height:r}=this,s=Math.abs(a-r);return a<r&&(t-=s/2,a+=s),r<a&&(n-=s/2,r+=s),new ut({x:t,y:n,width:a,height:r})}rescale(t){let n=af(t)?t.width:t,a=af(t)?t.height:t;return new ut({x:this.x*n,y:this.y*a,width:this.width*n,height:this.height*a})}pad(t,n){let[a,r,s,i]=[this.x-t/2,this.y-n/2,this.width+t,this.height+n];return new ut({x:a,y:r,width:s,height:i})}clipAtImageBorders(t,n){let{x:a,y:r,right:s,bottom:i}=this,o=Math.max(a,0),l=Math.max(r,0),c=s-o,u=i-l,p=Math.min(c,t-o),d=Math.min(u,n-l);return new ut({x:o,y:l,width:p,height:d}).floor()}shift(t,n){let{width:a,height:r}=this,s=this.x+t,i=this.y+n;return new ut({x:s,y:i,width:a,height:r})}padAtBorders(t,n){let a=this.width+1,r=this.height+1,s=1,i=1,o=a,l=r,c=this.left,u=this.top,p=this.right,d=this.bottom;return p>n&&(o=-p+n+a,p=n),d>t&&(l=-d+t+r,d=t),c<1&&(l=2-c,c=1),u<1&&(l=2-u,u=1),{dy:i,edy:l,dx:s,edx:o,y:u,ey:d,x:c,ex:p,w:a,h:r}}calibrate(t){return new ut({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var bu=class extends ut{constructor(t,n,a,r,s=!1){super({left:t,top:n,right:a,bottom:r},s)}};var ds=class{constructor(t,n,a,r,s){this._imageDims=new gn(s.width,s.height),this._score=t,this._classScore=n,this._className=a,this._box=new ut(r).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return 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t=this.getRefPointsForAlignment(),[n,a,r]=t,s=p=>r.sub(p).magnitude(),i=(s(n)+s(a))/2,o=Math.floor(i/dre),l=Zi(t),c=Math.floor(Math.max(0,l.x-cre*o)),u=Math.floor(Math.max(0,l.y-pre*o));return new xu(c,u,Math.min(o,this.imageWidth+c),Math.min(o,this.imageHeight+u))}alignMinBbox(t){let n=ow(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var H2=class extends ta{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],Zi([t[3],t[4]])]}};var vu=class extends ta{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Zi)}};var Fp=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Yi(this.distance)})`:""}`}};var $p=class extends ut{static assertIsValidLabeledBox(t,n){if(ut.assertIsValidBox(t,n),!Oa(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var _r=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(a=>!(a instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return 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lr;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(lr||(lr={}));var Dp=class{constructor(t={}){let{anchorPosition:n,backgroundColor:a,fontColor:r,fontSize:s,fontStyle:i,padding:o}=t;this.anchorPosition=n||lr.TOP_LEFT,this.backgroundColor=a||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=s||14,this.fontStyle=i||"Georgia",this.padding=o||4}},hs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof hs?t.text:t,this.anchor=n,this.options=new Dp(a)}measureWidth(t){let{padding:n}=this.options;return this.text.map(a=>t.measureText(a).width).reduce((a,r)=>a<r?r:a,0)+2*n}measureHeight(){let{fontSize:t,padding:n}=this.options;return this.text.length*t+2*n}getUpperLeft(t,n){let{anchorPosition:a}=this.options,r=a===lr.BOTTOM_RIGHT||a===lr.TOP_RIGHT,s=a===lr.BOTTOM_LEFT||a===lr.BOTTOM_RIGHT,i=this.measureWidth(t),o=this.measureHeight(),l=r?this.anchor.x-i:this.anchor.x,c=s?this.anchor.y-o:this.anchor.y;if(n){let{width:u,height:p}=n,d=Math.max(Math.min(l,u-i),0),h=Math.max(Math.min(c,p-o),0);return{x:d,y:h}}return{x:l,y:c}}draw(t){let n=Qi(t),a=Cn(n),{backgroundColor:r,fontColor:s,fontSize:i,fontStyle:o,padding:l}=this.options;a.font=`${i}px ${o}`;let c=this.measureWidth(a),u=this.measureHeight();a.fillStyle=r;let p=this.getUpperLeft(a,n);a.fillRect(p.x,p.y,c,u),a.fillStyle=s,this.text.forEach((d,h)=>{let m=l+p.x,f=l+p.y+(h+1)*i;a.fillText(d,m,f)})}};var gw=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:lr.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Dp({...i,...s})}},sf=class{constructor(t,n={}){this.box=new ut(t),this.options=new gw(n)}draw(t){let n=Cn(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:c}=this.options;c&&new hs([c],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function yre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof bt?a.score:or(a)?a.detection.score:void 0,s=a instanceof bt?a.box:or(a)?a.detection.box:new ut(a),i=r?`${Yi(r)}`:void 0;new sf(s,{label:i}).draw(e)})}function Rp(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function yw(e){return new Promise((t,n)=>{if(e instanceof st.getEnv().Canvas||Rp(e))return t(null);function a(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function 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l=n(`${o}/sub`,1),c=n(`${o}/truediv`,1);return{sub:l,truediv:c}}function r(o){let l=n(`${o}/filters`,4),c=n(`${o}/bias`,1);return{filters:l,bias:c}}function s(o){let l=r(`${o}/conv`),c=a(`${o}/bn`);return{conv:l,bn:c}}let i=Su(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function CC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Wre(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return yn(e,n),{params:i,paramMappings:n}}var ur=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Rw=class extends an{constructor(t){super("TinyYolov2");Dw(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Fr(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Fr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=Fr(a,n.conv6),a=Fr(a,n.conv7),no(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Au(no(t,n.conv0,"valid",!1)):$r(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=n.conv6?$r(a,n.conv6):a,a=n.conv7?$r(a,n.conv7):a,no(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return D(()=>{let r=ce(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?La(r,this.config.meanRgb):r,r=r.div(de(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await gt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new ur(n),s=await gt(t),i=await this.forwardInput(s,a),o=D(()=>dt(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},c=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let u=c.map(g=>g.box),p=c.map(g=>g.score),d=c.map(g=>g.classScore),h=c.map(g=>this.config.classes[g.label]);return lw(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new ds(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return CC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Rw.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return SC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?Ta(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):de(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;y<c;y++)for(let b=0;b<c;b++)for(let v=0;v<u;v++){let x=Ap(f[y][b][v][0]);if(!a||x>a){let N=(b+Ap(g[y][b][v][0]))/c*o,T=(y+Ap(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,F=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,$=N-C/2,P=T-F/2,B={row:y,col:b,anchor:v},{classScore:W,label:G}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new bu($,P,$+C,P+F),score:x,classScore:x*W,label:G,...B})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Fu=Rw;Fu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var $u=class extends Fu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:vC,classes:["face"],...t?{anchors:kC,meanRgb:IC}:{anchors:wC,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new bt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?TC:NC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Vre(e,t=!0){let n=new $u(t);return n.extractWeights(e),n}var Tf=class extends ur{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var wa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function io(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>ao(l)?r(l):l.detection),i=a||(t instanceof Ae?await Iu(t,s):await ku(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ae&&l.dispose()),o}async function Du(e,t,n,a,r){return io([e],t,async s=>n(s[0]),a,r)}var _C=.4,EC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],AC=[117.001,114.697,97.404];var Ru=class extends Fu{constructor(){let t={withSeparableConvs:!0,iouThreshold:_C,classes:["face"],anchors:EC,meanRgb:AC,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new bt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var nt={ssdMobilenetv1:new so,tinyFaceDetector:new Ru,tinyYolov2:new $u,faceLandmark68Net:new _u,faceLandmark68TinyNet:new xf,faceRecognitionNet:new Eu,faceExpressionNet:new gf,ageGenderNet:new bf},FC=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),Ure=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),Gre=(e,t)=>nt.tinyYolov2.locateFaces(e,t),$C=e=>nt.faceLandmark68Net.detectLandmarks(e),Hre=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),jre=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),qre=e=>nt.faceExpressionNet.predictExpressions(e),Kre=e=>nt.ageGenderNet.predictAgeAndGender(e),DC=e=>nt.ssdMobilenetv1.load(e),Xre=e=>nt.tinyFaceDetector.load(e),Yre=e=>nt.tinyYolov2.load(e),Zre=e=>nt.faceLandmark68Net.load(e),Jre=e=>nt.faceLandmark68TinyNet.load(e),Qre=e=>nt.faceRecognitionNet.load(e),ese=e=>nt.faceExpressionNet.load(e),tse=e=>nt.ageGenderNet.load(e),nse=DC,ase=FC,rse=$C;var Mw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ou=class extends Mw{async run(){let t=await this.parentTask,n=await io(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>yf(a,n[r]))}withAgeAndGender(){return new Mu(this,this.input)}},Lu=class extends Mw{async run(){let t=await this.parentTask;if(!t)return;let n=await Du(t,this.input,a=>nt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return yf(t,n)}withAgeAndGender(){return new Pu(this,this.input)}},uo=class extends Ou{withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},co=class extends Lu{withAgeAndGender(){return new lo(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Pw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Mu=class extends Pw{async run(){let t=await this.parentTask,n=await io(t,this.input,async a=>Promise.all(a.map(r=>nt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return kf(If(a,i,o),s)})}withFaceExpressions(){return new Ou(this,this.input)}},Pu=class extends Pw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Du(t,this.input,s=>nt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return kf(If(t,a,r),n)}withFaceExpressions(){return new Lu(this,this.input)}},oo=class extends Mu{withFaceExpressions(){return new uo(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},lo=class extends Pu{withFaceExpressions(){return new co(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Sf=class extends wa{constructor(t,n){super();this.parentTask=t;this.input=n}},fs=class extends Sf{async run(){let t=await this.parentTask;return(await io(t,this.input,a=>Promise.all(a.map(r=>nt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>wf(t[r],a))}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}},gs=class extends Sf{async run(){let t=await this.parentTask;if(!t)return;let n=await Du(t,this.input,a=>nt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return wf(t,n)}withFaceExpressions(){return new co(this,this.input)}withAgeAndGender(){return new lo(this,this.input)}};var Cf=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},_f=class extends Cf{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ae?await Iu(this.input,n):await ku(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ae&&s.dispose()),t.map((s,i)=>Cu(s,r[i]))}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptors(){return new fs(this,this.input)}},Ef=class extends Cf{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ae?await Iu(this.input,[n]):await ku(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ae&&s.dispose()),Cu(t,r)}withFaceExpressions(){return new co(this,this.input)}withAgeAndGender(){return new lo(this,this.input)}withFaceDescriptor(){return new gs(this,this.input)}};var Af=class extends wa{constructor(t,n=new va){super();this.input=t;this.options=n}},Vp=class extends Af{async run(){let{input:t,options:n}=this,a=n instanceof Tf?r=>nt.tinyFaceDetector.locateFaces(r,n):n instanceof va?r=>nt.ssdMobilenetv1.locateFaces(r,n):n instanceof ur?r=>nt.tinyYolov2.locateFaces(r,n):null;if(!a)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return a(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>Ji({},a)))})}withFaceLandmarks(t=!1){return new _f(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ou(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Mu(this.runAndExtendWithFaceDetections(),this.input)}},Ff=class extends Af{async run(){let t=await new Vp(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Ji({},n):void 0)})}withFaceLandmarks(t=!1){return new Ef(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Lu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Pu(this.runAndExtendWithFaceDetection(),this.input)}};function sse(e,t=new va){return new Ff(e,t)}function $f(e,t=new va){return new Vp(e,t)}async function RC(e,t){return $f(e,new va(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function ise(e,t={}){return $f(e,new ur(t)).withFaceLandmarks().withFaceDescriptors()}var ose=RC;function Ow(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var Df=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof _r)return i;if(i instanceof Float32Array)return new _r(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new _r(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>Ow(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Fp(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distance<a.distance?n:a)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this.distanceThreshold?n:new Fp("unknown",n.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>_r.fromJSON(a));return new Df(n,t.distanceThreshold)}};function lse(e){let t=new Ru;return t.extractWeights(e),t}function MC(e,t){let{width:n,height:a}=new gn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>MC(r,{width:n,height:a}));if(ao(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Cu(Ji(e,r),s)}return or(e)?Ji(e,e.detection.forSize(n,a)):e instanceof ta||e instanceof bt?e.forSize(n,a):e}var use=typeof process!="undefined",cse=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",pse={faceapi:Q2,node:use,browser:cse};export{bf as AgeGenderNet,bu as BoundingBox,ut as Box,wa as ComposableTask,fs as ComputeAllFaceDescriptorsTask,Sf as ComputeFaceDescriptorsTaskBase,gs as ComputeSingleFaceDescriptorTask,_f as DetectAllFaceLandmarksTask,Vp as DetectAllFacesTask,Cf as DetectFaceLandmarksTaskBase,Af as DetectFacesTaskBase,Ef as DetectSingleFaceLandmarksTask,Ff as DetectSingleFaceTask,gn as Dimensions,Iw as FACE_EXPRESSION_LABELS,bt as FaceDetection,xC as FaceDetectionNet,gf as FaceExpressionNet,ms as FaceExpressions,_u as FaceLandmark68Net,xf as FaceLandmark68TinyNet,oC as FaceLandmarkNet,ta as FaceLandmarks,H2 as FaceLandmarks5,vu as FaceLandmarks68,Fp as FaceMatch,Df as FaceMatcher,Eu as FaceRecognitionNet,Ar as Gender,$p as LabeledBox,_r as LabeledFaceDescriptors,Er as NetInput,an as NeuralNetwork,ds as ObjectDetection,De as Point,j2 as PredictedBox,xu as Rect,so as SsdMobilenetv1,va as SsdMobilenetv1Options,Ru as TinyFaceDetector,Tf as TinyFaceDetectorOptions,$u as TinyYolov2,ur as TinyYolov2Options,ose as allFaces,RC as allFacesSsdMobilenetv1,ise as allFacesTinyYolov2,yw as awaitMediaLoaded,bw as bufferToImage,jre as computeFaceDescriptor,wu as createCanvas,Mp as createCanvasFromMedia,zre as createFaceDetectionNet,_re as createFaceRecognitionNet,bC as createSsdMobilenetv1,lse as createTinyFaceDetector,Vre as createTinyYolov2,$f as detectAllFaces,$C as detectFaceLandmarks,Hre as detectFaceLandmarksTiny,rse as detectLandmarks,sse as detectSingleFace,Cw as draw,st as env,Ow as euclideanDistance,kf as extendWithAge,wf as extendWithFaceDescriptor,Ji as extendWithFaceDetection,yf as extendWithFaceExpressions,Cu as extendWithFaceLandmarks,If as extendWithGender,Iu as extractFaceTensors,ku as extractFaces,bre as fetchImage,ww as fetchJson,xre as fetchNetWeights,to as fetchOrThrow,Cn as getContext2dOrThrow,eo as getMediaDimensions,xw as imageTensorToCanvas,vw as imageToSquare,ure as inverseSigmoid,iw as iou,of as isMediaElement,Rp as isMediaLoaded,Ere as isWithAge,or as isWithFaceDetection,Nw as isWithFaceExpressions,ao as isWithFaceLandmarks,Are as isWithGender,tse as loadAgeGenderModel,nse as loadFaceDetectionModel,ese as loadFaceExpressionModel,Zre as loadFaceLandmarkModel,Jre as loadFaceLandmarkTinyModel,Qre as loadFaceRecognitionModel,DC as loadSsdMobilenetv1Model,Xre as loadTinyFaceDetectorModel,Yre as loadTinyYolov2Model,kw as loadWeightMap,ase as locateFaces,vre as matchDimensions,ow as minBbox,nt as nets,lw as nonMaxSuppression,La as normalize,uw as padToSquare,Kre as predictAgeAndGender,qre as recognizeFaceExpressions,MC as resizeResults,Qi as resolveInput,lre as shuffleArray,Ap as sigmoid,FC as ssdMobilenetv1,Ug as tf,Ure as tinyFaceDetector,Gre as tinyYolov2,gt as toNetInput,nw as utils,Dw as validateConfig,pse as version};
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/face-api.esm.js.map b/dist/face-api.esm.js.map
index fce088c..77996e5 100644
--- a/dist/face-api.esm.js.map
+++ b/dist/face-api.esm.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
-  "sources": ["../src/env/isNodejs.ts", "../node_modules/node-fetch/browser.js", "../node_modules/seedrandom/lib/alea.js", "../node_modules/seedrandom/lib/xor128.js", "../node_modules/seedrandom/lib/xorwow.js", "../node_modules/seedrandom/lib/xorshift7.js", "../node_modules/seedrandom/lib/xor4096.js", "../node_modules/seedrandom/lib/tychei.js", "(disabled):crypto", "../node_modules/seedrandom/seedrandom.js", "../node_modules/seedrandom/index.js", "../node_modules/string_decoder/lib/string_decoder.js", "(disabled):path", "(disabled):worker_threads", "(disabled):perf_hooks", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js", "../node_modules/@tensorflow/tfjs-core/src/backends/backend.ts", "../node_modules/@tensorflow/tfjs-core/src/util_base.ts", "../node_modules/@tensorflow/tfjs-core/src/environment.ts", "../node_modules/@tensorflow/tfjs-core/src/global_util.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_names.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/util.ts", "../node_modules/@tensorflow/tfjs-core/src/profiler.ts", "../node_modules/@tensorflow/tfjs-core/src/tape.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_format.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util.ts", "../node_modules/@tensorflow/tfjs-core/src/types.ts", "../node_modules/@tensorflow/tfjs-core/src/engine.ts", "../node_modules/@tensorflow/tfjs-core/src/device_util.ts", "../node_modules/@tensorflow/tfjs-core/src/flags.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util_env.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/operation.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/complex.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor_ops_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/io/types.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io_utils.ts", "../node_modules/@tensorflow/tfjs-core/src/io/router_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/io/indexed_db.ts", "../node_modules/@tensorflow/tfjs-core/src/io/local_storage.ts", "../node_modules/@tensorflow/tfjs-core/src/io/model_management.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_browser.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_node.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/buffer.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cast.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/clone.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/print.ts", "../node_modules/@tensorflow/tfjs-core/src/base_side_effects.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io.ts", "../node_modules/@tensorflow/tfjs-core/src/io/browser_files.ts", "../node_modules/@tensorflow/tfjs-core/src/io/progress.ts", "../node_modules/@tensorflow/tfjs-core/src/io/weights_loader.ts", "../node_modules/@tensorflow/tfjs-core/src/io/http.ts", "../node_modules/@tensorflow/tfjs-core/src/io/passthrough.ts", "../node_modules/@tensorflow/tfjs-core/src/math.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mat_mul.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/one_hot.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/confusion_matrix.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/browser.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/gather_nd_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/scatter_nd_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/slice_util.ts", "../node_modules/@tensorflow/tfjs-core/src/serialization.ts", "../node_modules/@tensorflow/tfjs-core/src/test_util.ts", "../node_modules/@tensorflow/tfjs-core/src/version.ts", "../node_modules/@tensorflow/tfjs-core/src/globals.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/add.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/floorDiv.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/div.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mul.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/abs.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/acos.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/acosh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/add_n.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/all.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/any.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/arg_max.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/arg_min.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/asin.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/asinh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atan.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atan2.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atanh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/reshape.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/avg_pool.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/avg_pool_3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/sigmoid.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/slice.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tanh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/basic_lstm_cell.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batch_to_space_nd.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm3d.ts", 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"../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/NeuralNetwork.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeightMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeightMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeightMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeightMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeightMap.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeightMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts", "../src/index.ts"],
-  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version;\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 moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var 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if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(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 shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var 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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)}var WASM_PAGE_SIZE=65536;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 STACK_BASE=5256480,STACKTOP=STACK_BASE,STACK_MAX=13600,DYNAMIC_BASE=5256480,DYNAMICTOP_PTR=12672;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_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_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"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_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}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){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;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 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 Math_ceil=Math.ceil;var Math_floor=Math.floor;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+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}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(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}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)&&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()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;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\"){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\");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();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13584;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(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>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 __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";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 __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";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={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12832;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13072;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___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+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(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+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>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){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},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\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(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.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}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){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},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){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;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);__ATEXIT__.unshift({func:func,arg: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_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){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{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}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];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}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){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;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(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,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)}},inEventHandler:0,currentEventHandler:null,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_async_queue_on_thread_(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_async_queue_on_thread_(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){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}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}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},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},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],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\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){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 contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};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.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() 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,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_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_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;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;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>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+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}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 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79: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: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();else PThread.initWorker();var GLctx;GL.init();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,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=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 _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"qa\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"ra\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"sa\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"ta\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"ua\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"va\"]).apply(null,arguments)};var 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_StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"Sa\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Ta\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Ua\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Va\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Wa\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Xa\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Ya\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Za\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"_a\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"$a\"]).apply(null,arguments)};var ___em_js__initPthreadsJS=Module[\"___em_js__initPthreadsJS\"]=function(){return(___em_js__initPthreadsJS=Module[\"___em_js__initPthreadsJS\"]=Module[\"asm\"][\"ab\"]).apply(null,arguments)};var ___errno_location=Module[\"___errno_location\"]=function(){return(___errno_location=Module[\"___errno_location\"]=Module[\"asm\"][\"bb\"]).apply(null,arguments)};var _emscripten_get_global_libc=Module[\"_emscripten_get_global_libc\"]=function(){return(_emscripten_get_global_libc=Module[\"_emscripten_get_global_libc\"]=Module[\"asm\"][\"cb\"]).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\"][\"hb\"]).apply(null,arguments)};var _emscripten_main_browser_thread_id=Module[\"_emscripten_main_browser_thread_id\"]=function(){return(_emscripten_main_browser_thread_id=Module[\"_emscripten_main_browser_thread_id\"]=Module[\"asm\"][\"ib\"]).apply(null,arguments)};var _emscripten_async_run_in_main_thread=Module[\"_emscripten_async_run_in_main_thread\"]=function(){return(_emscripten_async_run_in_main_thread=Module[\"_emscripten_async_run_in_main_thread\"]=Module[\"asm\"][\"jb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread=Module[\"_emscripten_sync_run_in_main_thread\"]=function(){return(_emscripten_sync_run_in_main_thread=Module[\"_emscripten_sync_run_in_main_thread\"]=Module[\"asm\"][\"kb\"]).apply(null,arguments)};var 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_emscripten_sync_run_in_main_thread_xprintf_varargs=Module[\"_emscripten_sync_run_in_main_thread_xprintf_varargs\"]=function(){return(_emscripten_sync_run_in_main_thread_xprintf_varargs=Module[\"_emscripten_sync_run_in_main_thread_xprintf_varargs\"]=Module[\"asm\"][\"ob\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_3=Module[\"_emscripten_sync_run_in_main_thread_3\"]=function(){return(_emscripten_sync_run_in_main_thread_3=Module[\"_emscripten_sync_run_in_main_thread_3\"]=Module[\"asm\"][\"pb\"]).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\"][\"qb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_5=Module[\"_emscripten_sync_run_in_main_thread_5\"]=function(){return(_emscripten_sync_run_in_main_thread_5=Module[\"_emscripten_sync_run_in_main_thread_5\"]=Module[\"asm\"][\"rb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_6=Module[\"_emscripten_sync_run_in_main_thread_6\"]=function(){return(_emscripten_sync_run_in_main_thread_6=Module[\"_emscripten_sync_run_in_main_thread_6\"]=Module[\"asm\"][\"sb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_7=Module[\"_emscripten_sync_run_in_main_thread_7\"]=function(){return(_emscripten_sync_run_in_main_thread_7=Module[\"_emscripten_sync_run_in_main_thread_7\"]=Module[\"asm\"][\"tb\"]).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\"][\"ub\"]).apply(null,arguments)};var _emscripten_async_queue_on_thread_=Module[\"_emscripten_async_queue_on_thread_\"]=function(){return(_emscripten_async_queue_on_thread_=Module[\"_emscripten_async_queue_on_thread_\"]=Module[\"asm\"][\"vb\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"wb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"xb\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"yb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"zb\"]).apply(null,arguments)};var dynCall_vi=Module[\"dynCall_vi\"]=function(){return(dynCall_vi=Module[\"dynCall_vi\"]=Module[\"asm\"][\"Ab\"]).apply(null,arguments)};var dynCall_v=Module[\"dynCall_v\"]=function(){return(dynCall_v=Module[\"dynCall_v\"]=Module[\"asm\"][\"Bb\"]).apply(null,arguments)};var dynCall_ii=Module[\"dynCall_ii\"]=function(){return(dynCall_ii=Module[\"dynCall_ii\"]=Module[\"asm\"][\"Cb\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"_pthread_self\"]=_pthread_self;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};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();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()()}}if(!ENVIRONMENT_IS_PTHREAD)noExitRuntime=true;if(!ENVIRONMENT_IS_PTHREAD)run();\n\n\n  return WasmBackendModuleThreadedSimd\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModuleThreadedSimd;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModuleThreadedSimd; });\n    else 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 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 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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(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 shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function 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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)}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_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;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){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;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 exitRuntime(){runtimeExited=true}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 Math_ceil=Math.ceil;var Math_floor=Math.floor;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+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}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(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}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)&&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()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);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\"){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\");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();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var 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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, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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 threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;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:selfThreadId})}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};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;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)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}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;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;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[\"dynCall_ii\"](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\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){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.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\");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, "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false,\n) {\n  ctx.beginPath();\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx];\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(x, y);\n  });\n\n  if (isClosed) {\n    const from = points[points.length - 1];\n    const to = points[0];\n    if (!from || !to) {\n      return;\n    }\n\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(to.x, to.y);\n  }\n\n  ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = 10 ** prec;\n  return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width);\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n  private _width: number\n\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n    }\n\n    this._width = width;\n    this._height = height;\n  }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height);\n  }\n}\n", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x;\n    this._y = y;\n  }\n\n  get x(): number { return this._x; }\n\n  get y(): number { return this._y; }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y);\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y);\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y);\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y);\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y));\n  }\n\n  public magnitude(): number {\n    return Math.sqrt((this.x ** 2) + (this.y ** 2));\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y));\n  }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n    }\n  }\n\n  private _x: number\n\n  private _y: number\n\n  private _width: number\n\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any;\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n    Box.assertIsValidBox({\n      x, y, width, height,\n    }, 'Box.constructor', allowNegativeDimensions);\n\n    this._x = x;\n    this._y = y;\n    this._width = width;\n    this._height = height;\n  }\n\n  public get x(): number { return this._x; }\n\n  public get y(): number { return this._y; }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public get left(): number { return this.x; }\n\n  public get top(): number { return this.y; }\n\n  public get right(): number { return this.x + this.width; }\n\n  public get bottom(): number { return this.y + this.height; }\n\n  public get area(): number { return this.width * this.height; }\n\n  public get topLeft(): Point { return new Point(this.left, this.top); }\n\n  public get topRight(): Point { return new Point(this.right, this.top); }\n\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n  public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.round(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.floor(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public toSquare(): Box<BoxType> {\n    let {\n      x, y, width, height,\n    } = this;\n    const diff = Math.abs(width - height);\n    if (width < height) {\n      x -= (diff / 2);\n      width += diff;\n    }\n    if (height < width) {\n      y -= (diff / 2);\n      height += diff;\n    }\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY,\n    });\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    const [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY,\n    ];\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const {\n      x, y, right, bottom,\n    } = this;\n    const clippedX = Math.max(x, 0);\n    const clippedY = Math.max(y, 0);\n\n    const newWidth = right - clippedX;\n    const newHeight = bottom - clippedY;\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n    return (new Box({\n      x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n    })).floor();\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this;\n    const x = this.x + sx;\n    const y = this.y + sy;\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1;\n    const h = this.height + 1;\n\n    const dx = 1;\n    const dy = 1;\n    let edx = w;\n    let edy = h;\n\n    let x = this.left;\n    let y = this.top;\n    let ex = this.right;\n    let ey = this.bottom;\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w;\n      ex = imageWidth;\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h;\n      ey = imageHeight;\n    }\n    if (x < 1) {\n      edy = 2 - x;\n      x = 1;\n    }\n    if (y < 1) {\n      edy = 2 - y;\n      y = 1;\n    }\n\n    return {\n      dy, edy, dx, edx, y, ey, x, ex, w, h,\n    };\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height),\n    }).toSquare().round();\n  }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({\n      left, top, right, bottom,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n\n  private _classScore: number\n\n  private _className: string\n\n  private _box: Rect\n\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions,\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n    this._score = score;\n    this._classScore = classScore;\n    this._className = className;\n    this._box = new Box(relativeBox).rescale(this._imageDims);\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n\n  public get className(): string { return this._className; }\n\n  public get box(): Box { return this._box; }\n\n  public get imageDims(): Dimensions { return this._imageDims; }\n\n  public get imageWidth(): number { return this.imageDims.width; }\n\n  public get imageHeight(): number { return this.imageDims.height; }\n\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height },\n    );\n  }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions,\n  ) {\n    super(score, score, '', relativeBox, imageDims);\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height);\n    return new FaceDetection(score, relativeBox, imageDims);\n  }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n  const interSection = width * height;\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map((pt) => pt.x);\n  const ys = pts.map((pt) => pt.y);\n  const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n  const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n  const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n  const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n  return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true,\n): number[] {\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map((c) => c.boxIndex);\n\n  const pick: number[] = [];\n\n  while (indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number;\n    pick.push(curr);\n\n    const indices = indicesSortedByScore;\n\n    const outputs: number[] = [];\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i];\n\n      const currBox = boxes[curr];\n      const idxBox = boxes[idx];\n\n      outputs.push(iou(currBox, idxBox, isIOU));\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold,\n    );\n  }\n\n  return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb;\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n    return tf.sub(x, avg_rgb);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [height, width] = imgTensor.shape.slice(1);\n    if (height === width) {\n      return imgTensor;\n    }\n\n    const dimDiff = Math.abs(height - width);\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n    const paddingAxis = height > width ? 2 : 1;\n\n    const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice();\n      paddingTensorShape[paddingAxis] = paddingAmountLocal;\n      return tf.fill(paddingTensorShape, 0, 'float32');\n    };\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount);\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null;\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend,\n    ]\n      .filter((t) => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n    return tf.concat(tensorsToStack, paddingAxis);\n  });\n}\n", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice();\n  for (let i = array.length - 1; i > 0; i--) {\n    const j = Math.floor(Math.random() * (i + 1));\n    const x = array[i];\n    array[i] = array[j];\n    array[j] = x;\n  }\n  return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({\n      x, y, width, height,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n\n  protected _positions: Point[]\n\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0),\n  ) {\n    const { width, height } = imgDims;\n    this._imgDims = new Dimensions(width, height);\n    this._shift = shift;\n    this._positions = relativeFaceLandmarkPositions.map(\n      (pt) => pt.mul(new Point(width, height)).add(shift),\n    );\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n  public get imageWidth(): number { return this._imgDims.width; }\n\n  public get imageHeight(): number { return this._imgDims.height; }\n\n  public get positions(): Point[] { return this._positions; }\n\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n    );\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height },\n    );\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y),\n    );\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y);\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection);\n\n      return this.shiftBy(box.x, box.y).align(null, options);\n    }\n\n    const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n    if (useDlibAlignment) {\n      return this.alignDlib();\n    }\n\n    return this.alignMinBbox(minBoxPadding);\n  }\n\n  private alignDlib(): Box {\n    const centers = this.getRefPointsForAlignment();\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n    const size = Math.floor(eyeToMouthDist / relScale);\n\n    const refPoint = getCenterPoint(centers);\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions);\n    return box.pad(box.width * padding, box.height * padding);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class');\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions;\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]]),\n    ];\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17);\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22);\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27);\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36);\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42);\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48);\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth(),\n    ].map(getCenterPoint);\n  }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label;\n    this._distance = distance;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get distance(): number { return this._distance; }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n  }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee);\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box);\n    this._label = label;\n  }\n\n  public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n  private _label: string\n\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n    }\n\n    this._label = label;\n    this._descriptors = descriptors;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get descriptors(): Float32Array[] { return this._descriptors; }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d)),\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee);\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n    }\n  }\n\n  private _score: number\n\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label);\n    this._score = score;\n    this._classScore = classScore;\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<\n  TSource\n>(\n  sourceObj: TSource,\n  detection: FaceDetection,\n): WithFaceDetection<TSource> {\n  const extension = { detection };\n  return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n  const fetch = window.fetch;\n  if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n  const readFile = () => {\n    throw new Error('readFile - filesystem not available for browser environment');\n  };\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile,\n  };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n  let requireFsError = '';\n\n  if (!fs) {\n    try {\n      // eslint-disable-next-line global-require\n      fs = require('fs');\n    } catch (err) {\n      requireFsError = err.toString();\n    }\n  }\n\n  const readFile = fs\n    ? (filePath: string) => new Promise<Buffer>((resolve, reject) => {\n      fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n    })\n    : () => {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n    };\n\n  return {\n    readFile,\n  };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n  // eslint-disable-next-line dot-notation\n  const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n  const Image = global.Image || global.HTMLImageElement;\n\n  const createCanvasElement = () => {\n    if (Canvas) return new Canvas();\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n  };\n\n  const createImageElement = () => {\n    if (Image) return new Image();\n    throw new Error('createImageElement - missing Image implementation for nodejs environment');\n  };\n\n  const fetch = global.fetch;\n  // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n  const fileSystem = createFileSystem();\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n    Image: Image || class {},\n    ImageData: global.ImageData || class {},\n    Video: global.HTMLVideoElement || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem,\n  };\n}\n", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n  }\n  return environment;\n}\n\nfunction setEnv(env: Environment) {\n  environment = env;\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) return setEnv(createBrowserEnv());\n  if (isNodejs()) return setEnv(createNodejsEnv());\n  return null;\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize();\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env;\n  environment.Canvas = Canvas;\n  environment.Image = Image;\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n  environment.createImageElement = env.createImageElement || (() => new Image());\n\n  environment.ImageData = env.ImageData || environment.ImageData;\n  environment.Video = env.Video || environment.Video;\n  environment.fetch = env.fetch || environment.fetch;\n  environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg);\n  }\n  return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg;\n  }\n\n  const canvas = resolveInput(canvasArg);\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n  }\n\n  const ctx = canvas.getContext('2d');\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null');\n  }\n\n  return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n  // eslint-disable-next-line no-unused-vars\n  TOP_LEFT = 'TOP_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  TOP_RIGHT = 'TOP_RIGHT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n\n  public backgroundColor: string\n\n  public fontColor: string\n\n  public fontSize: number\n\n  public fontStyle: string\n\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const {\n      anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = options;\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n    this.fontSize = fontSize || 14;\n    this.fontStyle = fontStyle || 'Georgia';\n    this.padding = padding || 4;\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n\n  public anchor : IPoint\n\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {},\n  ) {\n    // eslint-disable-next-line no-nested-ternary\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text);\n    this.anchor = anchor;\n    this.options = new DrawTextFieldOptions(options);\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options;\n    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options;\n    return this.text.length * fontSize + (2 * padding);\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options;\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n    const textFieldWidth = this.measureWidth(ctx);\n    const textFieldHeight = this.measureHeight();\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims;\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n      return { x: newX, y: newY };\n    }\n    return { x, y };\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg);\n    const ctx = getContext2dOrThrow(canvas);\n\n    const {\n      backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = this.options;\n\n    ctx.font = `${fontSize}px ${fontStyle}`;\n    const maxTextWidth = this.measureWidth(ctx);\n    const textHeight = this.measureHeight();\n\n    ctx.fillStyle = backgroundColor;\n    const upperLeft = this.getUpperLeft(ctx, canvas);\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x;\n      const y = padding + upperLeft.y + ((i + 1) * fontSize);\n      ctx.fillText(textLine, x, y);\n    });\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport {\n  AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,\n} from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n\n  public lineWidth: number\n\n  public drawLabelOptions: DrawTextFieldOptions\n\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const {\n      boxColor, lineWidth, label, drawLabelOptions,\n    } = options;\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n    this.lineWidth = lineWidth || 2;\n    this.label = label;\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor,\n    };\n    this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {},\n  ) {\n    this.box = new Box(box);\n    this.options = new DrawBoxOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const { boxColor, lineWidth } = this.options;\n\n    const {\n      x, y, width, height,\n    } = this.box;\n    ctx.strokeStyle = boxColor;\n    ctx.lineWidth = lineWidth;\n    ctx.strokeRect(x, y, width, height);\n\n    const { label } = this.options;\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n    }\n  }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>,\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n  detectionsArray.forEach((det) => {\n    // eslint-disable-next-line no-nested-ternary\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n    // eslint-disable-next-line no-nested-ternary\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n    const label = score ? `${round(score)}` : undefined;\n    new DrawBox(box, { label }).draw(canvasArg);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n  const { Image, Video } = env.getEnv();\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n  // eslint-disable-next-line consistent-return\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null);\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return;\n      // eslint-disable-next-line no-use-before-define\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      reject(e);\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return;\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      resolve(e);\n    }\n\n    media.addEventListener('load', onLoad);\n    media.addEventListener('error', onError);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n    const reader = new FileReader();\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n      const img = env.getEnv().createImageElement();\n      img.onload = () => resolve(img);\n      img.onerror = reject;\n      img.src = reader.result as string;\n    };\n    reader.onerror = reject;\n    reader.readAsDataURL(buf);\n  });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n  const { Image, Video } = env.getEnv();\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight);\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight);\n  }\n  return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n  const { createCanvasElement } = env.getEnv();\n  const canvas = createCanvasElement();\n  canvas.width = width;\n  canvas.height = height;\n  return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n  const { ImageData } = env.getEnv();\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet');\n  }\n\n  const { width, height } = dims || getMediaDimensions(media);\n  const canvas = createCanvas({ width, height });\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n  }\n  return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement,\n): Promise<HTMLCanvasElement> {\n  const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n  await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n  imgTensor3D.dispose();\n\n  return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n  const { Image, Canvas, Video } = env.getEnv();\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n  const { Image, Canvas } = env.getEnv();\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n  }\n\n  const dims = getMediaDimensions(input);\n  const scale = inputSize / Math.max(dims.height, dims.width);\n  const width = scale * dims.width;\n  const height = scale * dims.height;\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n  const offset = Math.abs(width - height) / 2;\n  const dx = centerImage && width < height ? offset : 0;\n  const dy = centerImage && height < width ? offset : 0;\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n  return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport {\n  computeReshapedDimensions, isTensor3D, isTensor4D, range,\n} from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n\n  private _canvases: HTMLCanvasElement[] = []\n\n  private _batchSize: number\n\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false,\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput;\n    this._batchSize = inputs.length;\n\n    inputs.forEach((input, idx) => {\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = input.shape;\n        return;\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0];\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n        }\n\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = (input as any).shape.slice(1);\n        return;\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n      this._canvases[idx] = canvas;\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n    });\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors;\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases;\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput;\n  }\n\n  public get batchSize(): number {\n    return this._batchSize;\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions;\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize;\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n    );\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx];\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0];\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1];\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n    }\n\n    const width = this.getInputWidth(batchIdx);\n    const height = this.getInputHeight(batchIdx);\n    return computeReshapedDimensions({ width, height }, this.inputSize);\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n    this._inputSize = inputSize;\n\n    return tf.tidy(() => {\n      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n        const input = this.getInput(batchIdx);\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>();\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3);\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n      });\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n\n      return batchTensor;\n    });\n  }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs;\n  }\n\n  const inputArgArray = Array.isArray(inputs)\n    ? inputs\n    : [inputs];\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input');\n  }\n\n  const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n\n  const inputArray = inputArgArray.map(resolveInput);\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0];\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n      }\n    }\n  });\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)),\n  );\n\n  return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>,\n): Promise<HTMLCanvasElement[]> {\n  const { Canvas } = env.getEnv();\n\n  let canvas = input as HTMLCanvasElement;\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input);\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported');\n    }\n\n    const tensorOrCanvas = netInput.getInput(0);\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas);\n  }\n\n  const ctx = getContext2dOrThrow(canvas);\n  const boxes = detections.map(\n    (det) => (det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det),\n  )\n    .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n\n  return boxes.map(({\n    x, y, width, height,\n  }) => {\n    const faceImg = createCanvas({ width, height });\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n    return faceImg;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>,\n): Promise<tf.Tensor3D[]> {\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported');\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n    const boxes = detections.map(\n      (det) => (det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det),\n    )\n      .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n    const faceTensors = boxes.map(({\n      x, y, width, height,\n    }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n    return faceTensors;\n  });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n  url: string,\n  // eslint-disable-next-line no-undef\n  init?: RequestInit,\n): Promise<Response> {\n  const { fetch } = env.getEnv();\n  const res = await fetch(url, init);\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n  }\n  return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri);\n  const blob = await (res).blob();\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n  }\n  return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename,\n    };\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`,\n    };\n  }\n  // eslint-disable-next-line no-nested-ternary\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter((s) => s);\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename;\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri);\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference;\n  input.width = width;\n  input.height = height;\n  return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork<TNetParams> {\n  constructor(name: string) {\n    this._name = name;\n  }\n\n  protected _params: TNetParams | undefined = undefined\n\n  protected _paramMappings: ParamMapping[] = []\n\n  public _name: any;\n\n  public get params(): TNetParams | undefined { return this._params; }\n\n  public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n  public get isLoaded(): boolean { return !!this.params; }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    return obj[objProp];\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    obj[objProp].dispose();\n    obj[objProp] = tensor;\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath),\n    }));\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable());\n    });\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync());\n      variable.dispose();\n      this.reassignParamFromPath(path, tensor);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach((param) => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`);\n      }\n      param.tensor.dispose();\n    });\n    this._params = undefined;\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr)),\n    );\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl);\n      return;\n    }\n    await this.loadFromUri(weightsOrUrl);\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`);\n    }\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n    }\n    const { readFile } = env.getEnv();\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)));\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n    const manifest = JSON.parse((await readFile(manifestUri)).toString());\n    const weightMap = await loadWeights(manifest, modelBaseUri);\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const { paramMappings, params } = this.extractParams(weights);\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error('traversePropertyPath - model has no loaded params');\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      // eslint-disable-next-line no-prototype-builtins\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n      }\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n    }, { nextObj: this.params });\n\n    const { obj, objProp } = result;\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n    }\n\n    return { obj, objProp };\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number],\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n    out = tf.add(out, params.bias);\n    return out;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n  });\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias,\n    ) as tf.Tensor4D;\n\n    return withRelu ? tf.relu(out) : out;\n  });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach((path) => {\n    if (!paramMappings.some((pm) => pm.originalPath === path)) {\n      weightMap[path].dispose();\n    }\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams => {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): FCParams => {\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias,\n    };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  // eslint-disable-next-line no-useless-constructor\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    public depthwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public pointwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public bias: tf.Tensor1D,\n  // eslint-disable-next-line no-empty-function\n  ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n\nexport function loadSeparableConvParamsFactory(\n  // eslint-disable-next-line no-unused-vars\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T,\n) {\n  return (prefix: string): SeparableConvParams => {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4);\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  return (originalPath: string, paramRank: number, mappedPath?: string) => {\n    const tensor = weightMap[originalPath];\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath },\n    );\n\n    return tensor;\n  };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights;\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights);\n    remainingWeights = remainingWeights.slice(numWeights);\n    return ret;\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights;\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights,\n  };\n}\n", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock4Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: {\n      dense0, dense1, dense2, dense3,\n    },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return (prefix: string): ConvParams => {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return { filters, bias };\n  };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock4Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n  constructor() {\n    super('FaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock4(normalized, params.dense0, true);\n      out = denseBlock4(out, params.dense1);\n      out = denseBlock4(out, params.dense2);\n      out = denseBlock4(out, params.dense3);\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams,\n): tf.Tensor2D {\n  return tf.tidy(() => tf.add(\n    tf.matMul(x, params.weights),\n    params.bias,\n  ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: extractFcParams('fc'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n  const featureExtractorMap: tf.NamedTensorMap = {};\n  const classifierMap: tf.NamedTensorMap = {};\n\n  Object.keys(weightMap).forEach((key) => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n    map[key] = weightMap[key];\n  });\n\n  return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name);\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor;\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  protected abstract getClassifierChannelsIn(): number\n\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeightMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const cIn = this.getClassifierChannelsIn();\n    const cOut = this.getClassifierChannelsOut();\n    const classifierWeightSize = (cOut * cIn) + cOut;\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n  public neutral: number\n\n  public happy: number\n\n  public sad: number\n\n  public angry: number\n\n  public fearful: number\n\n  public disgusted: number\n\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx];\n    });\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map((expression) => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor);\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)));\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n      const data = await t.data();\n      t.dispose();\n      return data;\n    }));\n    out.dispose();\n\n    const predictionsByBatch = probabilitesByBatch\n      .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7;\n  }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n>(\n  sourceObj: TSource,\n  expressions: FaceExpressions,\n): WithFaceExpressions<TSource> {\n  const extension = { expressions };\n  return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint,\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n  faceExpressionsArray.forEach((e) => {\n    // eslint-disable-next-line no-nested-ternary\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined);\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n    }\n\n    const sorted = expr.asSortedArray();\n    const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0));\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n      anchor,\n    );\n    drawTextField.draw(canvasArg);\n  });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n    landmarks: TFaceLandmarks\n    unshiftedLandmarks: TFaceLandmarks\n    alignedRect: FaceDetection\n  }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    // eslint-disable-next-line dot-notation\n    && obj['landmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['alignedRect'] instanceof FaceDetection;\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks<TSource, TFaceLandmarks> {\n  const { box: shift } = sourceObj.detection;\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y);\n\n  const rect = landmarks.align();\n  const { imageDims } = sourceObj.detection;\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect,\n  };\n\n  return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n\n  public drawPoints: boolean\n\n  public lineWidth: number\n\n  public pointSize: number\n\n  public lineColor: string\n\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const {\n      drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n    } = options;\n    this.drawLines = drawLines;\n    this.drawPoints = drawPoints;\n    this.lineWidth = lineWidth || 1;\n    this.pointSize = pointSize || 2;\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {},\n  ) {\n    this.faceLandmarks = faceLandmarks;\n    this.options = new DrawFaceLandmarksOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const {\n      drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n    } = this.options;\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor;\n      ctx.lineWidth = lineWidth;\n      drawContour(ctx, this.faceLandmarks.getJawOutline());\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getNose());\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n      drawContour(ctx, this.faceLandmarks.getMouth(), true);\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor;\n      ctx.fillStyle = pointColor;\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath();\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n        ctx.fill();\n      };\n      this.faceLandmarks.positions.forEach(drawPoint);\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>,\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n  faceLandmarksArray.forEach((f) => {\n    // eslint-disable-next-line no-nested-ternary\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg);\n  });\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x;\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n  out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n  return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n  out = tf.add(out, x);\n  return out;\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception');\n    this._numMainBlocks = numMainBlocks;\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n    if (!params) {\n      throw new Error('TinyXception - load model before inference');\n    }\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n      out = reductionBlock(out, params.entry_flow.reduction_block_1);\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n      });\n      out = reductionBlock(out, params.exit_flow.reduction_block);\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks);\n  }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const age = extractFCParams(512, 1, 'fc/age');\n  const gender = extractFCParams(512, 2, 'fc/gender');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender'),\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n  // eslint-disable-next-line no-unused-vars\n  FEMALE = 'female',\n  // eslint-disable-next-line no-unused-vars\n  MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport {\n  AgeAndGenderPrediction, Gender, NetOutput, NetParams,\n} from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet');\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor;\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n      const gender = fullyConnectedLayer(pooled, params.fc.gender);\n      return { age, gender };\n    });\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input);\n      return { age, gender: tf.softmax(gender) };\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n\n    const ages = tf.unstack(out.age);\n    const genders = tf.unstack(out.gender);\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i],\n    }));\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0];\n        const probMale = (await genderTensor.data())[0];\n        const isMale = probMale > 0.5;\n        const gender = isMale ? Gender.MALE : Gender.FEMALE;\n        const genderProbability = isMale ? probMale : (1 - probMale);\n\n        ageTensor.dispose();\n        genderTensor.dispose();\n        return { age, gender, genderProbability };\n      }),\n    );\n    out.age.dispose();\n    out.gender.dispose();\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model';\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeightMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width);\n      return {\n        width: width * scale,\n        height: height * scale,\n      };\n    });\n\n    const batchSize = inputDimensions.length;\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n      // eslint-disable-next-line no-unused-vars\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx];\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n      };\n\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          getPaddingX(batchIdx),\n          getPaddingY(batchIdx),\n        ))))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          inputDimensions[batchIdx].width,\n          inputDimensions[batchIdx].height,\n        ))));\n\n      return landmarkTensors as tf.Tensor2D;\n    });\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input);\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width })),\n      );\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input);\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data());\n        const xCoords = landmarksArray.filter((_, i) => isEven(i));\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width: netInput.getInputWidth(batchIdx),\n          },\n        );\n      },\n    ));\n\n    landmarkTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136;\n  }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n  weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock3Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock3Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n  constructor() {\n    super('TinyFaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock3(normalized, params.dense0, true);\n      out = denseBlock3(out, params.dense1);\n      out = denseBlock3(out, params.dense2);\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMapTiny(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights);\n  }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128;\n  }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n  const { filters, bias } = params.conv;\n\n  let out = tf.conv2d(x, filters, strides, padding);\n  out = tf.add(out, bias);\n  out = scale(out, params.scale);\n  return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,\n} from '../common/index';\nimport { isFloat } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues);\n    const depth = weights.length / (numFilters * filterSize * filterSize);\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0],\n      ),\n    );\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams {\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n    const bias = tf.tensor1d(extractWeights(numFilters));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n    const weights = tf.tensor1d(extractWeights(numWeights));\n    const biases = tf.tensor1d(extractWeights(numWeights));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` },\n    );\n\n    return {\n      weights,\n      biases,\n    };\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvLayerParams {\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n    return { conv, scale };\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false,\n  ): ResidualLayerParams {\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n    return { conv1, conv2 };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n  );\n  paramMappings.push({ paramPath: 'fc' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n    return { weights, biases };\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n    const scale = extractScaleLayerParams(prefix);\n\n    return { conv: { filters, bias }, scale };\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`),\n    };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const conv32_down = extractConvLayerParams('conv32_down');\n  const conv32_1 = extractResidualLayerParams('conv32_1');\n  const conv32_2 = extractResidualLayerParams('conv32_2');\n  const conv32_3 = extractResidualLayerParams('conv32_3');\n\n  const conv64_down = extractResidualLayerParams('conv64_down');\n  const conv64_1 = extractResidualLayerParams('conv64_1');\n  const conv64_2 = extractResidualLayerParams('conv64_2');\n  const conv64_3 = extractResidualLayerParams('conv64_3');\n\n  const conv128_down = extractResidualLayerParams('conv128_down');\n  const conv128_1 = extractResidualLayerParams('conv128_1');\n  const conv128_2 = extractResidualLayerParams('conv128_2');\n\n  const conv256_down = extractResidualLayerParams('conv256_down');\n  const conv256_1 = extractResidualLayerParams('conv256_1');\n  const conv256_2 = extractResidualLayerParams('conv256_2');\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n  const { fc } = weightMap;\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n  out = tf.add(out, x);\n  out = tf.relu(out);\n  return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape);\n  const isPad = pooled.shape[3] !== out.shape[3];\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number];\n    padShapeX[1] = 1;\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX);\n    out = tf.concat([out, zerosW], 1);\n\n    const padShapeY = [...out.shape] as [number, number, number, number];\n    padShapeY[2] = 1;\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY);\n    out = tf.concat([out, zerosH], 2);\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n  out = tf.add(pooled, out) as tf.Tensor4D;\n\n  out = tf.relu(out);\n  return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('FaceRecognitionNet');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = convDown(normalized, params.conv32_down);\n      out = tf.maxPool(out, 3, 2, 'valid');\n\n      out = residual(out, params.conv32_1);\n      out = residual(out, params.conv32_2);\n      out = residual(out, params.conv32_3);\n\n      out = residualDown(out, params.conv64_down);\n      out = residual(out, params.conv64_1);\n      out = residual(out, params.conv64_2);\n      out = residual(out, params.conv64_3);\n\n      out = residualDown(out, params.conv128_down);\n      out = residual(out, params.conv128_1);\n      out = residual(out, params.conv128_2);\n\n      out = residualDown(out, params.conv256_down);\n      out = residual(out, params.conv256_1);\n      out = residual(out, params.conv256_2);\n      out = residualDown(out, params.conv256_down_out);\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n      const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n      return fullyConnected;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n    const netInput = await toNetInput(input);\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n    faceDescriptorTensors.forEach((t) => t.dispose());\n    return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet();\n  net.extractWeights(weights);\n  return net;\n}\n", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n>(\n  sourceObj: TSource,\n  descriptor: Float32Array,\n): WithFaceDescriptor<TSource> {\n  const extension = { descriptor };\n  return { ...sourceObj, ...extension };\n}\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n  TSource\n>(\n  sourceObj: TSource,\n  age: number,\n): WithAge<TSource> {\n  const extension = { age };\n  return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n    && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n  TSource\n>(\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number,\n): WithGender<TSource> {\n  const extension = { gender, genderProbability };\n  return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,\n} from '../common/index';\nimport {\n  MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` },\n    );\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance,\n    };\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean,\n  ): ConvParams {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): PointwiseConvParams {\n    const {\n      filters,\n      bias,\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n    return {\n      filters,\n      batch_norm_offset: bias,\n    };\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): MobileNetV1.ConvPairParams {\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n    return { depthwise_conv, pointwise_conv };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13,\n    };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0,\n    };\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1,\n    };\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2,\n    };\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3,\n    };\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4,\n    };\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5,\n    };\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5,\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n  const mobilenetv1 = extractMobilenetV1Params();\n  const prediction_layer = extractPredictionLayerParams();\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4],\n  );\n  const output_layer = {\n    extra_dim,\n  };\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer,\n    },\n    paramMappings,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,\n} from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport {\n  BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n    return { filters, batch_norm_offset };\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n    const mappedPrefix = `mobilenetv1/conv_${idx}`;\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance,\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n    };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13),\n    };\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n    return { filters, bias };\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n    );\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`,\n    );\n    return { box_encoding_predictor, class_predictor };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5),\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n  const extra_dim = weightMap['Output/extra_dim'];\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim,\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.conv2d(x, params.filters, strides, 'same');\n    out = tf.add(out, params.batch_norm_offset);\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon,\n    );\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13,\n    ];\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1;\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n      if (layerIdx === 11) {\n        conv11 = out;\n      }\n    });\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null');\n    }\n\n    return {\n      out,\n      conv11: conv11 as any,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync();\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)\n      * Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n): number[] {\n  const numBoxes = boxes.shape[0];\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes,\n  );\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter((c) => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score);\n\n  const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n  const selected: number[] = [];\n\n  candidates.forEach((c) => {\n    if (selected.length >= outputSize) return;\n    const originalScore = c.score;\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j]);\n      if (iou === 0.0) continue;\n      c.score *= suppressFunc(iou);\n      if (c.score <= scoreThreshold) break;\n    }\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex);\n    }\n  });\n  return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1]),\n  ];\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),\n  ];\n  return {\n    sizes,\n    centers,\n  };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers,\n  } = getCenterCoordinatesAndSizesLayer(x0);\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out),\n    ]),\n    [1, 0],\n  );\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = boxPredictions.shape[0];\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n    );\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4],\n    );\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number],\n    );\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = x.shape[0];\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4],\n    );\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3],\n    );\n    return { boxPredictionEncoding, classPrediction };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams,\n) {\n  return tf.tidy(() => {\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding,\n    ], 1) as tf.Tensor4D;\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction,\n    ], 1) as tf.Tensor4D;\n\n    return {\n      boxPredictions,\n      classPredictions,\n    };\n  });\n}\n", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5;\n    this._maxResults = maxResults || 100;\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`);\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence; }\n\n  get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('SsdMobilenetv1');\n  }\n\n  public forwardInput(input: NetInput) {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;\n      const features = mobileNetV1(x, params.mobilenetv1);\n\n      const {\n        boxPredictions,\n        classPredictions,\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer);\n    });\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {},\n  ): Promise<FaceDetection[]> {\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n\n    const netInput = await toNetInput(input);\n\n    const {\n      boxes: _boxes,\n      scores: _scores,\n    } = this.forwardInput(netInput);\n\n    const boxes = _boxes[0];\n    const scores = _scores[0];\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose();\n      _scores[i].dispose();\n    }\n\n    const scoresData = Array.from(await scores.data());\n    const iouThreshold = 0.5;\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence,\n    );\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0);\n    const inputSize = netInput.inputSize as number;\n    const padX = inputSize / reshapedDims.width;\n    const padY = inputSize / reshapedDims.height;\n\n    const boxesData = boxes.arraySync();\n    const results = indices\n      .map((idx) => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2]),\n        ].map((val) => val * padY);\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3]),\n        ].map((val) => val * padX);\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top,\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0),\n          },\n        );\n      });\n\n    boxes.dispose();\n    scores.dispose();\n    return results;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1();\n  net.extractWeights(weights);\n  return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`);\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612));\n    return tf.add(tf.relu(tf.sub(x, min)), min);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n    out = tf.sub(out, params.bn.sub);\n    out = tf.mul(out, params.bn.truediv);\n    out = tf.add(out, params.conv.bias);\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n    out = tf.add(out, params.bias);\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n    const sub = tf.tensor1d(extractWeights(size));\n    const truediv = tf.tensor1d(extractWeights(size));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` },\n    );\n    return { sub, truediv };\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n    return { conv, bn };\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0');\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  }\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry(`${prefix}/sub`, 1);\n    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n    return { sub, truediv };\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { filters, bias };\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`);\n    const bn = extractBatchNormParams(`${prefix}/bn`);\n    return { conv, bn };\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    // eslint-disable-next-line no-mixed-operators\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8'),\n    };\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8'),\n    };\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n  return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416;\n    this._scoreThreshold = scoreThreshold || 0.5;\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n    }\n  }\n\n  get inputSize(): number { return this._inputSize; }\n\n  get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n  public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2');\n    validateConfig(config);\n    this._config = config;\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config;\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1;\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0);\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n    let out = convWithBatchNorm(x, params.conv0);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = convWithBatchNorm(out, params.conv6);\n    out = convWithBatchNorm(out, params.conv7);\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor;\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D;\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n    });\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input), inputSize);\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput, inputSize);\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0),\n    };\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n    out.dispose();\n    out0.dispose();\n\n    const boxes = results.map((res) => res.box);\n    const scores = results.map((res) => res.score);\n    const classScores = results.map((res) => res.classScore);\n    const classNames = results.map((res) => this.config.classes[res.label]);\n\n    const indices = nonMaxSuppression(\n      boxes.map((box) => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true,\n    );\n\n    const detections = indices.map((idx) => new ObjectDetection(\n      scores[idx],\n      classScores[idx],\n      classNames[idx],\n      boxes[idx],\n      inputDimensions,\n    ));\n    return detections;\n  }\n\n  protected getDefaultModelName(): string {\n    return '';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap, this.config);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n    const numFilters = filterSizes ? filterSizes.length : undefined;\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number,\n  ) {\n    const { width, height } = inputBlobDimensions;\n    const inputSize = Math.max(width, height);\n    const correctionFactorX = inputSize / width;\n    const correctionFactorY = inputSize / height;\n\n    const numCells = outputTensor.shape[1];\n    const numBoxes = this.config.anchors.length;\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0);\n      return [boxes, scores, classScores];\n    });\n\n    const results = [] as any;\n    const scoresData = await scoresTensor.array();\n    const boxesData = await boxesTensor.array();\n    for (let row = 0; row < numCells; row++) {\n      for (let col = 0; col < numCells; col++) {\n        for (let anchor = 0; anchor < numBoxes; anchor++) {\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n            const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n            const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n            const x = (ctX - (widthLocal / 2));\n            const y = (ctY - (heightLocal / 2));\n            const pos = { row, col, anchor };\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 };\n            results.push({\n              box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n              score,\n              classScore: score * classScore,\n              label,\n              ...pos,\n            });\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose();\n    scoresTensor.dispose();\n    classScoresTensor.dispose();\n    return results;\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n    const { row, col, anchor } = pos;\n    const classesData = await classesTensor.array();\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label,\n      }))\n      .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n  constructor(withSeparableConvs: boolean = true) {\n    const config = {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      ...(withSeparableConvs\n        ? {\n          anchors: BOX_ANCHORS_SEPARABLE,\n          meanRgb: MEAN_RGB_SEPARABLE,\n        }\n        : {\n          anchors: BOX_ANCHORS,\n          withClassScores: true,\n        }),\n    };\n\n    super(config);\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs;\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeightMap(weightMap);\n  }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs);\n  net.extractWeights(weights);\n  return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask<T> {\n  public async then(\n    // eslint-disable-next-line no-unused-vars\n    onfulfilled: (value: T) => T | PromiseLike<T>,\n  ): Promise<T> {\n    return onfulfilled(await this.run());\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented');\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n  const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n    ? getRectForAlignment(parentResult)\n    : parentResult.detection));\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  );\n\n  const results = await computeResults(faces);\n\n  faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n  return results;\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment?: (parentResultLocal: WithFaceLandmarks<TSource, any>) => FaceDetection,\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async (faces) => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment,\n  );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512],\n    };\n\n    super(config);\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeightMap(weightMap);\n  }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]> => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i]),\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces,\n    );\n\n    return extendWithFaceExpressions(parentResult, faceExpressions);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n    const parentResults = await this.parentTask;\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i];\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n    });\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces,\n    );\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n  ) {\n    super();\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>)),\n      null,\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      // eslint-disable-next-line no-shadow\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return extendWithFaceDescriptor(parentResult, descriptor);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected useTinyLandmarkNet: boolean,\n  ) {\n    super();\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net;\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const detections = parentResults.map((res) => res.detection);\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections);\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      (face) => this.landmarkNet.detectLandmarks(face),\n    )) as FaceLandmarks68[];\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return parentResults.map((parentResult, i) => extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i]));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { detection } = parentResult;\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection]);\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n  ) {\n    super();\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n  public async run(): Promise<FaceDetection[]> {\n    const { input, options } = this;\n\n    // eslint-disable-next-line no-nested-ternary\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      // eslint-disable-next-line no-shadow\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        // eslint-disable-next-line no-nested-ternary\n        options instanceof SsdMobilenetv1Options\n          // eslint-disable-next-line no-shadow\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              // eslint-disable-next-line no-shadow\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      );\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');\n    }\n\n    return faceDetectionFunction(input);\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}>[]>(async (resolve) => {\n      const detections = await this.run();\n      resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach((faceDetection) => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}> | undefined>(async (resolve) => {\n      const detection = await this.run();\n      resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number,\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {},\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n  const desc1 = Array.from(arr1);\n  const desc2 = Array.from(arr2);\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + (diff ** 2), 0),\n  );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6,\n  ) {\n    this._distanceThreshold = distanceThreshold;\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n    if (!inputArray.length) {\n      throw new Error('FaceRecognizer.constructor - expected atleast one input');\n    }\n\n    let count = 1;\n    const createUniqueLabel = () => `person ${count++}`;\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc;\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n      }\n\n      throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>');\n    });\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n  public get distanceThreshold(): number { return this._distanceThreshold; }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map((d) => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1);\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n        label,\n        this.computeMeanDistance(queryDescriptor, descriptors),\n      ))\n      .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor);\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance);\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector();\n  net.extractWeights(weights);\n  return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height);\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height));\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height);\n  }\n\n  return results;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n"],
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"../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Exp.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/ExpandDims.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Fill.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/FlipLeftRight.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Floor.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/FloorDiv.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/FusedBatchNorm.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/FusedConv2D.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/FusedDepthwiseConv2D.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/GatherNd.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/GatherV2.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Greater.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/GreaterEqual.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/LeakyRelu.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Less.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/LessEqual.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Log.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/LogicalAnd.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Max.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Maximum.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/MaxPool.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Mean.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Min.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Minimum.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Multiply.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Neg.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/NonMaxSuppression_util.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/NonMaxSuppressionV3.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/NonMaxSuppressionV4.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/NonMaxSuppressionV5.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/NotEqual.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/OneHot.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/OnesLike.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Pack.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/PadV2.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Pow.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Prelu.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Prod.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Range.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/RealDiv.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Relu.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Relu6.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/ResizeBilinear.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Reverse.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/RotateWithOffset.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Round.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Rsqrt.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/ScatterNd.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Select.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Sigmoid.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Sin.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Slice.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Softmax.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/SplitV.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Sqrt.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Square.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/SquaredDifference.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Step.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/StridedSlice.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Sub.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Sum.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Tanh.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Tile.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/TopK.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Unpack.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/ZerosLike.ts", 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"../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/NeuralNetwork.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeightMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeightMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeightMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeightMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeightMap.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeightMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts", "../src/index.ts"],
+  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version;\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 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\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}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}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(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 shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){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\"){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;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":171,\"maximum\":171+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;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)}var WASM_PAGE_SIZE=65536;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 STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_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_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"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_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}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){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;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 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 Math_ceil=Math.ceil;var Math_floor=Math.floor;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+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}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(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}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)&&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()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;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\"){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\");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();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13568;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(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>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 __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";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 __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";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={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12816;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13056;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___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+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(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+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>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){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},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\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(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.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}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){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},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){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;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);__ATEXIT__.unshift({func:func,arg: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_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){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{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}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];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}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){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;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(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,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)}},inEventHandler:0,currentEventHandler:null,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_async_queue_on_thread_(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_async_queue_on_thread_(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){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}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}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},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},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],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\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){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 contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};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.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() 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,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_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_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;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;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>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+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}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 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79: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: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();else PThread.initWorker();var GLctx;GL.init();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,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var 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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)}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_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;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){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;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 exitRuntime(){runtimeExited=true}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 Math_ceil=Math.ceil;var Math_floor=Math.floor;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+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}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(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}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)&&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()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);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\"){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\");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();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};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}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"Ceil\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Floor\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"LeakyRelu\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"MaxPool\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Maximum\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"Mean\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"Min\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Minimum\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Multiply\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"Neg\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"NotEqual\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"OneHot\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"PadV2\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"Pow\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Prelu\"]).apply(null,arguments)};var _Prod=Module[\"_Prod\"]=function(){return(_Prod=Module[\"_Prod\"]=Module[\"asm\"][\"Prod\"]).apply(null,arguments)};var _RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"RealDiv\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Relu\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Relu6\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"ResizeBilinear\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Reverse\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"RotateWithOffset\"]).apply(null,arguments)};var _Round=Module[\"_Round\"]=function(){return(_Round=Module[\"_Round\"]=Module[\"asm\"][\"Round\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Rsqrt\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"ScatterNd\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"SelectV2\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"Sigmoid\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Sin\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Softmax\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Sqrt\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Square\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"SquaredDifference\"]).apply(null,arguments)};var _Step=Module[\"_Step\"]=function(){return(_Step=Module[\"_Step\"]=Module[\"asm\"][\"Step\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"StridedSlice\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sub\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Sum\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Tanh\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Tile\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"TopK\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Transpose\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"_FusedMatMul\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"malloc\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"free\"]).apply(null,arguments)};var __start=Module[\"__start\"]=function(){return(__start=Module[\"__start\"]=Module[\"asm\"][\"_start\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"stackSave\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"stackAlloc\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"stackRestore\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}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();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);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(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}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()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n  return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModule;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModule; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModule\"] = WasmBackendModule;\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, 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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, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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 threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;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:selfThreadId})}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};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;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)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}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;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;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[\"dynCall_ii\"](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\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){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.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\");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, "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false,\n) {\n  ctx.beginPath();\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx];\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(x, y);\n  });\n\n  if (isClosed) {\n    const from = points[points.length - 1];\n    const to = points[0];\n    if (!from || !to) {\n      return;\n    }\n\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(to.x, to.y);\n  }\n\n  ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = 10 ** prec;\n  return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width);\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n  private _width: number\n\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n    }\n\n    this._width = width;\n    this._height = height;\n  }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height);\n  }\n}\n", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x;\n    this._y = y;\n  }\n\n  get x(): number { return this._x; }\n\n  get y(): number { return this._y; }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y);\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y);\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y);\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y);\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y));\n  }\n\n  public magnitude(): number {\n    return Math.sqrt((this.x ** 2) + (this.y ** 2));\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y));\n  }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n    }\n  }\n\n  private _x: number\n\n  private _y: number\n\n  private _width: number\n\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any;\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n    Box.assertIsValidBox({\n      x, y, width, height,\n    }, 'Box.constructor', allowNegativeDimensions);\n\n    this._x = x;\n    this._y = y;\n    this._width = width;\n    this._height = height;\n  }\n\n  public get x(): number { return this._x; }\n\n  public get y(): number { return this._y; }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public get left(): number { return this.x; }\n\n  public get top(): number { return this.y; }\n\n  public get right(): number { return this.x + this.width; }\n\n  public get bottom(): number { return this.y + this.height; }\n\n  public get area(): number { return this.width * this.height; }\n\n  public get topLeft(): Point { return new Point(this.left, this.top); }\n\n  public get topRight(): Point { return new Point(this.right, this.top); }\n\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n  public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.round(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.floor(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public toSquare(): Box<BoxType> {\n    let {\n      x, y, width, height,\n    } = this;\n    const diff = Math.abs(width - height);\n    if (width < height) {\n      x -= (diff / 2);\n      width += diff;\n    }\n    if (height < width) {\n      y -= (diff / 2);\n      height += diff;\n    }\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY,\n    });\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    const [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY,\n    ];\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const {\n      x, y, right, bottom,\n    } = this;\n    const clippedX = Math.max(x, 0);\n    const clippedY = Math.max(y, 0);\n\n    const newWidth = right - clippedX;\n    const newHeight = bottom - clippedY;\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n    return (new Box({\n      x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n    })).floor();\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this;\n    const x = this.x + sx;\n    const y = this.y + sy;\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1;\n    const h = this.height + 1;\n\n    const dx = 1;\n    const dy = 1;\n    let edx = w;\n    let edy = h;\n\n    let x = this.left;\n    let y = this.top;\n    let ex = this.right;\n    let ey = this.bottom;\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w;\n      ex = imageWidth;\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h;\n      ey = imageHeight;\n    }\n    if (x < 1) {\n      edy = 2 - x;\n      x = 1;\n    }\n    if (y < 1) {\n      edy = 2 - y;\n      y = 1;\n    }\n\n    return {\n      dy, edy, dx, edx, y, ey, x, ex, w, h,\n    };\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height),\n    }).toSquare().round();\n  }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({\n      left, top, right, bottom,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n\n  private _classScore: number\n\n  private _className: string\n\n  private _box: Rect\n\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions,\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n    this._score = score;\n    this._classScore = classScore;\n    this._className = className;\n    this._box = new Box(relativeBox).rescale(this._imageDims);\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n\n  public get className(): string { return this._className; }\n\n  public get box(): Box { return this._box; }\n\n  public get imageDims(): Dimensions { return this._imageDims; }\n\n  public get imageWidth(): number { return this.imageDims.width; }\n\n  public get imageHeight(): number { return this.imageDims.height; }\n\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height },\n    );\n  }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions,\n  ) {\n    super(score, score, '', relativeBox, imageDims);\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height);\n    return new FaceDetection(score, relativeBox, imageDims);\n  }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n  const interSection = width * height;\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map((pt) => pt.x);\n  const ys = pts.map((pt) => pt.y);\n  const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n  const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n  const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n  const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n  return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true,\n): number[] {\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map((c) => c.boxIndex);\n\n  const pick: number[] = [];\n\n  while (indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number;\n    pick.push(curr);\n\n    const indices = indicesSortedByScore;\n\n    const outputs: number[] = [];\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i];\n\n      const currBox = boxes[curr];\n      const idxBox = boxes[idx];\n\n      outputs.push(iou(currBox, idxBox, isIOU));\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold,\n    );\n  }\n\n  return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb;\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n    return tf.sub(x, avg_rgb);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [height, width] = imgTensor.shape.slice(1);\n    if (height === width) {\n      return imgTensor;\n    }\n\n    const dimDiff = Math.abs(height - width);\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n    const paddingAxis = height > width ? 2 : 1;\n\n    const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice();\n      paddingTensorShape[paddingAxis] = paddingAmountLocal;\n      return tf.fill(paddingTensorShape, 0, 'float32');\n    };\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount);\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null;\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend,\n    ]\n      .filter((t) => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n    return tf.concat(tensorsToStack, paddingAxis);\n  });\n}\n", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice();\n  for (let i = array.length - 1; i > 0; i--) {\n    const j = Math.floor(Math.random() * (i + 1));\n    const x = array[i];\n    array[i] = array[j];\n    array[j] = x;\n  }\n  return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({\n      x, y, width, height,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n\n  protected _positions: Point[]\n\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0),\n  ) {\n    const { width, height } = imgDims;\n    this._imgDims = new Dimensions(width, height);\n    this._shift = shift;\n    this._positions = relativeFaceLandmarkPositions.map(\n      (pt) => pt.mul(new Point(width, height)).add(shift),\n    );\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n  public get imageWidth(): number { return this._imgDims.width; }\n\n  public get imageHeight(): number { return this._imgDims.height; }\n\n  public get positions(): Point[] { return this._positions; }\n\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n    );\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height },\n    );\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y),\n    );\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y);\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection);\n\n      return this.shiftBy(box.x, box.y).align(null, options);\n    }\n\n    const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n    if (useDlibAlignment) {\n      return this.alignDlib();\n    }\n\n    return this.alignMinBbox(minBoxPadding);\n  }\n\n  private alignDlib(): Box {\n    const centers = this.getRefPointsForAlignment();\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n    const size = Math.floor(eyeToMouthDist / relScale);\n\n    const refPoint = getCenterPoint(centers);\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions);\n    return box.pad(box.width * padding, box.height * padding);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class');\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions;\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]]),\n    ];\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17);\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22);\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27);\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36);\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42);\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48);\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth(),\n    ].map(getCenterPoint);\n  }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label;\n    this._distance = distance;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get distance(): number { return this._distance; }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n  }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee);\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box);\n    this._label = label;\n  }\n\n  public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n  private _label: string\n\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n    }\n\n    this._label = label;\n    this._descriptors = descriptors;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get descriptors(): Float32Array[] { return this._descriptors; }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d)),\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee);\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n    }\n  }\n\n  private _score: number\n\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label);\n    this._score = score;\n    this._classScore = classScore;\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<\n  TSource\n>(\n  sourceObj: TSource,\n  detection: FaceDetection,\n): WithFaceDetection<TSource> {\n  const extension = { detection };\n  return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n  const fetch = window.fetch;\n  if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n  const readFile = () => {\n    throw new Error('readFile - filesystem not available for browser environment');\n  };\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile,\n  };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n  let requireFsError = '';\n\n  if (!fs) {\n    try {\n      // eslint-disable-next-line global-require\n      fs = require('fs');\n    } catch (err) {\n      requireFsError = err.toString();\n    }\n  }\n\n  const readFile = fs\n    ? (filePath: string) => new Promise<Buffer>((resolve, reject) => {\n      fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n    })\n    : () => {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n    };\n\n  return {\n    readFile,\n  };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n  // eslint-disable-next-line dot-notation\n  const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n  const Image = global.Image || global.HTMLImageElement;\n\n  const createCanvasElement = () => {\n    if (Canvas) return new Canvas();\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n  };\n\n  const createImageElement = () => {\n    if (Image) return new Image();\n    throw new Error('createImageElement - missing Image implementation for nodejs environment');\n  };\n\n  const fetch = global.fetch;\n  // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n  const fileSystem = createFileSystem();\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n    Image: Image || class {},\n    ImageData: global.ImageData || class {},\n    Video: global.HTMLVideoElement || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem,\n  };\n}\n", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n  }\n  return environment;\n}\n\nfunction setEnv(env: Environment) {\n  environment = env;\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) return setEnv(createBrowserEnv());\n  if (isNodejs()) return setEnv(createNodejsEnv());\n  return null;\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize();\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env;\n  environment.Canvas = Canvas;\n  environment.Image = Image;\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n  environment.createImageElement = env.createImageElement || (() => new Image());\n\n  environment.ImageData = env.ImageData || environment.ImageData;\n  environment.Video = env.Video || environment.Video;\n  environment.fetch = env.fetch || environment.fetch;\n  environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg);\n  }\n  return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg;\n  }\n\n  const canvas = resolveInput(canvasArg);\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n  }\n\n  const ctx = canvas.getContext('2d');\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null');\n  }\n\n  return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n  // eslint-disable-next-line no-unused-vars\n  TOP_LEFT = 'TOP_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  TOP_RIGHT = 'TOP_RIGHT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n\n  public backgroundColor: string\n\n  public fontColor: string\n\n  public fontSize: number\n\n  public fontStyle: string\n\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const {\n      anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = options;\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n    this.fontSize = fontSize || 14;\n    this.fontStyle = fontStyle || 'Georgia';\n    this.padding = padding || 4;\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n\n  public anchor : IPoint\n\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {},\n  ) {\n    // eslint-disable-next-line no-nested-ternary\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text);\n    this.anchor = anchor;\n    this.options = new DrawTextFieldOptions(options);\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options;\n    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options;\n    return this.text.length * fontSize + (2 * padding);\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options;\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n    const textFieldWidth = this.measureWidth(ctx);\n    const textFieldHeight = this.measureHeight();\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims;\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n      return { x: newX, y: newY };\n    }\n    return { x, y };\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg);\n    const ctx = getContext2dOrThrow(canvas);\n\n    const {\n      backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = this.options;\n\n    ctx.font = `${fontSize}px ${fontStyle}`;\n    const maxTextWidth = this.measureWidth(ctx);\n    const textHeight = this.measureHeight();\n\n    ctx.fillStyle = backgroundColor;\n    const upperLeft = this.getUpperLeft(ctx, canvas);\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x;\n      const y = padding + upperLeft.y + ((i + 1) * fontSize);\n      ctx.fillText(textLine, x, y);\n    });\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport {\n  AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,\n} from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n\n  public lineWidth: number\n\n  public drawLabelOptions: DrawTextFieldOptions\n\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const {\n      boxColor, lineWidth, label, drawLabelOptions,\n    } = options;\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n    this.lineWidth = lineWidth || 2;\n    this.label = label;\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor,\n    };\n    this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {},\n  ) {\n    this.box = new Box(box);\n    this.options = new DrawBoxOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const { boxColor, lineWidth } = this.options;\n\n    const {\n      x, y, width, height,\n    } = this.box;\n    ctx.strokeStyle = boxColor;\n    ctx.lineWidth = lineWidth;\n    ctx.strokeRect(x, y, width, height);\n\n    const { label } = this.options;\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n    }\n  }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>,\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n  detectionsArray.forEach((det) => {\n    // eslint-disable-next-line no-nested-ternary\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n    // eslint-disable-next-line no-nested-ternary\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n    const label = score ? `${round(score)}` : undefined;\n    new DrawBox(box, { label }).draw(canvasArg);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n  const { Image, Video } = env.getEnv();\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n  // eslint-disable-next-line consistent-return\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null);\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return;\n      // eslint-disable-next-line no-use-before-define\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      reject(e);\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return;\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      resolve(e);\n    }\n\n    media.addEventListener('load', onLoad);\n    media.addEventListener('error', onError);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n    const reader = new FileReader();\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n      const img = env.getEnv().createImageElement();\n      img.onload = () => resolve(img);\n      img.onerror = reject;\n      img.src = reader.result as string;\n    };\n    reader.onerror = reject;\n    reader.readAsDataURL(buf);\n  });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n  const { Image, Video } = env.getEnv();\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight);\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight);\n  }\n  return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n  const { createCanvasElement } = env.getEnv();\n  const canvas = createCanvasElement();\n  canvas.width = width;\n  canvas.height = height;\n  return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n  const { ImageData } = env.getEnv();\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet');\n  }\n\n  const { width, height } = dims || getMediaDimensions(media);\n  const canvas = createCanvas({ width, height });\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n  }\n  return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement,\n): Promise<HTMLCanvasElement> {\n  const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n  await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n  imgTensor3D.dispose();\n\n  return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n  const { Image, Canvas, Video } = env.getEnv();\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n  const { Image, Canvas } = env.getEnv();\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n  }\n\n  const dims = getMediaDimensions(input);\n  const scale = inputSize / Math.max(dims.height, dims.width);\n  const width = scale * dims.width;\n  const height = scale * dims.height;\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n  const offset = Math.abs(width - height) / 2;\n  const dx = centerImage && width < height ? offset : 0;\n  const dy = centerImage && height < width ? offset : 0;\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n  return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport {\n  computeReshapedDimensions, isTensor3D, isTensor4D, range,\n} from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n\n  private _canvases: HTMLCanvasElement[] = []\n\n  private _batchSize: number\n\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false,\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput;\n    this._batchSize = inputs.length;\n\n    inputs.forEach((input, idx) => {\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = input.shape;\n        return;\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0];\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n        }\n\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = (input as any).shape.slice(1);\n        return;\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n      this._canvases[idx] = canvas;\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n    });\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors;\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases;\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput;\n  }\n\n  public get batchSize(): number {\n    return this._batchSize;\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions;\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize;\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n    );\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx];\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0];\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1];\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n    }\n\n    const width = this.getInputWidth(batchIdx);\n    const height = this.getInputHeight(batchIdx);\n    return computeReshapedDimensions({ width, height }, this.inputSize);\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n    this._inputSize = inputSize;\n\n    return tf.tidy(() => {\n      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n        const input = this.getInput(batchIdx);\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>();\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3);\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n      });\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n\n      return batchTensor;\n    });\n  }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs;\n  }\n\n  const inputArgArray = Array.isArray(inputs)\n    ? inputs\n    : [inputs];\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input');\n  }\n\n  const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n\n  const inputArray = inputArgArray.map(resolveInput);\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0];\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n      }\n    }\n  });\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)),\n  );\n\n  return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>,\n): Promise<HTMLCanvasElement[]> {\n  const { Canvas } = env.getEnv();\n\n  let canvas = input as HTMLCanvasElement;\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input);\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported');\n    }\n\n    const tensorOrCanvas = netInput.getInput(0);\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas);\n  }\n\n  const ctx = getContext2dOrThrow(canvas);\n  const boxes = detections.map(\n    (det) => (det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det),\n  )\n    .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n\n  return boxes.map(({\n    x, y, width, height,\n  }) => {\n    const faceImg = createCanvas({ width, height });\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n    return faceImg;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>,\n): Promise<tf.Tensor3D[]> {\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported');\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n    const boxes = detections.map(\n      (det) => (det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det),\n    )\n      .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n    const faceTensors = boxes.map(({\n      x, y, width, height,\n    }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n    return faceTensors;\n  });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n  url: string,\n  // eslint-disable-next-line no-undef\n  init?: RequestInit,\n): Promise<Response> {\n  const { fetch } = env.getEnv();\n  const res = await fetch(url, init);\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n  }\n  return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri);\n  const blob = await (res).blob();\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n  }\n  return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename,\n    };\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`,\n    };\n  }\n  // eslint-disable-next-line no-nested-ternary\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter((s) => s);\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename;\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri);\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference;\n  input.width = width;\n  input.height = height;\n  return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork<TNetParams> {\n  constructor(name: string) {\n    this._name = name;\n  }\n\n  protected _params: TNetParams | undefined = undefined\n\n  protected _paramMappings: ParamMapping[] = []\n\n  public _name: any;\n\n  public get params(): TNetParams | undefined { return this._params; }\n\n  public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n  public get isLoaded(): boolean { return !!this.params; }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    return obj[objProp];\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    obj[objProp].dispose();\n    obj[objProp] = tensor;\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath),\n    }));\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable());\n    });\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync());\n      variable.dispose();\n      this.reassignParamFromPath(path, tensor);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach((param) => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`);\n      }\n      param.tensor.dispose();\n    });\n    this._params = undefined;\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr)),\n    );\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl);\n      return;\n    }\n    await this.loadFromUri(weightsOrUrl);\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`);\n    }\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n    }\n    const { readFile } = env.getEnv();\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)));\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n    const manifest = JSON.parse((await readFile(manifestUri)).toString());\n    const weightMap = await loadWeights(manifest, modelBaseUri);\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const { paramMappings, params } = this.extractParams(weights);\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error('traversePropertyPath - model has no loaded params');\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      // eslint-disable-next-line no-prototype-builtins\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n      }\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n    }, { nextObj: this.params });\n\n    const { obj, objProp } = result;\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n    }\n\n    return { obj, objProp };\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number],\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n    out = tf.add(out, params.bias);\n    return out;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n  });\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias,\n    ) as tf.Tensor4D;\n\n    return withRelu ? tf.relu(out) : out;\n  });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach((path) => {\n    if (!paramMappings.some((pm) => pm.originalPath === path)) {\n      weightMap[path].dispose();\n    }\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams => {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): FCParams => {\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias,\n    };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  // eslint-disable-next-line no-useless-constructor\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    public depthwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public pointwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public bias: tf.Tensor1D,\n  // eslint-disable-next-line no-empty-function\n  ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n\nexport function loadSeparableConvParamsFactory(\n  // eslint-disable-next-line no-unused-vars\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T,\n) {\n  return (prefix: string): SeparableConvParams => {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4);\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  return (originalPath: string, paramRank: number, mappedPath?: string) => {\n    const tensor = weightMap[originalPath];\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath },\n    );\n\n    return tensor;\n  };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights;\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights);\n    remainingWeights = remainingWeights.slice(numWeights);\n    return ret;\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights;\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights,\n  };\n}\n", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock4Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: {\n      dense0, dense1, dense2, dense3,\n    },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return (prefix: string): ConvParams => {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return { filters, bias };\n  };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock4Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n  constructor() {\n    super('FaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock4(normalized, params.dense0, true);\n      out = denseBlock4(out, params.dense1);\n      out = denseBlock4(out, params.dense2);\n      out = denseBlock4(out, params.dense3);\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams,\n): tf.Tensor2D {\n  return tf.tidy(() => tf.add(\n    tf.matMul(x, params.weights),\n    params.bias,\n  ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: extractFcParams('fc'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n  const featureExtractorMap: tf.NamedTensorMap = {};\n  const classifierMap: tf.NamedTensorMap = {};\n\n  Object.keys(weightMap).forEach((key) => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n    map[key] = weightMap[key];\n  });\n\n  return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name);\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor;\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  protected abstract getClassifierChannelsIn(): number\n\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeightMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const cIn = this.getClassifierChannelsIn();\n    const cOut = this.getClassifierChannelsOut();\n    const classifierWeightSize = (cOut * cIn) + cOut;\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n  public neutral: number\n\n  public happy: number\n\n  public sad: number\n\n  public angry: number\n\n  public fearful: number\n\n  public disgusted: number\n\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx];\n    });\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map((expression) => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor);\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)));\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n      const data = await t.data();\n      t.dispose();\n      return data;\n    }));\n    out.dispose();\n\n    const predictionsByBatch = probabilitesByBatch\n      .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7;\n  }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n>(\n  sourceObj: TSource,\n  expressions: FaceExpressions,\n): WithFaceExpressions<TSource> {\n  const extension = { expressions };\n  return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint,\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n  faceExpressionsArray.forEach((e) => {\n    // eslint-disable-next-line no-nested-ternary\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined);\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n    }\n\n    const sorted = expr.asSortedArray();\n    const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0));\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n      anchor,\n    );\n    drawTextField.draw(canvasArg);\n  });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n    landmarks: TFaceLandmarks\n    unshiftedLandmarks: TFaceLandmarks\n    alignedRect: FaceDetection\n  }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    // eslint-disable-next-line dot-notation\n    && obj['landmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['alignedRect'] instanceof FaceDetection;\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks<TSource, TFaceLandmarks> {\n  const { box: shift } = sourceObj.detection;\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y);\n\n  const rect = landmarks.align();\n  const { imageDims } = sourceObj.detection;\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect,\n  };\n\n  return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n\n  public drawPoints: boolean\n\n  public lineWidth: number\n\n  public pointSize: number\n\n  public lineColor: string\n\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const {\n      drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n    } = options;\n    this.drawLines = drawLines;\n    this.drawPoints = drawPoints;\n    this.lineWidth = lineWidth || 1;\n    this.pointSize = pointSize || 2;\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {},\n  ) {\n    this.faceLandmarks = faceLandmarks;\n    this.options = new DrawFaceLandmarksOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const {\n      drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n    } = this.options;\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor;\n      ctx.lineWidth = lineWidth;\n      drawContour(ctx, this.faceLandmarks.getJawOutline());\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getNose());\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n      drawContour(ctx, this.faceLandmarks.getMouth(), true);\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor;\n      ctx.fillStyle = pointColor;\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath();\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n        ctx.fill();\n      };\n      this.faceLandmarks.positions.forEach(drawPoint);\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>,\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n  faceLandmarksArray.forEach((f) => {\n    // eslint-disable-next-line no-nested-ternary\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg);\n  });\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x;\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n  out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n  return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n  out = tf.add(out, x);\n  return out;\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception');\n    this._numMainBlocks = numMainBlocks;\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n    if (!params) {\n      throw new Error('TinyXception - load model before inference');\n    }\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n      out = reductionBlock(out, params.entry_flow.reduction_block_1);\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n      });\n      out = reductionBlock(out, params.exit_flow.reduction_block);\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks);\n  }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const age = extractFCParams(512, 1, 'fc/age');\n  const gender = extractFCParams(512, 2, 'fc/gender');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender'),\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n  // eslint-disable-next-line no-unused-vars\n  FEMALE = 'female',\n  // eslint-disable-next-line no-unused-vars\n  MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport {\n  AgeAndGenderPrediction, Gender, NetOutput, NetParams,\n} from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet');\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor;\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n      const gender = fullyConnectedLayer(pooled, params.fc.gender);\n      return { age, gender };\n    });\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input);\n      return { age, gender: tf.softmax(gender) };\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n\n    const ages = tf.unstack(out.age);\n    const genders = tf.unstack(out.gender);\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i],\n    }));\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0];\n        const probMale = (await genderTensor.data())[0];\n        const isMale = probMale > 0.5;\n        const gender = isMale ? Gender.MALE : Gender.FEMALE;\n        const genderProbability = isMale ? probMale : (1 - probMale);\n\n        ageTensor.dispose();\n        genderTensor.dispose();\n        return { age, gender, genderProbability };\n      }),\n    );\n    out.age.dispose();\n    out.gender.dispose();\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model';\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeightMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width);\n      return {\n        width: width * scale,\n        height: height * scale,\n      };\n    });\n\n    const batchSize = inputDimensions.length;\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n      // eslint-disable-next-line no-unused-vars\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx];\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n      };\n\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          getPaddingX(batchIdx),\n          getPaddingY(batchIdx),\n        ))))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          inputDimensions[batchIdx].width,\n          inputDimensions[batchIdx].height,\n        ))));\n\n      return landmarkTensors as tf.Tensor2D;\n    });\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input);\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width })),\n      );\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input);\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data());\n        const xCoords = landmarksArray.filter((_, i) => isEven(i));\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width: netInput.getInputWidth(batchIdx),\n          },\n        );\n      },\n    ));\n\n    landmarkTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136;\n  }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n  weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock3Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock3Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n  constructor() {\n    super('TinyFaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock3(normalized, params.dense0, true);\n      out = denseBlock3(out, params.dense1);\n      out = denseBlock3(out, params.dense2);\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMapTiny(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights);\n  }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128;\n  }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n  const { filters, bias } = params.conv;\n\n  let out = tf.conv2d(x, filters, strides, padding);\n  out = tf.add(out, bias);\n  out = scale(out, params.scale);\n  return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,\n} from '../common/index';\nimport { isFloat } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues);\n    const depth = weights.length / (numFilters * filterSize * filterSize);\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0],\n      ),\n    );\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams {\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n    const bias = tf.tensor1d(extractWeights(numFilters));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n    const weights = tf.tensor1d(extractWeights(numWeights));\n    const biases = tf.tensor1d(extractWeights(numWeights));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` },\n    );\n\n    return {\n      weights,\n      biases,\n    };\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvLayerParams {\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n    return { conv, scale };\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false,\n  ): ResidualLayerParams {\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n    return { conv1, conv2 };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n  );\n  paramMappings.push({ paramPath: 'fc' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n    return { weights, biases };\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n    const scale = extractScaleLayerParams(prefix);\n\n    return { conv: { filters, bias }, scale };\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`),\n    };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const conv32_down = extractConvLayerParams('conv32_down');\n  const conv32_1 = extractResidualLayerParams('conv32_1');\n  const conv32_2 = extractResidualLayerParams('conv32_2');\n  const conv32_3 = extractResidualLayerParams('conv32_3');\n\n  const conv64_down = extractResidualLayerParams('conv64_down');\n  const conv64_1 = extractResidualLayerParams('conv64_1');\n  const conv64_2 = extractResidualLayerParams('conv64_2');\n  const conv64_3 = extractResidualLayerParams('conv64_3');\n\n  const conv128_down = extractResidualLayerParams('conv128_down');\n  const conv128_1 = extractResidualLayerParams('conv128_1');\n  const conv128_2 = extractResidualLayerParams('conv128_2');\n\n  const conv256_down = extractResidualLayerParams('conv256_down');\n  const conv256_1 = extractResidualLayerParams('conv256_1');\n  const conv256_2 = extractResidualLayerParams('conv256_2');\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n  const { fc } = weightMap;\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n  out = tf.add(out, x);\n  out = tf.relu(out);\n  return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape);\n  const isPad = pooled.shape[3] !== out.shape[3];\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number];\n    padShapeX[1] = 1;\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX);\n    out = tf.concat([out, zerosW], 1);\n\n    const padShapeY = [...out.shape] as [number, number, number, number];\n    padShapeY[2] = 1;\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY);\n    out = tf.concat([out, zerosH], 2);\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n  out = tf.add(pooled, out) as tf.Tensor4D;\n\n  out = tf.relu(out);\n  return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('FaceRecognitionNet');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = convDown(normalized, params.conv32_down);\n      out = tf.maxPool(out, 3, 2, 'valid');\n\n      out = residual(out, params.conv32_1);\n      out = residual(out, params.conv32_2);\n      out = residual(out, params.conv32_3);\n\n      out = residualDown(out, params.conv64_down);\n      out = residual(out, params.conv64_1);\n      out = residual(out, params.conv64_2);\n      out = residual(out, params.conv64_3);\n\n      out = residualDown(out, params.conv128_down);\n      out = residual(out, params.conv128_1);\n      out = residual(out, params.conv128_2);\n\n      out = residualDown(out, params.conv256_down);\n      out = residual(out, params.conv256_1);\n      out = residual(out, params.conv256_2);\n      out = residualDown(out, params.conv256_down_out);\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n      const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n      return fullyConnected;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n    const netInput = await toNetInput(input);\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n    faceDescriptorTensors.forEach((t) => t.dispose());\n    return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet();\n  net.extractWeights(weights);\n  return net;\n}\n", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n>(\n  sourceObj: TSource,\n  descriptor: Float32Array,\n): WithFaceDescriptor<TSource> {\n  const extension = { descriptor };\n  return { ...sourceObj, ...extension };\n}\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n  TSource\n>(\n  sourceObj: TSource,\n  age: number,\n): WithAge<TSource> {\n  const extension = { age };\n  return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n    && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n  TSource\n>(\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number,\n): WithGender<TSource> {\n  const extension = { gender, genderProbability };\n  return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,\n} from '../common/index';\nimport {\n  MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` },\n    );\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance,\n    };\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean,\n  ): ConvParams {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): PointwiseConvParams {\n    const {\n      filters,\n      bias,\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n    return {\n      filters,\n      batch_norm_offset: bias,\n    };\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): MobileNetV1.ConvPairParams {\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n    return { depthwise_conv, pointwise_conv };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13,\n    };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0,\n    };\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1,\n    };\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2,\n    };\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3,\n    };\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4,\n    };\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5,\n    };\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5,\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n  const mobilenetv1 = extractMobilenetV1Params();\n  const prediction_layer = extractPredictionLayerParams();\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4],\n  );\n  const output_layer = {\n    extra_dim,\n  };\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer,\n    },\n    paramMappings,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,\n} from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport {\n  BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n    return { filters, batch_norm_offset };\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n    const mappedPrefix = `mobilenetv1/conv_${idx}`;\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance,\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n    };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13),\n    };\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n    return { filters, bias };\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n    );\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`,\n    );\n    return { box_encoding_predictor, class_predictor };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5),\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n  const extra_dim = weightMap['Output/extra_dim'];\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim,\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.conv2d(x, params.filters, strides, 'same');\n    out = tf.add(out, params.batch_norm_offset);\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon,\n    );\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13,\n    ];\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1;\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n      if (layerIdx === 11) {\n        conv11 = out;\n      }\n    });\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null');\n    }\n\n    return {\n      out,\n      conv11: conv11 as any,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync();\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)\n      * Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n): number[] {\n  const numBoxes = boxes.shape[0];\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes,\n  );\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter((c) => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score);\n\n  const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n  const selected: number[] = [];\n\n  candidates.forEach((c) => {\n    if (selected.length >= outputSize) return;\n    const originalScore = c.score;\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j]);\n      if (iou === 0.0) continue;\n      c.score *= suppressFunc(iou);\n      if (c.score <= scoreThreshold) break;\n    }\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex);\n    }\n  });\n  return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1]),\n  ];\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),\n  ];\n  return {\n    sizes,\n    centers,\n  };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers,\n  } = getCenterCoordinatesAndSizesLayer(x0);\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out),\n    ]),\n    [1, 0],\n  );\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = boxPredictions.shape[0];\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n    );\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4],\n    );\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number],\n    );\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = x.shape[0];\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4],\n    );\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3],\n    );\n    return { boxPredictionEncoding, classPrediction };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams,\n) {\n  return tf.tidy(() => {\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding,\n    ], 1) as tf.Tensor4D;\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction,\n    ], 1) as tf.Tensor4D;\n\n    return {\n      boxPredictions,\n      classPredictions,\n    };\n  });\n}\n", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5;\n    this._maxResults = maxResults || 100;\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`);\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence; }\n\n  get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('SsdMobilenetv1');\n  }\n\n  public forwardInput(input: NetInput) {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;\n      const features = mobileNetV1(x, params.mobilenetv1);\n\n      const {\n        boxPredictions,\n        classPredictions,\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer);\n    });\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {},\n  ): Promise<FaceDetection[]> {\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n\n    const netInput = await toNetInput(input);\n\n    const {\n      boxes: _boxes,\n      scores: _scores,\n    } = this.forwardInput(netInput);\n\n    const boxes = _boxes[0];\n    const scores = _scores[0];\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose();\n      _scores[i].dispose();\n    }\n\n    const scoresData = Array.from(await scores.data());\n    const iouThreshold = 0.5;\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence,\n    );\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0);\n    const inputSize = netInput.inputSize as number;\n    const padX = inputSize / reshapedDims.width;\n    const padY = inputSize / reshapedDims.height;\n\n    const boxesData = boxes.arraySync();\n    const results = indices\n      .map((idx) => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2]),\n        ].map((val) => val * padY);\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3]),\n        ].map((val) => val * padX);\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top,\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0),\n          },\n        );\n      });\n\n    boxes.dispose();\n    scores.dispose();\n    return results;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1();\n  net.extractWeights(weights);\n  return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`);\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612));\n    return tf.add(tf.relu(tf.sub(x, min)), min);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n    out = tf.sub(out, params.bn.sub);\n    out = tf.mul(out, params.bn.truediv);\n    out = tf.add(out, params.conv.bias);\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n    out = tf.add(out, params.bias);\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n    const sub = tf.tensor1d(extractWeights(size));\n    const truediv = tf.tensor1d(extractWeights(size));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` },\n    );\n    return { sub, truediv };\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n    return { conv, bn };\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0');\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  }\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry(`${prefix}/sub`, 1);\n    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n    return { sub, truediv };\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { filters, bias };\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`);\n    const bn = extractBatchNormParams(`${prefix}/bn`);\n    return { conv, bn };\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    // eslint-disable-next-line no-mixed-operators\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8'),\n    };\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8'),\n    };\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n  return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416;\n    this._scoreThreshold = scoreThreshold || 0.5;\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n    }\n  }\n\n  get inputSize(): number { return this._inputSize; }\n\n  get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n  public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2');\n    validateConfig(config);\n    this._config = config;\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config;\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1;\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0);\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n    let out = convWithBatchNorm(x, params.conv0);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = convWithBatchNorm(out, params.conv6);\n    out = convWithBatchNorm(out, params.conv7);\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor;\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D;\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n    });\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input), inputSize);\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput, inputSize);\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0),\n    };\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n    out.dispose();\n    out0.dispose();\n\n    const boxes = results.map((res) => res.box);\n    const scores = results.map((res) => res.score);\n    const classScores = results.map((res) => res.classScore);\n    const classNames = results.map((res) => this.config.classes[res.label]);\n\n    const indices = nonMaxSuppression(\n      boxes.map((box) => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true,\n    );\n\n    const detections = indices.map((idx) => new ObjectDetection(\n      scores[idx],\n      classScores[idx],\n      classNames[idx],\n      boxes[idx],\n      inputDimensions,\n    ));\n    return detections;\n  }\n\n  protected getDefaultModelName(): string {\n    return '';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap, this.config);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n    const numFilters = filterSizes ? filterSizes.length : undefined;\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number,\n  ) {\n    const { width, height } = inputBlobDimensions;\n    const inputSize = Math.max(width, height);\n    const correctionFactorX = inputSize / width;\n    const correctionFactorY = inputSize / height;\n\n    const numCells = outputTensor.shape[1];\n    const numBoxes = this.config.anchors.length;\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0);\n      return [boxes, scores, classScores];\n    });\n\n    const results = [] as any;\n    const scoresData = await scoresTensor.array();\n    const boxesData = await boxesTensor.array();\n    for (let row = 0; row < numCells; row++) {\n      for (let col = 0; col < numCells; col++) {\n        for (let anchor = 0; anchor < numBoxes; anchor++) {\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n            const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n            const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n            const x = (ctX - (widthLocal / 2));\n            const y = (ctY - (heightLocal / 2));\n            const pos = { row, col, anchor };\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 };\n            results.push({\n              box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n              score,\n              classScore: score * classScore,\n              label,\n              ...pos,\n            });\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose();\n    scoresTensor.dispose();\n    classScoresTensor.dispose();\n    return results;\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n    const { row, col, anchor } = pos;\n    const classesData = await classesTensor.array();\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label,\n      }))\n      .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n  constructor(withSeparableConvs: boolean = true) {\n    const config = {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      ...(withSeparableConvs\n        ? {\n          anchors: BOX_ANCHORS_SEPARABLE,\n          meanRgb: MEAN_RGB_SEPARABLE,\n        }\n        : {\n          anchors: BOX_ANCHORS,\n          withClassScores: true,\n        }),\n    };\n\n    super(config);\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs;\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeightMap(weightMap);\n  }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs);\n  net.extractWeights(weights);\n  return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask<T> {\n  public async then(\n    // eslint-disable-next-line no-unused-vars\n    onfulfilled: (value: T) => T | PromiseLike<T>,\n  ): Promise<T> {\n    return onfulfilled(await this.run());\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented');\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n  const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n    ? getRectForAlignment(parentResult)\n    : parentResult.detection));\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  );\n\n  const results = await computeResults(faces);\n\n  faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n  return results;\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment?: (parentResultLocal: WithFaceLandmarks<TSource, any>) => FaceDetection,\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async (faces) => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment,\n  );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512],\n    };\n\n    super(config);\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeightMap(weightMap);\n  }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]> => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i]),\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces,\n    );\n\n    return extendWithFaceExpressions(parentResult, faceExpressions);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n    const parentResults = await this.parentTask;\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i];\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n    });\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces,\n    );\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n  ) {\n    super();\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>)),\n      null,\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      // eslint-disable-next-line no-shadow\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return extendWithFaceDescriptor(parentResult, descriptor);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected useTinyLandmarkNet: boolean,\n  ) {\n    super();\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net;\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const detections = parentResults.map((res) => res.detection);\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections);\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      (face) => this.landmarkNet.detectLandmarks(face),\n    )) as FaceLandmarks68[];\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return parentResults.map((parentResult, i) => extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i]));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { detection } = parentResult;\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection]);\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n  ) {\n    super();\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n  public async run(): Promise<FaceDetection[]> {\n    const { input, options } = this;\n\n    // eslint-disable-next-line no-nested-ternary\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      // eslint-disable-next-line no-shadow\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        // eslint-disable-next-line no-nested-ternary\n        options instanceof SsdMobilenetv1Options\n          // eslint-disable-next-line no-shadow\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              // eslint-disable-next-line no-shadow\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      );\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');\n    }\n\n    return faceDetectionFunction(input);\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}>[]>(async (resolve) => {\n      const detections = await this.run();\n      resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach((faceDetection) => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}> | undefined>(async (resolve) => {\n      const detection = await this.run();\n      resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number,\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {},\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n  const desc1 = Array.from(arr1);\n  const desc2 = Array.from(arr2);\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + (diff ** 2), 0),\n  );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6,\n  ) {\n    this._distanceThreshold = distanceThreshold;\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n    if (!inputArray.length) {\n      throw new Error('FaceRecognizer.constructor - expected atleast one input');\n    }\n\n    let count = 1;\n    const createUniqueLabel = () => `person ${count++}`;\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc;\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n      }\n\n      throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>');\n    });\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n  public get distanceThreshold(): number { return this._distanceThreshold; }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map((d) => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1);\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n        label,\n        this.computeMeanDistance(queryDescriptor, descriptors),\n      ))\n      .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor);\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance);\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector();\n  net.extractWeights(weights);\n  return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height);\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height));\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height);\n  }\n\n  return results;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n"],
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   "names": []
 }
diff --git a/dist/face-api.esm.json b/dist/face-api.esm.json
index e2d4202..fbfda08 100644
--- a/dist/face-api.esm.json
+++ b/dist/face-api.esm.json
@@ -1,7 +1,7 @@
 {
   "inputs": {
     "dist/tfjs.esm.js": {
-      "bytes": 1053904,
+      "bytes": 1055784,
       "imports": []
     },
     "src/draw/drawContour.ts": {
@@ -2591,7 +2591,7 @@
       "imports": [],
       "exports": [],
       "inputs": {},
-      "bytes": 1445459
+      "bytes": 1447733
     },
     "dist/face-api.esm.js": {
       "imports": [],
@@ -2722,7 +2722,7 @@
           "bytesInOutput": 181
         },
         "dist/tfjs.esm.js": {
-          "bytesInOutput": 1045331
+          "bytesInOutput": 1047212
         },
         "src/draw/index.ts": {
           "bytesInOutput": 280
@@ -3187,7 +3187,7 @@
           "bytesInOutput": 446
         }
       },
-      "bytes": 1132745
+      "bytes": 1134626
     }
   }
 }
diff --git a/dist/face-api.js b/dist/face-api.js
index 2ff79fe..7f9f148 100644
--- a/dist/face-api.js
+++ b/dist/face-api.js
@@ -5,43 +5,43 @@
   author: <https://github.com/vladmandic>'
   */
 
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To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await Ga().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Ga().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return zl.print(this,e)}clone(){return this.throwIfDisposed(),zl.clone(this)}toString(e=!1){let t=this.dataSync();return FA(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),zl.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Ga().makeVariable(this,e,t,n)}};Object.defineProperty(Ae,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Y(){return ly("Tensor",()=>Ae)}Y();var jr=class extends Ae{constructor(e,t,n,a){super(e.shape,e.dtype,e.dataId,a);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(!gr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Ga().disposeTensor(this),this.dataId=e.dataId,Ga().incRef(this,null)}dispose(){Ga().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(jr,Symbol.hasInstance,{value:e=>e instanceof Ae&&e.assign!=null&&e.assign instanceof Function});var Na={};Oe(Na,{assertTypesMatch:()=>b0,getTensorsInContainer:()=>hy,isTensorInList:()=>PA,makeTypesMatch:()=>Nt});var my;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(my||(my={}));var fy;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(fy||(fy={}));var gy;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(gy||(gy={}));var yy;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(yy||(yy={}));var by;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(by||(by={}));var OA={float32:yy,int32:fy,bool:gy,complex64:by};function ca(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return OA[e][t]}function yh(e){return ca(e,"int32")}function Nt(e,t){if(e.dtype===t.dtype)return[e,t];let n=ca(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function b0(e,t){A(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function PA(e,t){return t.some(n=>n.id===e.id)}function hy(e){let t=[],n=new Set;return x0(e,t,n),t}function x0(e,t,n){if(e==null)return;if(e instanceof Ae){t.push(e);return}if(!LA(e))return;let a=e;for(let r in a){let s=a[r];n.has(s)||(n.add(s),x0(s,t,n))}}function LA(e){return Array.isArray(e)||typeof e=="object"}function xy(e){return e.kernelName!=null}var v0=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Lc=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new v0}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;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 CA(this.backendInstance),!0}setupRegisteredKernels(){hh(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){hh(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 dc)&&typeof n.then=="function"){let a=++this.pendingBackendInitId,r=n.then(s=>a<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(a<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,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:a,asyncInit:r}=this.initializeBackend(n);if(r||a)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),a=n.backend,r=this.readSync(t);a.disposeData(t),n.backend=e,e.move(t,r,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let a;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(a),()=>(a=t(),a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),a))}scopedRun(e,t,n){e();try{let a=n();return t(),a}catch(a){throw t(),a}}nextTensorId(){return Lc.nextTensorId++}nextVariableId(){return Lc.nextVariableId++}clone(e){let t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return M.runKernel(Ps,o,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],a,r,{}),t}runKernel(e,t,n){if(dh(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=xy(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(xy(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=dh(h,this.backendName);A(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let b=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,b);let v=b.map(x=>{if(x.rank!=null)return x;let{dataId:N,shape:T,dtype:C}=x;return this.makeTensorFromDataId(N,T,C)});if(a){let x=this.getTensorsForGradient(h,m,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:c,attrs:u}=e,p=xy(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)}),a&&this.addTapeNode(l,c,t,p,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(h=>c[h]!=null?c[h].shape:null),outputShapes:t.map(h=>h.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 a=cy(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(A(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,c)=>s[c]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&Br(e[0])&&(r=e.map(o=>Rc(o)));let s=a.write(r,t,n),i=new Ae(t,n,s,this.nextTensorId());if(this.incRef(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=i0(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new Ae(t,n,e,this.nextTensorId());return this.incRef(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new jr(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}incRef(e,t){let n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let a=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(a=e.size*s0(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:a,refCount:0}),this.state.numBytes+=a}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof jr||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;let t=this.state.tensorInfo.get(e.dataId);t.refCount<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):(t.backend.decComplexRef(e.dataId),this.state.tensorInfo.get(e.dataId).refCount--)}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(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of 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s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(A(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));A(r instanceof Ae,()=>"The result y returned by f() must be a tensor.");let s=_A(this.state.activeTape,t,r);if(!a&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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+`;return d[d.length-1]=" "+d[d.length-1]+"]"+(s?"":m),d}function Oc(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Ot=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Pt(e),n!=null){let a=n.length;A(a===this.size,()=>`Length of values '${a}' does not match the size inferred by the shape '${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||l0(t,this.size),this.strides=Oo(e)}set(e,...t){t.length===0&&(t=[0]),A(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 a of e){if(a<0||a>=this.shape[t]){let r=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(r)}t++}let n=e[e.length-1];for(let a=0;a<e.length-1;++a)n+=this.strides[a]*e[a];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 Ga().makeTensor(this.values,this.shape,this.dtype)}},Ga=null,zl=null,LA=null;function zA(e){Ga=e}function BA(e){zl=e}function WA(e){LA=e}var Ae=class{constructor(e,t,n,a){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Pt(e),this.strides=Oo(e),this.dataId=n,this.id=a,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return zl.buffer(this.shape,this.dtype,e)}bufferSync(){return zl.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Lo(this.shape,e)}arraySync(){return Lo(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let e=Ga().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>bh(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=Ga().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>bh(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 Ga().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Ga().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return zl.print(this,e)}clone(){return this.throwIfDisposed(),zl.clone(this)}toString(e=!1){let t=this.dataSync();return OA(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),zl.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Ga().makeVariable(this,e,t,n)}};Object.defineProperty(Ae,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Y(){return dy("Tensor",()=>Ae)}Y();var qr=class extends Ae{constructor(e,t,n,a){super(e.shape,e.dtype,e.dataId,a);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(!yr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Ga().disposeTensor(this),this.dataId=e.dataId,Ga().incRef(this,null)}dispose(){Ga().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(qr,Symbol.hasInstance,{value:e=>e instanceof Ae&&e.assign!=null&&e.assign instanceof Function});var Ta={};Oe(Ta,{assertTypesMatch:()=>I0,getTensorsInContainer:()=>gy,isTensorInList:()=>VA,makeTypesMatch:()=>Nt});var yy;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(yy||(yy={}));var by;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(by||(by={}));var xy;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(xy||(xy={}));var vy;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(vy||(vy={}));var wy;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(wy||(wy={}));var UA={float32:vy,int32:by,bool:xy,complex64:wy};function pa(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return UA[e][t]}function vh(e){return pa(e,"int32")}function Nt(e,t){if(e.dtype===t.dtype)return[e,t];let n=pa(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function I0(e,t){A(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function VA(e,t){return t.some(n=>n.id===e.id)}function gy(e){let t=[],n=new Set;return N0(e,t,n),t}function N0(e,t,n){if(e==null)return;if(e instanceof Ae){t.push(e);return}if(!GA(e))return;let a=e;for(let r in a){let s=a[r];n.has(s)||(n.add(s),N0(s,t,n))}}function GA(e){return Array.isArray(e)||typeof e=="object"}function ky(e){return e.kernelName!=null}var T0=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Lc=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new T0}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;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 DA(this.backendInstance),!0}setupRegisteredKernels(){gh(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){gh(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 pc)&&typeof n.then=="function"){let a=++this.pendingBackendInitId,r=n.then(s=>a<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(a<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,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:a,asyncInit:r}=this.initializeBackend(n);if(r||a)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),a=n.backend,r=this.readSync(t),s=a.refCount(t);a.disposeData(t,!0),n.backend=e,e.move(t,r,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 to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let a;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(a),()=>(a=t(),a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),a))}scopedRun(e,t,n){e();try{let a=n();return t(),a}catch(a){throw t(),a}}nextTensorId(){return Lc.nextTensorId++}nextVariableId(){return Lc.nextVariableId++}clone(e){let t=M.runKernel(Xs,{x:e}),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return M.runKernel(Ps,o,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],a,r,{}),t}runKernel(e,t,n){if(fh(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=ky(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(ky(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=fh(h,this.backendName);A(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let b=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,b);let v=b.map(x=>{if(x.rank!=null)return x;let{dataId:N,shape:T,dtype:C}=x;return this.makeTensorFromDataId(N,T,C)});if(a){let x=this.getTensorsForGradient(h,m,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:c,attrs:u}=e,p=ky(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)}),a&&this.addTapeNode(l,c,t,p,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(h=>c[h]!=null?c[h].shape:null),outputShapes:t.map(h=>h.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 a=my(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(A(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,c)=>s[c]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&Wr(e[0])&&(r=e.map(o=>Rc(o)));let s=a.write(r,t,n),i=new Ae(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=p0(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new Ae(t,n,e,this.nextTensorId());return this.trackTensor(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new qr(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*iy(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 qr||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*iy(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(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of 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s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(A(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));A(r instanceof Ae,()=>"The result y returned by f() must be a tensor.");let s=RA(this.state.activeTape,t,r);if(!a&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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i=E(e,"x","batchNorm"),o=E(t,"mean","batchNorm"),l=E(n,"variance","batchNorm"),c;r!=null&&(c=E(r,"scale","batchNorm"));let u;return a!=null&&(u=E(a,"offset","batchNorm")),A(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),A(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),A(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&A(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&A(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),xr(i,o,l,u,c,s)}var b1=O({batchNorm4d_:sD});function iD(e,t,n){let a=E(e,"x","bincount"),r=E(t,"weights","bincount");A(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),A(n>=0,()=>`size must be non-negative, but got 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Qy=O({ceil_:lD});function uD(e,t,n){let a=E(e,"x","clipByValue");A(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:a},s={clipValueMin:t,clipValueMax:n};return M.runKernel(Gr,r,s)}var qt=O({clipByValue_:uD});function cD(e){return Qe(e,0)}var v1=O({concat1d_:cD});function pD(e,t){return Qe(e,t)}var w1=O({concat2d_:pD});function dD(e,t){return Qe(e,t)}var k1=O({concat3d_:dD});function hD(e,t){return Qe(e,t)}var I1=O({concat4d_:hD});function mD(e,t,n,a,r="NHWC",s=[1,1],i){let o=E(e,"x","conv2d"),l=E(t,"filter","conv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),A(c.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),A(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&A(Gt(a),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p=r==="NHWC"?c.shape[3]:c.shape[1];A(p===l.shape[2],()=>`Error in conv2d: depth 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vO=P({softmaxCrossEntropy_:xO}),wO={fft:Qc,ifft:eu,rfft:ep,irfft:qh},kO={hammingWindow:xP,hannWindow:j1,frame:q1,stft:IP},Ja={flipLeftRight:CP,resizeNearestNeighbor:Q1,resizeBilinear:J1,rotateWithOffset:EP,cropAndResize:TP,nonMaxSuppression:FP,nonMaxSuppressionAsync:zP,nonMaxSuppressionWithScore:WP,nonMaxSuppressionWithScoreAsync:UP,nonMaxSuppressionPadded:HP,nonMaxSuppressionPaddedAsync:qP},tk={bandPart:ZP,gramSchmidt:QP,qr:tO},IO={absoluteDifference:rO,computeWeightedLoss:vr,cosineDistance:iO,hingeLoss:lO,huberLoss:cO,logLoss:dO,meanSquaredError:mO,sigmoidCrossEntropy:yO,softmaxCrossEntropy:vO},wr=class extends t1{minimize(e,t=!1,n){let{value:a,grads:r}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(s)}else this.applyGradients(r);return Fe(r),t?a:(a.dispose(),null)}get iterations(){return 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wr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=M.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:D(()=>kn(a.shape,this.initialAccumulatorValue).variable(i))}}let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;D(()=>{let i=Z(s,ut(r));s.assign(i);let o=Z(L(ve(r,tn(Z(i,M.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Fe(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};sm.className="Adamax";Zr(sm);var tp=class extends wr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=M.registeredVariables[t];D(()=>{let s=Z(L(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=jt(de(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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a=M.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:D(()=>Ge(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:D(()=>Ge(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:D(()=>Ge(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;D(()=>{let l=Z(L(i,this.decay),L(ut(s),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[n].variable,u=Z(L(c,this.decay),L(s,1-this.decay)),p=ve(L(s,this.learningRate),tn(fe(l,Z(ut(u),this.epsilon)))),d=Z(L(o,this.momentum),p);i.assign(l),c.assign(u),o.assign(d);let h=fe(a,d);a.assign(h)}else{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};lm.className="Adamax";Jr(lm);var tp=class extends kr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=M.registeredVariables[t];D(()=>{let s=Z(L(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=jt(de(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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extends qe{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=vm(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],ss(this.inputs).length!==this.inputs.length)throw new z(`The list of inputs passed to the model is redundant. 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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 b=y.sourceLayer,v=y.nodeIndex,x=y.tensorIndex;this.outputLayers.push(b),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let y of this.inputs){let b=y.sourceLayer,v=y.nodeIndex,x=y.tensorIndex;er(v===0,"input layer has >1 nodes"),er(x===0,"input layer has >1 tensors"),this.inputLayers.push(b),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let b=this.inputLayers[y];if(!(b instanceof su))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${b.getClassName()}.`);this.inputNames.push(b.name),this.feedInputShapes.push(b.batchInputShape),this.feedInputNames.push(b.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={},a={},r={},s={},i=[],o=(y,b,v,x,N,T)=>{(x==null||N==null||T==null)&&(x=y.sourceLayer,N=y.nodeIndex,T=y.tensorIndex);let C=x.inboundNodes[N];if(v.indexOf(C)!==-1)throw new Ea(`The tensor ${y.name} at layer "${x.name}" is part of a cycle.`);if(b.indexOf(C)!==-1)return;this.containerNodes.add(rr.nodeKey(x,N)),x.id in s||(s[x.id]=Object.keys(s).length),v.indexOf(C)===-1&&v.push(C);let $=C.inboundLayers.length;for(let F=0;F<$;F++){let O=C.inputTensors[F],B=C.inboundLayers[F],W=C.nodeIndices[F],G=C.tensorIndices[F];o(O,b,v,B,W,G)}for(b.push(C);v.indexOf(C)>=0;)v.splice(v.indexOf(C),1);i.push(C)},l=[],c=[];for(let y of this.outputs)o(y,l,c);let u=i.slice().reverse();for(let y of u){n[y.id]=y,y.id in t||(t[y.id]=0);let b=t[y.id],v=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];b=Math.max(b,v),a[y.outboundLayer.id]=b,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=b;for(let x=0;x<y.inboundLayers.length;x++){let N=y.inboundLayers[x],T=y.nodeIndices[x],C=N.inboundNodes[T],$=t[C.id]==null?0:t[C.id];t[C.id]=Math.max(b+1,$),n[C.id]=C}}let p={};for(let y in t){let b=t[y];b in p||(p[b]=[]),p[b].push(n[y])}let d={};for(let y in a){let b=a[y];b in d||(d[b]=[]),d[b].push(r[y])}let h=Object.keys(d).map(y=>parseInt(y,10)).sort(um);this.layers=[];for(let y of h){let b=d[y];b.sort((v,x)=>{let N=s[v.id],T=s[x.id];return N<T?-1:N>T?1:0});for(let v of b)v instanceof rr&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(y=>parseInt(y,10)).sort(um);let m=this.inputs.slice(),f=[];for(let y of h)for(let b of p[y]){let v=b.outboundLayer;if(v!=null){for(let x of b.inputTensors)if(m.indexOf(x)===-1)throw new Ea(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${f}`);for(let x of b.outputTensors)m.push(x);f.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(y=>y.name);for(let y of g){let b=g.filter(v=>v===y).length;if(b!==1)throw new Ea(`The name "${y}" is used ${b} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Im({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new z("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new z(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,a++}let r=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[s]]);else if(t)throw new z(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new z(`${s.length} of ${a} weights are not set: ${s}`)}Qb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${$m}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=ix(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return D(()=>{e=bt(e);let n=new Gi;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return dp(this.outputs,n,t)})}computeMask(e,t){return D(()=>{e=bt(e);let n;return t==null?n=Li(null,e.length):n=bt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=wm(e);if(t.length!==this.inputLayers.length)throw new z(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(um);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(c.id)!==-1)continue;let u=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],b=`${f.name}_${g}_${y}`,v=n[b];u.push(v)}let p=c.computeOutputShape(In(u)),d=wm(p),h=c.inboundNodes.indexOf(l);for(let m=0;m<d.length;m++){let f=`${c.name}_${h}_${m}`;n[f]=d[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${c}`;s.push(u)}for(let i=0;i<s.length;i++){let o=s[i];er(o in n),r.push(n[o])}return In(r)}runInternalGraph(e,t){t==null&&(t=Li(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],c=e[o],u=t[o];n[l.id]=[c,u]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(um);for(let o of a){let l=this.nodesByDepth[o];for(let c of l){let u=c.outboundLayer,p=c.inputTensors,d=c.outputTensors,h=new Array;for(let m of p)m.id in n&&h.push(n[m.id]);if(h.length===p.length){let m={},f,g,y,b;if(c.callArgs!=null&&(m=c.callArgs),h.length===1){let[v,x]=h[0];m.mask==null&&(m.mask=x),y=bt(u.call(v,m)),b=bt(u.computeMask(v,x)),f=[v],g=[x]}else f=h.map(v=>v[0]),g=h.map(v=>v[1]),m.mask==null&&(m.mask=g),y=bt(u.call(f,m)),b=bt(u.computeMask(f,g));if(u.activityRegularizer)throw new $e("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],N=y[v],T=b[v];n[x.id]=[N,T]}}}}let r=[],s=[],i=[];for(let o of this.outputs){er(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,c]=n[o.id];i.push(l.shape),r.push(l),s.push(c)}return[r,s,i]}buildNodeConversionMap(e){let t={},n;for(let a of this.layers){n=a instanceof rr?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=rr.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new z(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new z("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new z(`No such layer: ${e}`)}calculateLosses(){return D(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let a=rr.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let u=0;u<s.inboundNodes.length;u++){let p=s.inboundNodes[u],d=rr.nodeKey(s,u),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let m=[];for(let f=0;f<p.inboundLayers.length;f++){let g=p.inboundLayers[f],y=p.nodeIndices[f],b=p.tensorIndices[f],v=rr.nodeKey(g,y),x=t[v];x==null&&(x=0),m.push([g.name,x,b,h])}l.push(m)}}}let c={};c.name=s.name,c.className=i,c.config=o,c.inboundNodes=l,n.push(c)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=rr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[s];a.push([i.name,c,u])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=rr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[s];r.push([i.name,c,u])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(f,g){f.name in s?s[f.name].push(g):s[f.name]=[g]}function o(f,g){let y=[],b;for(let v of g){let x=v[0],N=v[1],T=v[2];if(b=v[3]==null?{}:v[3],!(x in r)){i(f,g);return}let C=r[x];if(C.inboundNodes.length<=N){i(f,g);return}let $=C.inboundNodes[N];y.push($.outputTensors[T])}y.length>0&&f.apply(In(y),b)}function l(f){let g=f.name,y=$a(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),r[g]=y,f.inboundNodes.forEach(b=>{if(!(b instanceof Array))throw new z(`Corrupted configuration, expected array for nodeData: ${b}`);i(y,b)})}let c=t.name,u=t.layers;for(let f of u)l(f);for(;!uz(s);)for(let f of u){let g=r[f.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let b of y)o(g,b)}}let p=[],d=[],h=t.inputLayers;for(let f of h){let g=f[0],y=f[1],b=f[2];er(g in r);let v=r[g].inboundNodes[y].outputTensors;p.push(v[b])}let m=t.outputLayers;for(let f of m){let g=f[0],y=f[1],b=f[2];er(g in r);let v=r[g].inboundNodes[y].outputTensors;d.push(v[b])}return new e({inputs:p,outputs:d,name:c})}get stateful(){if(this._stateful)throw new z("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){D(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function LW(e,t,n){let a=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(a===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==a)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${a} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(s=>{s in e?r.push(e[s]):r.push(null)}),r}else throw new Error(`The model has multiple (${a}) outputs, so ${n} must be either an array with ${a} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function iI(e,t){return LW(e,t,"classWeight")}async function oI(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=D(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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 r.data());Fe(r);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])}),nt(i,"float32")}else return null}function zW(e,t){return L(e,t)}var BW=32;function uI(e,t){let n,a,r=t;n=r.xs,a=r.ys,k.assert(n!=null&&a!=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=lI("input",e.inputNames,n),i=lI("output",e.outputNames,a),o=s[0].shape[0];k.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)})`),k.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)k.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++)k.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 lI(e,t,n){if(n instanceof Ae)return[n];if(Array.isArray(n))return k.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let a=[];for(let r of t){if(n[r]==null)throw new z(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function WW(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 UW(e,t,n){let a=n.batchesPerEpoch!=null;if(k.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k.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}`),k.assert(!a||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,s,i;if(r)if(cI(n.validationData))k.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=WW(n.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;r?c=l.slice().concat(l.map(g=>"val_"+g)):c=l.slice();let u=Kk(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=Xk(u,p,n.epochs,null,null,VW(t,n),null,r,c);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let m=n.initialEpoch==null?0:n.initialEpoch,f=await t.iterator();for(;m<n.epochs;){let g={};await d.onEpochBegin(m);let y=0,b=0;for(a||(f=await t.iterator());a?y<n.batchesPerEpoch:!0;){let v=await f.next();if(a&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Ea("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new Ea("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new Ea("The model needs to be compiled before being used.");return 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};xx.className="ThresholdedReLU";re.registerClass(xx);var vx=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new dx().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Me(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}};vx.className="Softmax";re.registerClass(vx);function uu(e,t,n){if(typeof e=="number")return Li(e,t);if(e.length!==t)throw new z(`The ${n} argument must be an integer or tuple of ${t} integers. 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instead`);if(s==="channelsFirst"&&(e=Ue(e,[0,2,1])),r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=_h(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=nr(o,n)),o})}function $I(e,t,n,a=[1,1],r="valid",s,i,o=null){return D(()=>{if(s==null&&(s=_a()),Dt(s),e.rank!==3&&e.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=wx(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=rs.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Ue(l,[0,3,1,2])),l})}function c4(e,t,n,a=[1,1,1],r="valid",s,i){return D(()=>{if(s==null&&(s=_a()),Dt(s),e.rank!==4&&e.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=FI(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Jy(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=nr(o,n)),s==="channelsFirst"&&(o=Ue(o,[0,4,1,2,3])),o})}var kx=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",kx.verifyArgs(t),this.rank=e,Kt(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=uu(t.kernelSize,e,"kernelSize"),this.strides=uu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Qn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Dt(this.dataFormat),this.activation=cs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=wt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Vt(t.biasConstraint),this.biasRegularizer=kt(t.biasRegularizer),this.activityRegularizer=kt(t.activityRegularizer),this.dilationRate=uu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`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 z(`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 z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(er("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Fb(e.kernelSize,"number",1,3))throw new z(`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:us(this.activation),useBias:this.useBias,biasInitializer:Ct(this.biasInitializer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},fp=class extends kx{constructor(e,t){super(e,t);this.kernel=null,fp.verifyArgs(t),this.filters=t.filters,Kt(this.filters,"filters"),this.kernelInitializer=wt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Vt(t.kernelConstraint),this.kernelRegularizer=kt(t.kernelRegularizer)}build(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return D(()=>{e=Me(e);let n,a=this.bias==null?null:this.bias.read(),r=yk(this.activation.getClassName());if(r!=null&&this.rank===2)n=$I(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=u4(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=$I(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=c4(e,this.kernel.read(),a,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=ht(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let s=Da(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:Ct(this.kernelInitializer),kernelRegularizer:mt(this.kernelRegularizer),kernelConstraint:Wt(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 z(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},gp=class extends fp{constructor(e){super(2,e);gp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Fb(e.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};gp.className="Conv2D";re.registerClass(gp);var Rm=class extends fp{constructor(e){super(3,e);Rm.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 z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Rm.className="Conv3D";re.registerClass(Rm);var Ix=class extends gp{constructor(e){super(e);if(this.inputSpec=[new Xt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ht(e),e.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Xt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{let n=Me(e);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],c=this.kernelSize[0],u=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Dm(o,p,c,this.padding),m=Dm(l,d,u,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ue(n,[0,2,3,1]));let g=Eh(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ue(g,[0,3,1,2])),this.bias!=null&&(g=nr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ht(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=Dm(t[a],o,s,this.padding),t[r]=Dm(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ix.className="Conv2DTranspose";re.registerClass(Ix);var DI=class extends fp{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 z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new z("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 z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=kt(t.depthwiseRegularizer),this.depthwiseConstraint=Vt(t.depthwiseConstraint),this.pointwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=kt(t.pointwiseRegularizer),this.pointwiseConstraint=Vt(t.pointwiseConstraint)}build(e){if(e=ht(e),e.length<this.rank+2)throw new z(`Inputs to SeparableConv${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 z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"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 Xt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{e=Me(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=Ue(e,[0,2,3,1])),n=Mi(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=nr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ue(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=Ct(this.depthwiseInitializer),e.pointwiseInitializer=Ct(this.pointwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.pointwiseRegularizer=mt(this.pointwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseConstraint),e.pointwiseConstraint=Wt(this.pointwiseConstraint),e}};DI.className="SeparableConv";var Nx=class extends DI{constructor(e){super(2,e)}};Nx.className="SeparableConv2D";re.registerClass(Nx);var Mm=class extends fp{constructor(e){super(1,e);Mm.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"&&!Fb(e.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Mm.className="Conv1D";re.registerClass(Mm);var Tx=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 D(()=>{if(e=Me(e),this.dataFormat==="channelsLast"){let n=cm(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return cm(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=cm(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return cm(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}};Tx.className="Cropping2D";re.registerClass(Tx);var Sx=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,Dt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Fz(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 D(()=>{let n=Me(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ue(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s]);return Ue(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Sx.className="UpSampling2D";re.registerClass(Sx);function p4(e,t,n=[1,1],a="valid",r,s){return D(()=>{r==null&&(r=_a()),Dt(r);let i=wx(e,r);if(e.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=es(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}var Cx=class extends kx{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=Vt(e.depthwiseConstraint),this.depthwiseRegularizer=kt(e.depthwiseRegularizer)}build(e){if(e=ht(e),e.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,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 D(()=>{e=Me(e);let n=p4(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=nr(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],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Da(t,this.kernelSize[0],this.padding,this.strides[0]),s=Da(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ct(this.depthwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseRegularizer),e}};Cx.className="DepthwiseConv2D";re.registerClass(Cx);function RI(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function MI(e,t,n,a=!1,r,s,i=!1,o=!1){return D(()=>{let l=t.shape.length;if(l<3)throw new z(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Aa(2,l));if(t=Ue(t,c),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=Fn(r,-1)),r=Ue(r,c)),a&&(t=Rn(t,0),r!=null&&(r=Rn(r,0)));let u=[],p,d=n,h=t.shape[0],m=dt(t),f;r!=null&&(f=dt(r));for(let y=0;y<h;++y){let b=m[y],v=D(()=>e(b,d));if(r==null)p=v[0],d=v[1];else{let x=D(()=>{let N=f[y],T=Dn(N).sub(N),C=v[0].mul(N).add(d[0].mul(T)),$=d.map((F,O)=>v[1][O].mul(N).add(F.mul(T)));return{output:C,newStates:$}});p=x.output,d=x.newStates}o&&u.push(p)}let g;return o&&(g=$t(u,1)),[p,g,d]})}var ar=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Pm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new z("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 Xt({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 Aa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Zb(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return D(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}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.");Zb(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new Xt({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new $e("Constants support is not implemented in RNN yet.");this.cell.build(r);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!k.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Xt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new kr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("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(a=>vt([n,a])):this.states_=[vt([n,this.cell.stateSize])];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>vt([n,a])):this.states_[0]=vt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`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()):Fe(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!k.arraysEqual(r.shape,i))throw new z(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>jt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=RI(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.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 Xt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Fa){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 D(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Me(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new z(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=MI((d,h)=>{let m=this.cell.call([d].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,a);let p=this.returnSequences?c:l;return this.returnState?[p].concat(u):p})}getInitialState(e){return D(()=>{let t=vt(e.shape);return t=Te(t,[1,2]),t=ip(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Ob(t,[1,n]):t):this.cell.stateSize>1?[Ob(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===ar.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=$a(a,n);return new e(Object.assign(t,{cell:r}))}};ar.className="RNN";re.registerClass(ar);var up=class extends qe{},Om=class extends up{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=cs(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=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=ru([1,os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ru([1,os([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 D(()=>{if(e=e,e.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ps({ones:()=>Dn(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ps({ones:()=>Dn(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=tr(L(e,s),this.kernel.read()):r=tr(e,this.kernel.read()),this.bias!=null&&(r=nr(r,this.bias.read())),i!=null&&(n=L(n,i));let o=Z(r,tr(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:us(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Om.className="SimpleRNNCell";re.registerClass(Om);var _x=class extends ar{constructor(e){e.cell=new Om(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};_x.className="SimpleRNN";re.registerClass(_x);var Lm=class extends up{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 z("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Kt(this.units,"units"),this.activation=cs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=cs(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=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=ru([1,os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ru([1,os([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 D(()=>{if(e=e,e.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ps({ones:()=>Dn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ps({ones:()=>Dn(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,r[0]));let c=tr(e,this.kernel.read());this.useBias&&(c=nr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,s[0]));let u=this.recurrentKernel.read(),[p,d]=Mn(u,[2*this.units,this.units],u.rank-1),h=tr(a,p),[m,f,g]=Mn(c,3,c.rank-1),[y,b]=Mn(h,2,h.rank-1);i=this.recurrentActivation.apply(Z(m,y)),o=this.recurrentActivation.apply(Z(f,b));let v=tr(L(o,a),d);l=this.activation.apply(Z(g,v));let x=Z(L(i,a),L(Z(1,Tt(i)),l));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:us(this.activation),recurrentActivation:us(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Lm.className="GRUCell";re.registerClass(Lm);var Ex=class extends ar{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 Lm(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Ex.className="GRU";re.registerClass(Ex);var yp=class extends up{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=cs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=cs(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=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=ru([1,os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ru([1,os([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 a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends fa{apply(i,o){let l=r.apply([s]),c=new dm().apply([s]),u=r.apply([s*2]);return Ck(Ck(l,c),u)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ps({ones:()=>Dn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ps({ones:()=>Dn(a),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=L(e,s[0]));let p=tr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,i[0])),p=Z(p,tr(a,this.recurrentKernel.read())),this.useBias&&(p=nr(p,this.bias.read()));let[d,h,m,f]=Mn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(h),c=Z(L(l,r),L(o,this.activation.apply(m))),u=this.recurrentActivation.apply(f);let g=L(u,this.activation.apply(c));return[g,g,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:us(this.activation),recurrentActivation:us(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};yp.className="LSTMCell";re.registerClass(yp);var Ax=class extends ar{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 yp(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Ax.className="LSTM";re.registerClass(Ax);var Pm=class extends up{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 D(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){Zb(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Wi(`RNNCell_${a}`,()=>{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=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push($a(r,n));return new e({cells:a})}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 Jb(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}Qb(t)}};Pm.className="StackedRNNCells";re.registerClass(Pm);function ps(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>Ek(t(),n),i=()=>lp(s,t,a);return!r||r<=1?jt(i().clone()):Array(r).fill(void 0).map(i).map(o=>jt(o.clone()))}var d4=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},PI=class extends ar{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 Xt({ndim:5})]}call(e,t){return D(()=>{if(this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new z("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}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 D(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=vt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new kr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new z("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(()=>vt(r)):this.states_=[vt(r)];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>vt(r)):this.states_[0]=vt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`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()):Fe(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!k.arraysEqual(i.shape,o))throw new z(`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=>jt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=Da(l,a[0],r,s[0],i[0]),p=Da(c,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,p]:[u,p,n]]}};PI.className="ConvRNN2D";var zm=class extends yp{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Kt(this.filters,"filters"),this.kernelSize=uu(n,2,"kernelSize"),this.kernelSize.forEach(o=>Kt(o,"kernelSize")),this.strides=uu(a||1,2,"strides"),this.strides.forEach(o=>Kt(o,"strides")),this.padding=r||"valid",Qn(this.padding),this.dataFormat=s||"channelsLast",Dt(this.dataFormat),this.dilationRate=uu(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 z(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);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 fa{apply(u,p){let d=l.apply([c]),h=Za([c]),m=l.apply([c*2]);return zb([d,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return D(()=>{if(e.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ps({ones:()=>Dn(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(J,se,Q)=>!se||!se[Q]?J:L(se[Q],J),c=l(a,o,0),u=l(a,o,1),p=l(a,o,2),d=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ps({ones:()=>Dn(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[v,x,N,T]=Mn(this.kernel.read(),i,b),[C,$,F,O]=this.useBias?Mn(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,v,C,this.padding),u=this.inputConv(u,x,$,this.padding),p=this.inputConv(p,N,F,this.padding),d=this.inputConv(d,T,O,this.padding);let[B,W,G,q]=Mn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,B),f=this.recurrentConv(f,W),g=this.recurrentConv(g,G),y=this.recurrentConv(y,q);let H=this.recurrentActivation.apply(Z(c,m)),K=this.recurrentActivation.apply(Z(u,f)),X=Z(L(K,s),L(H,this.activation.apply(Z(p,g)))),ne=L(this.recurrentActivation.apply(Z(d,y)),this.activation.apply(X));return[ne,ne,X]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=d4(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=At(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?nr(r,n,this.dataFormat):r}recurrentConv(e,t){return At(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};zm.className="ConvLSTM2DCell";re.registerClass(zm);var Fx=class extends PI{constructor(e){let t=new zm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Fx.className="ConvLSTM2D";re.registerClass(Fx);var Bm=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 a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return lp(()=>Ek(n,this.rate,r,this.seed),()=>n,a)}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()}};Bm.className="Dropout";re.registerClass(Bm);var $x=class extends Bm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};$x.className="SpatialDropout1D";re.registerClass($x);var Dx=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,Kt(this.units,"units"),this.activation=cs(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=Vt(e.kernelConstraint),this.biasConstraint=Vt(e.biasConstraint),this.kernelRegularizer=kt(e.kernelRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.activityRegularizer=kt(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 D(()=>{this.invokeCallHook(e,t);let n=Me(e),a=yk(this.activation.getClassName()),r;return a!=null?r=tr(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=tr(n,this.kernel.read()),this.bias!=null&&(r=nr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:us(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Dx.className="Dense";re.registerClass(Dx);var Rx=class extends qe{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 z(`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],is(e,1)]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=n.transpose(a)}return Oz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Rx.className="Flatten";re.registerClass(Rx);var Mx=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=cs(e.activation)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);return this.activation.apply(n)})}getConfig(){let e={activation:us(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Mx.className="Activation";re.registerClass(Mx);var Px=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 D(()=>(e=Me(e),Mz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Px.className="RepeatVector";re.registerClass(Px);var Ox=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.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new z("Can only specifiy one unknown dimension.");else r*=l}let i=is(e);if(s!==null){if(r===0||i%r!=0)throw new z(n);a[s]=i/r}else if(i!==r)throw new z(n);return a}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 D(()=>{this.invokeCallHook(e,t);let n=Me(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return n.reshape(r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Ox.className="Reshape";re.registerClass(Ox);var Lx=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=Aa(1,e.dims.length+1);if(!k.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 Xt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ht(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Ue(Me(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Lx.className="Permute";re.registerClass(Lx);var zx=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=Me(e),a=-1;return Wc(Ri(n,this.maskValue),a)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e),a=-1,r=!0,s=Wc(Ri(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};zx.className="Masking";re.registerClass(zx);var Bx=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(bt(e.inputLength))}this.inputDim=e.inputDim,Kt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Kt(this.outputDim,"outputDim"),this.embeddingsInitializer=wt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=kt(e.embeddingsRegularizer),this.activityRegularizer=kt(e.activityRegularizer),this.embeddingsConstraint=Vt(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 D(()=>this.maskZero?(e=Me(e),Ri(e,Ge(e))):null)}computeOutputShape(e){if(e=ht(e),this.inputLength==null)return[...e,this.outputDim];let t=bt(this.inputLength);if(t.length!==e.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a<t.length;++a){let r=t[a],s=e[a+1];if(r!=null&&s!=null&&r!==s)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);return n.dtype!=="int32"&&(n=sp(n,"int32")),_k(this.embeddings.read(),n.as1D()).reshape(ht(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ct(this.embeddingsInitializer),embeddingsRegularizer:mt(this.embeddingsRegularizer),activityRegularizer:mt(this.activityRegularizer),embeddingsConstraint:Wt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Bx.className="Embedding";re.registerClass(Bx);var ji=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 a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ht(e)]),e=e,e.length<2)throw new z(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=ss(t),t.length>1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&ss(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return D(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=os(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=ip(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],p=c.slice(1).concat([u]),d=o.reshape([u].concat(is(c.slice(1))));d=Ue(d,[1,0]),d=d.reshape(p),n.push(d),r=!0}else if(l>1){let c=Aa(1,l).concat([0]);n.push(Ue(o,c)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=Ue(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(Aa(0,i-1));s=Ue(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=ss(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return D(()=>{if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an Array");if(!Array.isArray(e))throw new z("`inputs` should be an Array");if(t.length!==e.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:Fn(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=ha(n,t[a]);return n})}},Wx=class extends ji{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Z(t,e[n]);return t})}};Wx.className="Add";re.registerClass(Wx);var Vx=class extends ji{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};Vx.className="Multiply";re.registerClass(Vx);var Ux=class extends ji{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Z(t,e[n]);return L(1/e.length,t)})}};Ux.className="Average";re.registerClass(Ux);var Gx=class extends ji{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ya(t,e[n]);return t})}};Gx.className="Maximum";re.registerClass(Gx);var Hx=class extends ji{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Zl(t,e[n]);return t})}};Hx.className="Minimum";re.registerClass(Hx);var jx=class extends ji{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 z("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let a of e)if(a!=null){t=!1;break}if(t)return;let n=[];for(let a=0;a<e.length;++a){let r=e[a].slice();r.splice(this.axis,1);let s=!1;for(let i of n)if(k.arraysEqual(i,r)){s=!0;break}s||n.push(r)}if(n.length>1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return D(()=>zb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new z("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new z("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new z(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return D(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s<e.length;++s)t[s]==null?a.push(Dn(e[s]).asType("bool")):t[s].rank<e[s].rank?a.push(Fn(t[s],-1)):a.push(t[s]);let r=Qe(a,this.axis);return Sh(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};jx.className="Concatenate";re.registerClass(jx);function bp(e,t){for(;e<0;)e+=t;return e}function h4(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(k.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k.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 a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return D(()=>{let i;if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(r>a){i=r-a;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;a>r?l=a+r-3:l=a-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 qx=class extends ji{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k.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 a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new z(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>bp(r,e[s].shape.length)):a=[bp(this.axes,t.shape.length),bp(this.axes,n.shape.length)],this.normalize&&(t=Nm(t,a[0]),n=Nm(n,a[1])),h4(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[bp(this.axes,e.length),bp(this.axes,t.length)],n}computeOutputShape(e){k.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 a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};qx.className="Dot";re.registerClass(qx);var Kx=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 D(()=>{this.invokeCallHook(e,t);let n=Me(e);return lp(()=>pm(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Kx.className="GaussianNoise";re.registerClass(Kx);var Xx=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 D(()=>{this.invokeCallHook(e,t);let n=Me(e);return this.rate>0&&this.rate<1?lp(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(pm(n.shape,1,a))},()=>n,t.training||!1):n})}};Xx.className="GaussianDropout";re.registerClass(Xx);var Yx=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Me(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 D(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return lp(()=>{let a=Me(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=ns(Jl(n),this.rate);o=sp(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>Me(e),t.training||!1)}return e})}};Yx.className="AlphaDropout";re.registerClass(Yx);function xp(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=p1(e,t,n,a,r,s);else if(e.rank===3)i=d1(e,t,n,a,r,s);else if(e.rank===4)i=h1(e,t,n,a,r,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function m4(e,t,n,a,r=.001){return D(()=>{let s=Lh(e,a),i=s.mean,o=s.variance;return[xp(e,i,o,n,t,r),i,o]})}function f4(e,t,n,a,r=.001){return D(()=>{let s=Lh(e,a),i=s.mean,o=s.variance,l=[];for(let h of Aa(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let c=i.reshape(l),u=o.reshape(l),p=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[xp(e,c,u,d,p,r),i,o]})}function g4(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),Aa(0,e.rank-1))?m4(e,t,n,a,r):f4(e,t,n,a,r)}var Zx=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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.movingMeanInitializer=wt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=wt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Vt(e.betaConstraint),this.gammaConstraint=Vt(e.gammaConstraint),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer)}build(e){e=ht(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new z(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Xt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training,a=Me(e),r=a.shape,s=r.length,i=Aa(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Li(1,s);l[o]=r[o];let c=i.slice();c.sort();let u=!k.arraysEqual(c,Aa(0,s).slice(0,s-1)),p=()=>{if(u){let g=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),b=this.center?this.beta.read().reshape(l):null,v=this.scale?this.gamma.read().reshape(l):null;return xp(a,g,y,b,v,this.epsilon)}else return xp(a,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 p();let[d,h,m]=g4(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,b)=>{D(()=>{let v=1-b,x=g.read(),N=x.sub(y).mul(v);g.write(x.sub(N))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),movingMeanInitializer:Ct(this.movingMeanInitializer),movingVarianceInitializer:Ct(this.movingVarianceInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer),betaConstraint:Wt(this.betaConstraint),gammaConstraint:Wt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Zx.className="BatchNormalization";re.registerClass(Zx);var Jx=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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ht(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==ss(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Me(e),a=n.shape,r=a.length;return D(()=>{let s=!0,{mean:i,variance:o}=Lh(n,this.axis,s),l=Li(1,r);for(let m of this.axis)l[m]=a[m];let c=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(l):m,u=c(this.gamma.read()),p=c(this.beta.read()),d=[],h=[];for(let m=0;m<r;++m)this.axis.indexOf(m)!==-1?(d.push(a[m]),h.push(1)):(d.push(1),h.push(a[m]));return i=i.tile(d),o=o.tile(d),u=u.tile(h),p=p.tile(h),xp(n,i,o,p,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Jx.className="LayerNormalization";re.registerClass(Jx);function y4(e,t,n){return D(()=>{if(e.rank!==4)throw new z(`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 z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=_a()),n!=="channelsLast"&&n!=="channelsFirst")throw new z(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],Jn(e,a)})}var Qx=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?_a():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new z(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new z(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new z(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){e=ht(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return D(()=>y4(Me(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Qx.className="ZeroPadding2D";re.registerClass(Qx);function Wm(e,t,n,a,r,s){return D(()=>{Dt(r),wk(s),Qn(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=_a()),s==null&&(s="max"),e=wx(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Ft(e,t,n,o):i=Yn(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}function OI(e,t,n,a,r,s){return D(()=>{Dt(r),wk(s),Qn(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=_a()),s==null&&(s="max"),e=FI(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=ub(e,t,n,o):i=Xy(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,4,1,2,3])),i})}var LI=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 z(`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 z(`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,Qn(this.padding),this.inputSpec=[new Xt({ndim:3})]}computeOutputShape(e){e=ht(e);let t=Da(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return D(()=>{this.invokeCallHook(e,t),e=ip(Me(e),2);let n=this.poolingFunction(Me(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return as(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},ev=class extends LI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Qn(a),Wm(e,t,n,a,r,"max")}};ev.className="MaxPooling1D";re.registerClass(ev);var tv=class extends LI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Qn(a),Wm(e,t,n,a,r,"avg")}};tv.className="AveragePooling1D";re.registerClass(tv);var zI=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 z(`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];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Dt(this.dataFormat),Qn(this.padding),this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Da(t,this.poolSize[0],this.padding,this.strides[0]),n=Da(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 D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(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}},nv=class extends zI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Qn(a),Wm(e,t,n,a,r,"max")}};nv.className="MaxPooling2D";re.registerClass(nv);var av=class extends zI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Qn(a),Wm(e,t,n,a,r,"avg")}};av.className="AveragePooling2D";re.registerClass(av);var BI=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 z(`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,Dt(this.dataFormat),Qn(this.padding),this.inputSpec=[new Xt({ndim:5})]}computeOutputShape(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Da(t,this.poolSize[0],this.padding,this.strides[0]),n=Da(n,this.poolSize[1],this.padding,this.strides[1]),a=Da(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(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}},rv=class extends BI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Qn(a),OI(e,t,n,a,r,"max")}};rv.className="MaxPooling3D";re.registerClass(rv);var sv=class extends BI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Qn(a),OI(e,t,n,a,r,"avg")}};sv.className="AveragePooling3D";re.registerClass(sv);var WI=class extends qe{constructor(e){super(e);this.inputSpec=[new Xt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},iv=class extends WI{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Me(e);return St(n,1)})}};iv.className="GlobalAveragePooling1D";re.registerClass(iv);var ov=class extends WI{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Me(e);return Zn(n,1)})}};ov.className="GlobalMaxPooling1D";re.registerClass(ov);var VI=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Dt(this.dataFormat),this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new $e}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},lv=class extends VI{call(e,t){return D(()=>{let n=Me(e);return this.dataFormat==="channelsLast"?St(n,[1,2]):St(n,[2,3])})}};lv.className="GlobalAveragePooling2D";re.registerClass(lv);var uv=class extends VI{call(e,t){return D(()=>{let n=Me(e);return this.dataFormat==="channelsLast"?Zn(n,[1,2]):Zn(n,[2,3])})}};uv.className="GlobalMaxPooling2D";re.registerClass(uv);var UI=class extends qe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let a=t.layer,r=$a(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},cv=class extends UI{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ht(e),e.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=ht(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return D(()=>(e=Me(e),MI((n,a)=>[Me(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};cv.className="TimeDistributed";re.registerClass(cv);function b4(e){Bi(Az,"BidirectionalMergeMode",e)}var x4="concat",pv=class extends UI{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=$a(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=$a(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?x4:e.mergeMode,b4(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,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):In(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=RI(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new z("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 Xt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),i.push(...c)}if(a!=null)throw new $e("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Fa;for(let l of s)if(l instanceof Fa!==o)throw new z("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),c=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=c;let p=super.apply(l,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return D(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=Rn(r,1));let 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extends qe{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=Im(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],ss(this.inputs).length!==this.inputs.length)throw new z(`The list of inputs passed to the model is redundant. 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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 b=y.sourceLayer,v=y.nodeIndex,x=y.tensorIndex;this.outputLayers.push(b),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let y of this.inputs){let b=y.sourceLayer,v=y.nodeIndex,x=y.tensorIndex;tr(v===0,"input layer has >1 nodes"),tr(x===0,"input layer has >1 tensors"),this.inputLayers.push(b),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let b=this.inputLayers[y];if(!(b instanceof su))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${b.getClassName()}.`);this.inputNames.push(b.name),this.feedInputShapes.push(b.batchInputShape),this.feedInputNames.push(b.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={},a={},r={},s={},i=[],o=(y,b,v,x,N,T)=>{(x==null||N==null||T==null)&&(x=y.sourceLayer,N=y.nodeIndex,T=y.tensorIndex);let C=x.inboundNodes[N];if(v.indexOf(C)!==-1)throw new Aa(`The tensor ${y.name} at layer "${x.name}" is part of a cycle.`);if(b.indexOf(C)!==-1)return;this.containerNodes.add(sr.nodeKey(x,N)),x.id in s||(s[x.id]=Object.keys(s).length),v.indexOf(C)===-1&&v.push(C);let F=C.inboundLayers.length;for(let $=0;$<F;$++){let P=C.inputTensors[$],B=C.inboundLayers[$],W=C.nodeIndices[$],G=C.tensorIndices[$];o(P,b,v,B,W,G)}for(b.push(C);v.indexOf(C)>=0;)v.splice(v.indexOf(C),1);i.push(C)},l=[],c=[];for(let y of this.outputs)o(y,l,c);let u=i.slice().reverse();for(let y of u){n[y.id]=y,y.id in t||(t[y.id]=0);let b=t[y.id],v=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];b=Math.max(b,v),a[y.outboundLayer.id]=b,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=b;for(let x=0;x<y.inboundLayers.length;x++){let N=y.inboundLayers[x],T=y.nodeIndices[x],C=N.inboundNodes[T],F=t[C.id]==null?0:t[C.id];t[C.id]=Math.max(b+1,F),n[C.id]=C}}let p={};for(let y in t){let b=t[y];b in p||(p[b]=[]),p[b].push(n[y])}let d={};for(let y in a){let b=a[y];b in d||(d[b]=[]),d[b].push(r[y])}let h=Object.keys(d).map(y=>parseInt(y,10)).sort(dm);this.layers=[];for(let y of h){let b=d[y];b.sort((v,x)=>{let N=s[v.id],T=s[x.id];return N<T?-1:N>T?1:0});for(let v of b)v instanceof sr&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(y=>parseInt(y,10)).sort(dm);let m=this.inputs.slice(),f=[];for(let y of h)for(let b of p[y]){let v=b.outboundLayer;if(v!=null){for(let x of b.inputTensors)if(m.indexOf(x)===-1)throw new Aa(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${f}`);for(let x of b.outputTensors)m.push(x);f.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(y=>y.name);for(let y of g){let b=g.filter(v=>v===y).length;if(b!==1)throw new Aa(`The name "${y}" is used ${b} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Sm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new z("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new z(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,a++}let r=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[s]]);else if(t)throw new z(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new z(`${s.length} of ${a} weights are not set: ${s}`)}nx(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Mm}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=ux(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return D(()=>{e=bt(e);let n=new ji;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return dp(this.outputs,n,t)})}computeMask(e,t){return D(()=>{e=bt(e);let n;return t==null?n=Bi(null,e.length):n=bt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Nm(e);if(t.length!==this.inputLayers.length)throw new z(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(dm);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(c.id)!==-1)continue;let u=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],b=`${f.name}_${g}_${y}`,v=n[b];u.push(v)}let p=c.computeOutputShape(In(u)),d=Nm(p),h=c.inboundNodes.indexOf(l);for(let m=0;m<d.length;m++){let f=`${c.name}_${h}_${m}`;n[f]=d[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${c}`;s.push(u)}for(let i=0;i<s.length;i++){let o=s[i];tr(o in n),r.push(n[o])}return In(r)}runInternalGraph(e,t){t==null&&(t=Bi(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],c=e[o],u=t[o];n[l.id]=[c,u]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(dm);for(let o of a){let l=this.nodesByDepth[o];for(let c of l){let u=c.outboundLayer,p=c.inputTensors,d=c.outputTensors,h=new Array;for(let m of p)m.id in n&&h.push(n[m.id]);if(h.length===p.length){let m={},f,g,y,b;if(c.callArgs!=null&&(m=c.callArgs),h.length===1){let[v,x]=h[0];m.mask==null&&(m.mask=x),y=bt(u.call(v,m)),b=bt(u.computeMask(v,x)),f=[v],g=[x]}else f=h.map(v=>v[0]),g=h.map(v=>v[1]),m.mask==null&&(m.mask=g),y=bt(u.call(f,m)),b=bt(u.computeMask(f,g));if(u.activityRegularizer)throw new $e("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],N=y[v],T=b[v];n[x.id]=[N,T]}}}}let r=[],s=[],i=[];for(let o of this.outputs){tr(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,c]=n[o.id];i.push(l.shape),r.push(l),s.push(c)}return[r,s,i]}buildNodeConversionMap(e){let t={},n;for(let a of this.layers){n=a instanceof sr?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=sr.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new z(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new z("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new z(`No such layer: ${e}`)}calculateLosses(){return D(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let a=sr.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let u=0;u<s.inboundNodes.length;u++){let p=s.inboundNodes[u],d=sr.nodeKey(s,u),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let m=[];for(let f=0;f<p.inboundLayers.length;f++){let g=p.inboundLayers[f],y=p.nodeIndices[f],b=p.tensorIndices[f],v=sr.nodeKey(g,y),x=t[v];x==null&&(x=0),m.push([g.name,x,b,h])}l.push(m)}}}let c={};c.name=s.name,c.className=i,c.config=o,c.inboundNodes=l,n.push(c)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=sr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[s];a.push([i.name,c,u])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=sr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[s];r.push([i.name,c,u])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(f,g){f.name in s?s[f.name].push(g):s[f.name]=[g]}function o(f,g){let y=[],b;for(let v of g){let x=v[0],N=v[1],T=v[2];if(b=v[3]==null?{}:v[3],!(x in r)){i(f,g);return}let C=r[x];if(C.inboundNodes.length<=N){i(f,g);return}let F=C.inboundNodes[N];y.push(F.outputTensors[T])}y.length>0&&f.apply(In(y),b)}function l(f){let g=f.name,y=Da(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),r[g]=y,f.inboundNodes.forEach(b=>{if(!(b instanceof Array))throw new z(`Corrupted configuration, expected array for nodeData: ${b}`);i(y,b)})}let c=t.name,u=t.layers;for(let f of u)l(f);for(;!fz(s);)for(let f of u){let g=r[f.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let b of y)o(g,b)}}let p=[],d=[],h=t.inputLayers;for(let f of h){let g=f[0],y=f[1],b=f[2];tr(g in r);let v=r[g].inboundNodes[y].outputTensors;p.push(v[b])}let m=t.outputLayers;for(let f of m){let g=f[0],y=f[1],b=f[2];tr(g in r);let v=r[g].inboundNodes[y].outputTensors;d.push(v[b])}return new e({inputs:p,outputs:d,name:c})}get stateful(){if(this._stateful)throw new z("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){D(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function GW(e,t,n){let a=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(a===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==a)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${a} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(s=>{s in e?r.push(e[s]):r.push(null)}),r}else throw new Error(`The model has multiple (${a}) outputs, so ${n} must be either an array with ${a} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function pI(e,t){return GW(e,t,"classWeight")}async function dI(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=D(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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 r.data());Fe(r);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])}),nt(i,"float32")}else return null}function HW(e,t){return L(e,t)}var jW=32;function mI(e,t){let n,a,r=t;n=r.xs,a=r.ys,k.assert(n!=null&&a!=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=hI("input",e.inputNames,n),i=hI("output",e.outputNames,a),o=s[0].shape[0];k.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)})`),k.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)k.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++)k.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 hI(e,t,n){if(n instanceof Ae)return[n];if(Array.isArray(n))return k.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let a=[];for(let r of t){if(n[r]==null)throw new z(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function qW(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 XW(e,t,n){let a=n.batchesPerEpoch!=null;if(k.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k.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}`),k.assert(!a||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,s,i;if(r)if(fI(n.validationData))k.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=qW(n.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;r?c=l.slice().concat(l.map(g=>"val_"+g)):c=l.slice();let u=Qk(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=eI(u,p,n.epochs,null,null,KW(t,n),null,r,c);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let m=n.initialEpoch==null?0:n.initialEpoch,f=await t.iterator();for(;m<n.epochs;){let g={};await d.onEpochBegin(m);let y=0,b=0;for(a||(f=await t.iterator());a?y<n.batchesPerEpoch:!0;){let v=await f.next();if(a&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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instead`);if(s==="channelsFirst"&&(e=Ue(e,[0,2,1])),r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Fh(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=ar(o,n)),o})}function OI(e,t,n,a=[1,1],r="valid",s,i,o=null){return D(()=>{if(s==null&&(s=Ea()),Dt(s),e.rank!==3&&e.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Nx(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=rs.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Ue(l,[0,3,1,2])),l})}function g4(e,t,n,a=[1,1,1],r="valid",s,i){return D(()=>{if(s==null&&(s=Ea()),Dt(s),e.rank!==4&&e.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=PI(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=tb(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=ar(o,n)),s==="channelsFirst"&&(o=Ue(o,[0,4,1,2,3])),o})}var Tx=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Tx.verifyArgs(t),this.rank=e,Kt(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=uu(t.kernelSize,e,"kernelSize"),this.strides=uu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Qn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Dt(this.dataFormat),this.activation=cs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=wt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Vt(t.biasConstraint),this.biasRegularizer=kt(t.biasRegularizer),this.activityRegularizer=kt(t.activityRegularizer),this.dilationRate=uu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`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 z(`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 z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(tr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Rb(e.kernelSize,"number",1,3))throw new z(`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:us(this.activation),useBias:this.useBias,biasInitializer:Ct(this.biasInitializer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},fp=class extends Tx{constructor(e,t){super(e,t);this.kernel=null,fp.verifyArgs(t),this.filters=t.filters,Kt(this.filters,"filters"),this.kernelInitializer=wt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Vt(t.kernelConstraint),this.kernelRegularizer=kt(t.kernelRegularizer)}build(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,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 D(()=>{e=Me(e);let n,a=this.bias==null?null:this.bias.read(),r=kk(this.activation.getClassName());if(r!=null&&this.rank===2)n=OI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=f4(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=OI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=g4(e,this.kernel.read(),a,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=ht(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let s=Ra(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:Ct(this.kernelInitializer),kernelRegularizer:mt(this.kernelRegularizer),kernelConstraint:Wt(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 z(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},gp=class extends fp{constructor(e){super(2,e);gp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Rb(e.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};gp.className="Conv2D";re.registerClass(gp);var Om=class extends fp{constructor(e){super(3,e);Om.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 z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Om.className="Conv3D";re.registerClass(Om);var Sx=class extends gp{constructor(e){super(e);if(this.inputSpec=[new Xt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ht(e),e.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Xt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{let n=Me(e);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],c=this.kernelSize[0],u=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Pm(o,p,c,this.padding),m=Pm(l,d,u,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ue(n,[0,2,3,1]));let g=$h(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ue(g,[0,3,1,2])),this.bias!=null&&(g=ar(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ht(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=Pm(t[a],o,s,this.padding),t[r]=Pm(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Sx.className="Conv2DTranspose";re.registerClass(Sx);var LI=class extends fp{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=kt(t.depthwiseRegularizer),this.depthwiseConstraint=Vt(t.depthwiseConstraint),this.pointwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=kt(t.pointwiseRegularizer),this.pointwiseConstraint=Vt(t.pointwiseConstraint)}build(e){if(e=ht(e),e.length<this.rank+2)throw new z(`Inputs to SeparableConv${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 z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"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 Xt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{e=Me(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=Ue(e,[0,2,3,1])),n=Oi(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ar(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ue(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=Ct(this.depthwiseInitializer),e.pointwiseInitializer=Ct(this.pointwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.pointwiseRegularizer=mt(this.pointwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseConstraint),e.pointwiseConstraint=Wt(this.pointwiseConstraint),e}};LI.className="SeparableConv";var Cx=class extends LI{constructor(e){super(2,e)}};Cx.className="SeparableConv2D";re.registerClass(Cx);var Lm=class extends fp{constructor(e){super(1,e);Lm.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"&&!Rb(e.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Lm.className="Conv1D";re.registerClass(Lm);var _x=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 D(()=>{if(e=Me(e),this.dataFormat==="channelsLast"){let n=hm(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return hm(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=hm(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return hm(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}};_x.className="Cropping2D";re.registerClass(_x);var Ex=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,Dt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Oz(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 D(()=>{let n=Me(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ue(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s]);return Ue(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ex.className="UpSampling2D";re.registerClass(Ex);function y4(e,t,n=[1,1],a="valid",r,s){return D(()=>{r==null&&(r=Ea()),Dt(r);let i=Nx(e,r);if(e.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=es(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}var Ax=class extends Tx{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=Vt(e.depthwiseConstraint),this.depthwiseRegularizer=kt(e.depthwiseRegularizer)}build(e){if(e=ht(e),e.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,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 D(()=>{e=Me(e);let n=y4(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ar(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],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Ra(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ra(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ct(this.depthwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseRegularizer),e}};Ax.className="DepthwiseConv2D";re.registerClass(Ax);function zI(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function BI(e,t,n,a=!1,r,s,i=!1,o=!1){return D(()=>{let l=t.shape.length;if(l<3)throw new z(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Fa(2,l));if(t=Ue(t,c),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=$n(r,-1)),r=Ue(r,c)),a&&(t=Mn(t,0),r!=null&&(r=Mn(r,0)));let u=[],p,d=n,h=t.shape[0],m=dt(t),f;r!=null&&(f=dt(r));for(let y=0;y<h;++y){let b=m[y],v=D(()=>e(b,d));if(r==null)p=v[0],d=v[1];else{let x=D(()=>{let N=f[y],T=Rn(N).sub(N),C=v[0].mul(N).add(d[0].mul(T)),F=d.map(($,P)=>v[1][P].mul(N).add($.mul(T)));return{output:C,newStates:F}});p=x.output,d=x.newStates}o&&u.push(p)}let g;return o&&(g=$t(u,1)),[p,g,d]})}var rr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new zm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new z("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 Xt({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 Fa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ex(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return D(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}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.");ex(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new Xt({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new $e("Constants support is not implemented in RNN yet.");this.cell.build(r);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!k.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Xt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new Ir("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("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(a=>vt([n,a])):this.states_=[vt([n,this.cell.stateSize])];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>vt([n,a])):this.states_[0]=vt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`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()):Fe(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!k.arraysEqual(r.shape,i))throw new z(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>jt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=zI(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.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 Xt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof $a){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 D(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Me(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new z(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=BI((d,h)=>{let m=this.cell.call([d].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,a);let p=this.returnSequences?c:l;return this.returnState?[p].concat(u):p})}getInitialState(e){return D(()=>{let t=vt(e.shape);return t=Te(t,[1,2]),t=ip(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Bb(t,[1,n]):t):this.cell.stateSize>1?[Bb(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()===rr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Da(a,n);return new e(Object.assign(t,{cell:r}))}};rr.className="RNN";re.registerClass(rr);var up=class extends qe{},Bm=class extends up{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=cs(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=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=ru([1,os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ru([1,os([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 D(()=>{if(e=e,e.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ps({ones:()=>Rn(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ps({ones:()=>Rn(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=nr(L(e,s),this.kernel.read()):r=nr(e,this.kernel.read()),this.bias!=null&&(r=ar(r,this.bias.read())),i!=null&&(n=L(n,i));let o=Z(r,nr(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:us(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Bm.className="SimpleRNNCell";re.registerClass(Bm);var Fx=class extends rr{constructor(e){e.cell=new Bm(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};Fx.className="SimpleRNN";re.registerClass(Fx);var Wm=class extends up{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 z("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Kt(this.units,"units"),this.activation=cs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=cs(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=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=ru([1,os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ru([1,os([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 D(()=>{if(e=e,e.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ps({ones:()=>Rn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ps({ones:()=>Rn(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,r[0]));let c=nr(e,this.kernel.read());this.useBias&&(c=ar(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,s[0]));let u=this.recurrentKernel.read(),[p,d]=Pn(u,[2*this.units,this.units],u.rank-1),h=nr(a,p),[m,f,g]=Pn(c,3,c.rank-1),[y,b]=Pn(h,2,h.rank-1);i=this.recurrentActivation.apply(Z(m,y)),o=this.recurrentActivation.apply(Z(f,b));let v=nr(L(o,a),d);l=this.activation.apply(Z(g,v));let x=Z(L(i,a),L(Z(1,Tt(i)),l));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:us(this.activation),recurrentActivation:us(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Wm.className="GRUCell";re.registerClass(Wm);var $x=class extends rr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Wm(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};$x.className="GRU";re.registerClass($x);var yp=class extends up{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=cs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=cs(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=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=ru([1,os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ru([1,os([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 a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends ga{apply(i,o){let l=r.apply([s]),c=new fm().apply([s]),u=r.apply([s*2]);return $k($k(l,c),u)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ps({ones:()=>Rn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ps({ones:()=>Rn(a),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=L(e,s[0]));let p=nr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,i[0])),p=Z(p,nr(a,this.recurrentKernel.read())),this.useBias&&(p=ar(p,this.bias.read()));let[d,h,m,f]=Pn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(h),c=Z(L(l,r),L(o,this.activation.apply(m))),u=this.recurrentActivation.apply(f);let g=L(u,this.activation.apply(c));return[g,g,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:us(this.activation),recurrentActivation:us(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};yp.className="LSTMCell";re.registerClass(yp);var Dx=class extends rr{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 yp(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Dx.className="LSTM";re.registerClass(Dx);var zm=class extends up{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 D(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){ex(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Ui(`RNNCell_${a}`,()=>{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=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Da(r,n));return new e({cells:a})}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 tx(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}nx(t)}};zm.className="StackedRNNCells";re.registerClass(zm);function ps(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>Rk(t(),n),i=()=>lp(s,t,a);return!r||r<=1?jt(i().clone()):Array(r).fill(void 0).map(i).map(o=>jt(o.clone()))}var b4=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},WI=class extends rr{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 Xt({ndim:5})]}call(e,t){return D(()=>{if(this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new z("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}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 D(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=vt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new Ir("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new z("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(()=>vt(r)):this.states_=[vt(r)];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>vt(r)):this.states_[0]=vt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`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()):Fe(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!k.arraysEqual(i.shape,o))throw new z(`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=>jt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=Ra(l,a[0],r,s[0],i[0]),p=Ra(c,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,p]:[u,p,n]]}};WI.className="ConvRNN2D";var Vm=class extends yp{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Kt(this.filters,"filters"),this.kernelSize=uu(n,2,"kernelSize"),this.kernelSize.forEach(o=>Kt(o,"kernelSize")),this.strides=uu(a||1,2,"strides"),this.strides.forEach(o=>Kt(o,"strides")),this.padding=r||"valid",Qn(this.padding),this.dataFormat=s||"channelsLast",Dt(this.dataFormat),this.dilationRate=uu(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 z(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);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 ga{apply(u,p){let d=l.apply([c]),h=Ja([c]),m=l.apply([c*2]);return Vb([d,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return D(()=>{if(e.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ps({ones:()=>Rn(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(J,se,Q)=>!se||!se[Q]?J:L(se[Q],J),c=l(a,o,0),u=l(a,o,1),p=l(a,o,2),d=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ps({ones:()=>Rn(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[v,x,N,T]=Pn(this.kernel.read(),i,b),[C,F,$,P]=this.useBias?Pn(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,v,C,this.padding),u=this.inputConv(u,x,F,this.padding),p=this.inputConv(p,N,$,this.padding),d=this.inputConv(d,T,P,this.padding);let[B,W,G,q]=Pn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,B),f=this.recurrentConv(f,W),g=this.recurrentConv(g,G),y=this.recurrentConv(y,q);let j=this.recurrentActivation.apply(Z(c,m)),X=this.recurrentActivation.apply(Z(u,f)),K=Z(L(X,s),L(j,this.activation.apply(Z(p,g)))),ne=L(this.recurrentActivation.apply(Z(d,y)),this.activation.apply(K));return[ne,ne,K]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=b4(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=At(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ar(r,n,this.dataFormat):r}recurrentConv(e,t){return At(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Vm.className="ConvLSTM2DCell";re.registerClass(Vm);var Rx=class extends WI{constructor(e){let t=new Vm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Rx.className="ConvLSTM2D";re.registerClass(Rx);var Um=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 a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return lp(()=>Rk(n,this.rate,r,this.seed),()=>n,a)}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()}};Um.className="Dropout";re.registerClass(Um);var Mx=class extends Um{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Mx.className="SpatialDropout1D";re.registerClass(Mx);var Px=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,Kt(this.units,"units"),this.activation=cs(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=Vt(e.kernelConstraint),this.biasConstraint=Vt(e.biasConstraint),this.kernelRegularizer=kt(e.kernelRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.activityRegularizer=kt(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 D(()=>{this.invokeCallHook(e,t);let n=Me(e),a=kk(this.activation.getClassName()),r;return a!=null?r=nr(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=nr(n,this.kernel.read()),this.bias!=null&&(r=ar(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:us(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Px.className="Dense";re.registerClass(Px);var Ox=class extends qe{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 z(`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],is(e,1)]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=n.transpose(a)}return Uz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Ox.className="Flatten";re.registerClass(Ox);var Lx=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=cs(e.activation)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);return this.activation.apply(n)})}getConfig(){let e={activation:us(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Lx.className="Activation";re.registerClass(Lx);var zx=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 D(()=>(e=Me(e),Wz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};zx.className="RepeatVector";re.registerClass(zx);var Bx=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.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new z("Can only specifiy one unknown dimension.");else r*=l}let i=is(e);if(s!==null){if(r===0||i%r!=0)throw new z(n);a[s]=i/r}else if(i!==r)throw new z(n);return a}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 D(()=>{this.invokeCallHook(e,t);let n=Me(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return n.reshape(r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Bx.className="Reshape";re.registerClass(Bx);var Wx=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=Fa(1,e.dims.length+1);if(!k.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 Xt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ht(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Ue(Me(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Wx.className="Permute";re.registerClass(Wx);var Vx=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=Me(e),a=-1;return Wc(Pi(n,this.maskValue),a)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e),a=-1,r=!0,s=Wc(Pi(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};Vx.className="Masking";re.registerClass(Vx);var Ux=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(bt(e.inputLength))}this.inputDim=e.inputDim,Kt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Kt(this.outputDim,"outputDim"),this.embeddingsInitializer=wt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=kt(e.embeddingsRegularizer),this.activityRegularizer=kt(e.activityRegularizer),this.embeddingsConstraint=Vt(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 D(()=>this.maskZero?(e=Me(e),Pi(e,Ge(e))):null)}computeOutputShape(e){if(e=ht(e),this.inputLength==null)return[...e,this.outputDim];let t=bt(this.inputLength);if(t.length!==e.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a<t.length;++a){let r=t[a],s=e[a+1];if(r!=null&&s!=null&&r!==s)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Me(e);return n.dtype!=="int32"&&(n=sp(n,"int32")),Dk(this.embeddings.read(),n.as1D()).reshape(ht(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ct(this.embeddingsInitializer),embeddingsRegularizer:mt(this.embeddingsRegularizer),activityRegularizer:mt(this.activityRegularizer),embeddingsConstraint:Wt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Ux.className="Embedding";re.registerClass(Ux);var Ki=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 a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ht(e)]),e=e,e.length<2)throw new z(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=ss(t),t.length>1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&ss(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return D(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=os(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=ip(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],p=c.slice(1).concat([u]),d=o.reshape([u].concat(is(c.slice(1))));d=Ue(d,[1,0]),d=d.reshape(p),n.push(d),r=!0}else if(l>1){let c=Fa(1,l).concat([0]);n.push(Ue(o,c)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=Ue(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(Fa(0,i-1));s=Ue(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=ss(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return D(()=>{if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an Array");if(!Array.isArray(e))throw new z("`inputs` should be an Array");if(t.length!==e.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:$n(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=ma(n,t[a]);return n})}},Gx=class extends Ki{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Z(t,e[n]);return t})}};Gx.className="Add";re.registerClass(Gx);var Hx=class extends Ki{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};Hx.className="Multiply";re.registerClass(Hx);var jx=class extends Ki{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Z(t,e[n]);return L(1/e.length,t)})}};jx.className="Average";re.registerClass(jx);var qx=class extends Ki{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Za(t,e[n]);return t})}};qx.className="Maximum";re.registerClass(qx);var Kx=class extends Ki{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Zl(t,e[n]);return t})}};Kx.className="Minimum";re.registerClass(Kx);var Xx=class extends Ki{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 z("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let a of e)if(a!=null){t=!1;break}if(t)return;let n=[];for(let a=0;a<e.length;++a){let r=e[a].slice();r.splice(this.axis,1);let s=!1;for(let i of n)if(k.arraysEqual(i,r)){s=!0;break}s||n.push(r)}if(n.length>1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return D(()=>Vb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new z("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new z("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new z(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return D(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s<e.length;++s)t[s]==null?a.push(Rn(e[s]).asType("bool")):t[s].rank<e[s].rank?a.push($n(t[s],-1)):a.push(t[s]);let r=Qe(a,this.axis);return Eh(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Xx.className="Concatenate";re.registerClass(Xx);function bp(e,t){for(;e<0;)e+=t;return e}function x4(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(k.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k.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 a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return D(()=>{let i;if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(r>a){i=r-a;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;a>r?l=a+r-3:l=a-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 Yx=class extends Ki{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k.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 a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new z(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>bp(r,e[s].shape.length)):a=[bp(this.axes,t.shape.length),bp(this.axes,n.shape.length)],this.normalize&&(t=Cm(t,a[0]),n=Cm(n,a[1])),x4(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[bp(this.axes,e.length),bp(this.axes,t.length)],n}computeOutputShape(e){k.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 a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Yx.className="Dot";re.registerClass(Yx);var Zx=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 D(()=>{this.invokeCallHook(e,t);let n=Me(e);return lp(()=>mm(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Zx.className="GaussianNoise";re.registerClass(Zx);var Jx=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 D(()=>{this.invokeCallHook(e,t);let n=Me(e);return this.rate>0&&this.rate<1?lp(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(mm(n.shape,1,a))},()=>n,t.training||!1):n})}};Jx.className="GaussianDropout";re.registerClass(Jx);var Qx=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Me(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 D(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return lp(()=>{let a=Me(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=ns(Jl(n),this.rate);o=sp(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>Me(e),t.training||!1)}return e})}};Qx.className="AlphaDropout";re.registerClass(Qx);function xp(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=g1(e,t,n,a,r,s);else if(e.rank===3)i=y1(e,t,n,a,r,s);else if(e.rank===4)i=b1(e,t,n,a,r,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function v4(e,t,n,a,r=.001){return D(()=>{let s=Wh(e,a),i=s.mean,o=s.variance;return[xp(e,i,o,n,t,r),i,o]})}function w4(e,t,n,a,r=.001){return D(()=>{let s=Wh(e,a),i=s.mean,o=s.variance,l=[];for(let h of Fa(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let c=i.reshape(l),u=o.reshape(l),p=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[xp(e,c,u,d,p,r),i,o]})}function k4(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),Fa(0,e.rank-1))?v4(e,t,n,a,r):w4(e,t,n,a,r)}var ev=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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.movingMeanInitializer=wt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=wt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Vt(e.betaConstraint),this.gammaConstraint=Vt(e.gammaConstraint),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer)}build(e){e=ht(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new z(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Xt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training,a=Me(e),r=a.shape,s=r.length,i=Fa(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Bi(1,s);l[o]=r[o];let c=i.slice();c.sort();let u=!k.arraysEqual(c,Fa(0,s).slice(0,s-1)),p=()=>{if(u){let g=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),b=this.center?this.beta.read().reshape(l):null,v=this.scale?this.gamma.read().reshape(l):null;return xp(a,g,y,b,v,this.epsilon)}else return xp(a,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 p();let[d,h,m]=k4(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,b)=>{D(()=>{let v=1-b,x=g.read(),N=x.sub(y).mul(v);g.write(x.sub(N))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),movingMeanInitializer:Ct(this.movingMeanInitializer),movingVarianceInitializer:Ct(this.movingVarianceInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer),betaConstraint:Wt(this.betaConstraint),gammaConstraint:Wt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};ev.className="BatchNormalization";re.registerClass(ev);var tv=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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ht(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==ss(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Me(e),a=n.shape,r=a.length;return D(()=>{let s=!0,{mean:i,variance:o}=Wh(n,this.axis,s),l=Bi(1,r);for(let m of this.axis)l[m]=a[m];let c=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(l):m,u=c(this.gamma.read()),p=c(this.beta.read()),d=[],h=[];for(let m=0;m<r;++m)this.axis.indexOf(m)!==-1?(d.push(a[m]),h.push(1)):(d.push(1),h.push(a[m]));return i=i.tile(d),o=o.tile(d),u=u.tile(h),p=p.tile(h),xp(n,i,o,p,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};tv.className="LayerNormalization";re.registerClass(tv);function I4(e,t,n){return D(()=>{if(e.rank!==4)throw new z(`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 z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Ea()),n!=="channelsLast"&&n!=="channelsFirst")throw new z(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],Jn(e,a)})}var nv=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Ea():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new z(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new z(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new z(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){e=ht(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return D(()=>I4(Me(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};nv.className="ZeroPadding2D";re.registerClass(nv);function Gm(e,t,n,a,r,s){return D(()=>{Dt(r),Sk(s),Qn(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Ea()),s==null&&(s="max"),e=Nx(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Ft(e,t,n,o):i=Yn(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}function VI(e,t,n,a,r,s){return D(()=>{Dt(r),Sk(s),Qn(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Ea()),s==null&&(s="max"),e=PI(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=db(e,t,n,o):i=Jy(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,4,1,2,3])),i})}var UI=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 z(`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 z(`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,Qn(this.padding),this.inputSpec=[new Xt({ndim:3})]}computeOutputShape(e){e=ht(e);let t=Ra(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return D(()=>{this.invokeCallHook(e,t),e=ip(Me(e),2);let n=this.poolingFunction(Me(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return as(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},av=class extends UI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Qn(a),Gm(e,t,n,a,r,"max")}};av.className="MaxPooling1D";re.registerClass(av);var rv=class extends UI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Qn(a),Gm(e,t,n,a,r,"avg")}};rv.className="AveragePooling1D";re.registerClass(rv);var GI=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 z(`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];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Dt(this.dataFormat),Qn(this.padding),this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ra(t,this.poolSize[0],this.padding,this.strides[0]),n=Ra(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 D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(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}},sv=class extends GI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Qn(a),Gm(e,t,n,a,r,"max")}};sv.className="MaxPooling2D";re.registerClass(sv);var iv=class extends GI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Qn(a),Gm(e,t,n,a,r,"avg")}};iv.className="AveragePooling2D";re.registerClass(iv);var HI=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 z(`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,Dt(this.dataFormat),Qn(this.padding),this.inputSpec=[new Xt({ndim:5})]}computeOutputShape(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ra(t,this.poolSize[0],this.padding,this.strides[0]),n=Ra(n,this.poolSize[1],this.padding,this.strides[1]),a=Ra(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(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}},ov=class extends HI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Qn(a),VI(e,t,n,a,r,"max")}};ov.className="MaxPooling3D";re.registerClass(ov);var lv=class extends HI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Dt(r),Qn(a),VI(e,t,n,a,r,"avg")}};lv.className="AveragePooling3D";re.registerClass(lv);var jI=class extends qe{constructor(e){super(e);this.inputSpec=[new Xt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},uv=class extends jI{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Me(e);return St(n,1)})}};uv.className="GlobalAveragePooling1D";re.registerClass(uv);var cv=class extends jI{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Me(e);return Zn(n,1)})}};cv.className="GlobalMaxPooling1D";re.registerClass(cv);var qI=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Dt(this.dataFormat),this.inputSpec=[new Xt({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}},pv=class extends qI{call(e,t){return D(()=>{let n=Me(e);return this.dataFormat==="channelsLast"?St(n,[1,2]):St(n,[2,3])})}};pv.className="GlobalAveragePooling2D";re.registerClass(pv);var dv=class extends qI{call(e,t){return D(()=>{let n=Me(e);return 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e(s)}},hv=class extends KI{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ht(e),e.length<3)throw new z(`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=ht(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return D(()=>(e=Me(e),BI((n,a)=>[Me(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};hv.className="TimeDistributed";re.registerClass(hv);function N4(e){Vi(Pz,"BidirectionalMergeMode",e)}var T4="concat",mv=class extends KI{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Da(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Da(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?T4:e.mergeMode,N4(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 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i;return this.mergeMode==="concat"?i=Vb([a,r]):this.mergeMode==="sum"?i=Z(a,r):this.mergeMode==="ave"?i=L(.5,Z(a,r)):this.mergeMode==="mul"?i=L(a,r):this.mergeMode==null&&(i=[a,r]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Ui(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ui(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 a=this.forwardLayer.states.map(r=>null);return Array.isArray(n)?n.concat(a).concat(a):[n].concat(a).concat(a)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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a=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[Qy(I("x",e,t,n),a,r)]}case"BroadcastTo":return[Hc(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function vN(e,t,n,a){let r=((s,i,o)=>{switch(s.category){case"arithmetic":return D(()=>lV(s,i,o));case"basic_math":return D(()=>uV(s,i,o));case"control":return gV(s,i,o);case"convolution":return D(()=>yV(s,i,o));case"creation":return D(()=>bV(s,i,o));case"dynamic":return xV(s,i,o);case"evaluation":return D(()=>vV(s,i,o));case"image":return D(()=>NV(s,i,o));case"graph":return D(()=>wV(s,i,o));case"logical":return D(()=>TV(s,i,o));case"matrices":return D(()=>SV(s,i,o));case"normalization":return D(()=>CV(s,i,o));case"reduction":return D(()=>_V(s,i,o));case"slice_join":return D(()=>EV(s,i,o));case"spectral":return D(()=>AV(s,i,o));case"transformation":return D(()=>FV(s,i,o));case"hash_table":return IV(s,i,o,a);case"custom":let l=ZI(s.op);if(l&&l.customExecutor)return l.customExecutor(new oV(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. 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e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function IN(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>On(d)[0]),u=[];a!=null&&(u=a.map(d=>On(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((kN(d)||$V(d)||DV(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&c.indexOf(d.name)===-1&&u.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function RV(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(u=>On(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{a.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{a.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{a.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(p=>{!l.has(p.name)&&a.has(p.name)&&p.inputs.every(d=>l.has(d.name))&&s.push(p)})}return c}var MV=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],PV=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],OV=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function kN(e){return MV.indexOf(e.op)>=0}function $V(e){return PV.indexOf(e.op)>=0}function DV(e){return OV.indexOf(e.op)>=0}var Tv=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Tv(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=IN(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. 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c}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Tr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Tn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Tn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=On(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&k.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=On(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=On(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},LV=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},zV="?tfjs-format=file",BV="model.json",NN=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new LV}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Ht.browserHTTPRequest(e,this.loadOptions);else{let t=Ht.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Ht.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let a=Ht.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Tv(gN.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=gN.Instance.transformGraph(e.modelInitializer);this.initializer=new Tv(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Ht.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Ae)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function WV(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Yt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},dU=class extends Yt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Fe(e.value)}}},hU=class extends 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Yt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},ds;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ds||(ds={}));var iU=class extends Yt{constructor(e,t=ds.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function a(s){return s instanceof 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-`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},jm='"',wp=Symbol("out"),BN=Symbol("field"),qm=Symbol("quote"),Ev=Symbol("quoteafterquote"),WN=Symbol("quoteinquote"),_N=class extends cu{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new CN(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r<this.fullColumnNames.length;r++){let s=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let c=Number(o);if(isNaN(c))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=c;else switch(i.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(o);break;default:l=c}}i&&i.isLabel?a[s]=l:n[s]=l}}return Object.keys(a).length===0?n:{xs:n,ys:a}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],a=0,r=e.length,s=wp;for(let i=0;i<r;i++)switch(s){case wp:switch(e.charAt(i)){case jm:a=i+1,s=qm;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=wp;break;default:s=BN,a=i;break}break;case BN:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=wp,a=i+1;break;default:}break;case qm:switch(e.charAt(i)){case jm:s=Ev;break;default:}break;case Ev:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=wp,a=i+1;break;case jm:s=qm;break;default:s=WN;break}break;case WN:switch(e.charAt(i)){case jm:s=qm;break;default:}break;default:}if(s===Ev?n.push(e.substring(a,r-1)):n.push(e.substring(a)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},VN=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(ee().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new VN(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 a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[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(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&a({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(r),a({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((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),Kn(n,t)}},UN=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=nt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Sa([s,r,o,i],[1,4])}else this.cropBox=Sa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(ee().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 UN(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.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=_i.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return D(()=>{let t=Fn(ce(e,"float32"),0),n;n=Ja.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return j(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},GN=class{},HN=class extends Yt{split(e){return new xU(this,e)}},xU=class extends HN{constructor(e,t){super();this.upstream=e,this.impl=new vU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},vU=class extends _v{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},kU=class extends Yt{decodeUTF8(){return new wU(this)}},wU=class extends HN{constructor(e){super();this.upstream=e,this.impl=new IU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},IU=class extends _v{constructor(e){super();if(this.upstream=e,ee().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=HE();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return ee().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},jN=class extends kU{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(ee().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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function TU(e,t={}){let n,a;typeof e=="string"?n=e:(n=e.url,a=NU(e));let r=await k.fetch(n,a);if(r.ok){let s=new Uint8Array(await r.arrayBuffer());return new jN(s,t)}else throw new Error(r.statusText)}var NU=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function qN(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var EN=class extends GN{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(qN(this.input)&&ee().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new jN(this.input,this.options)}},AN=class extends GN{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return qN(this.url)?new EN(this.url,this.fileOptions).iterator():TU(this.url,this.fileOptions)}};function GV(e,t={}){return new _N(new AN(e),t)}function HV(e){let t=Cv(e);return Ln(async()=>t)}function jV(e){return Ln(async()=>{let t=await e();return Cv(()=>t.next())})}async function qV(e,t){return UN.create(e,t)}async function KV(e){return VN.create(e)}var FN="3.0.0";function we(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var SU=Qa.whereImpl,CU=class extends dc{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Fd(this,Jr())}write(e,t,n){this.firstUse&&(this.firstUse=!1,ee().get("IS_NODE")&&_.warn(`
+        ${a}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=Cv(s,n),o=a===0?0:e.size/a,l=D(()=>{let u=[];e=H(e,[1,a,o]);for(let p=0;p<t.length;++p){let d=p===0?0:r[p-1],h=[0,d,0],m=[1,t[p],o];u[p]=H(We(e,h,m),i)}return e.dispose(),u}),c=new wp([],n,e.dtype,t.length);for(let u=0;u<l.length;u++)c.setItem(u,l[u]);return c}var wV=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let a=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await s.data())[0]?n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let a=I("body",e,t,n),r=I("cond",e,t,n),s=I("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(u=>u.id),l=await i[0].data();i.forEach(u=>{!u.kept&&o.indexOf(u.id)===-1&&u.dispose()});let c=s;for(;l[0];){let u=c;c=await n.functionMap[a].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);let p=c.map(h=>h.id);u.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()});let d=await n.functionMap[r].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);l=await d[0].data(),d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()})}return c}case"LoopCond":{let a=I("pred",e,t,n);return[Cr(a)]}case"Switch":{let a=I("pred",e,t,n),r=I("data",e,t,n);return r.kept||(r=Cr(r)),(await a.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let a=e.inputNames.find(r=>Tn(r,t,n)!==void 0);if(a){let r=Tn(a,t,n);return[Cr(r)]}return}case"Enter":{let a=I("frameName",e,t,n),r=I("tensor",e,t,n);return n.enterFrame(a),[Cr(r)]}case"Exit":{let a=I("tensor",e,t,n);return n.exitFrame(),[Cr(a)]}case"NextIteration":{let a=I("tensor",e,t,n);return n.nextIteration(),[Cr(a)]}case"TensorArrayV3":{let a=I("size",e,t,n),r=I("dtype",e,t,n),s=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),l=I("identicalElementShapes",e,t,n),c=I("name",e,t,n),u=new gV(c,r,a,s,l,i,o);return n.addTensorArray(u),[u.idTensor,de(1)]}case"TensorArrayWriteV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(a.id).read(r)]}case"TensorArrayGatherV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(a.id).gather(r,s)]}case"TensorArrayScatterV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id),s=I("dtype",e,t,n);return[r.concat(s)]}case"TensorArraySplitV3":{let a=I("tensorArrayId",e,t,n),r=I("tensor",e,t,n),s=I("lengths",e,t,n),i=n.getTensorArray(a.id);return i.split(s,r),[i.idTensor]}case"TensorArraySizeV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return[de(r.size(),"int32")]}case"TensorArrayCloseV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let a=I("tensorListId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorList(a.id);return i.setItem(r,s),[i.idTensor]}case"TensorListGetItem":{let a=I("tensorListId",e,t,n),r=I("index",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).getItem(r,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let a=I("indices",e,t,n),r=I("tensor",e,t,n),s=I("elementShape",e,t,n),i=I("numElements",e,t,n),o=xV(r,a,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let a=I("elementShape",e,t,n),r=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=bV(a,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let a=I("tensorListId",e,t,n),r=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).gather(r,i,s)]}case"TensorListStack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(a.id).stack(r,s,i)]}case"TensorListFromTensor":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=yV(a,r,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let a=I("tensorListId",e,t,n),r=n.getTensorList(a.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[r.concat(s,i)]}case"TensorListPushBack":{let a=I("tensorListId",e,t,n),r=I("tensor",e,t,n),s=n.getTensorList(a.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let 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a=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[nb(I("x",e,t,n),a,r)]}case"BroadcastTo":return[Hc(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function SN(e,t,n,a){let r=((s,i,o)=>{switch(s.category){case"arithmetic":return D(()=>mV(s,i,o));case"basic_math":return D(()=>fV(s,i,o));case"control":return wV(s,i,o);case"convolution":return D(()=>kV(s,i,o));case"creation":return D(()=>IV(s,i,o));case"dynamic":return NV(s,i,o);case"evaluation":return D(()=>TV(s,i,o));case"image":return D(()=>EV(s,i,o));case"graph":return D(()=>SV(s,i,o));case"logical":return D(()=>AV(s,i,o));case"matrices":return D(()=>FV(s,i,o));case"normalization":return D(()=>$V(s,i,o));case"reduction":return D(()=>DV(s,i,o));case"slice_join":return D(()=>RV(s,i,o));case"spectral":return D(()=>MV(s,i,o));case"transformation":return D(()=>PV(s,i,o));case"hash_table":return _V(s,i,o,a);case"custom":let l=nN(s.op);if(l&&l.customExecutor)return l.customExecutor(new hV(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return k.isPromise(r)?r.then(s=>[].concat(s)):[].concat(r)}var CN=class{constructor(e={},t={},n={},a={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function EN(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>Ln(d)[0]),u=[];a!=null&&(u=a.map(d=>Ln(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((_N(d)||OV(d)||LV(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&c.indexOf(d.name)===-1&&u.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function zV(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(u=>Ln(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{a.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{a.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{a.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(p=>{!l.has(p.name)&&a.has(p.name)&&p.inputs.every(d=>l.has(d.name))&&s.push(p)})}return c}var BV=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],WV=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],VV=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function _N(e){return BV.indexOf(e.op)>=0}function OV(e){return WV.indexOf(e.op)>=0}function LV(e){return VV.indexOf(e.op)>=0}var Ev=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Ev(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=EN(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. 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You can use model.execute() instead.");let y=o.filter(b=>!_N(b)&&!Tn(b.name,h,t)).map(b=>b.name);if(y.length>0){let b="";throw u!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${c}]. ${b}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let p="";if(u.node.op==="Enter"&&I("isConstant",u.node,a,n)&&([p]=Sr(u.node.name,n)),a[u.node.name]==null){let d=SN(u.node,a,n,this._resourceManager);p||([p]=Sr(u.node.name,n));let h=n.currentContext;k.isPromise(d)?c.push(d.then(m=>(a[p]=m,n.currentContext=h,this.checkTensorForDisposal(p,u.node,a,n,s,i,o),this.processChildNodes(u.node,t,n,a,r,l),m))):(a[p]=d,this.checkTensorForDisposal(p,u.node,a,n,s,i,o),this.processChildNodes(u.node,t,n,a,r,l))}else this.processChildNodes(u.node,t,n,a,r,l)}return c}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Sr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Tn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Tn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Ln(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&k.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Ln(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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 '${t}' is not found in the graph`)})}},UV=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},GV="?tfjs-format=file",HV="model.json",AN=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new UV}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Ht.browserHTTPRequest(e,this.loadOptions);else{let t=Ht.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Ht.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let a=Ht.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Ev(wN.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=wN.Instance.transformGraph(e.modelInitializer);this.initializer=new Ev(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Ht.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Ae)&&!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,a)=>(t[n]=e[a],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 jV(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},KN=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(ee().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new KN(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 a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[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(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&a({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(r),a({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((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),Kn(n,t)}},XN=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=nt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Ca([s,r,o,i],[1,4])}else this.cropBox=Ca([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(ee().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 XN(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.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=Ai.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return D(()=>{let t=$n(ce(e,"float32"),0),n;n=Qa.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return H(n,a.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.")}},YN=class{},ZN=class extends Yt{split(e){return new NU(this,e)}},NU=class extends ZN{constructor(e,t){super();this.upstream=e,this.impl=new TU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},TU=class extends $v{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${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}},CU=class extends Yt{decodeUTF8(){return new SU(this)}},SU=class extends ZN{constructor(e){super();this.upstream=e,this.impl=new _U(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},_U=class extends $v{constructor(e){super();if(this.upstream=e,ee().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=ZE();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return ee().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},JN=class extends CU{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(ee().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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof 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YN{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return QN(this.url)?new MN(this.url,this.fileOptions).iterator():AU(this.url,this.fileOptions)}};function XV(e,t={}){return new RN(new PN(e),t)}function YV(e){let t=Fv(e);return zn(async()=>t)}function ZV(e){return zn(async()=>{let t=await e();return Fv(()=>t.next())})}async function JV(e,t){return XN.create(e,t)}async function QV(e){return KN.create(e)}var ON="3.1.0";function we(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var FU=er.whereImpl,Rv=class extends pc{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Rd(this,qa())}nextDataId(){return Rv.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,ee().get("IS_NODE")&&_.warn(`
 ============================
 Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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xG={kernelName:ii,backendName:"cpu",kernelFunc:IT},NT=it(oi,e=>Math.max(0,e)),vG={kernelName:oi,backendName:"cpu",kernelFunc:NT},TT=it(ui,e=>Math.min(Math.max(0,e),6)),wG={kernelName:ui,backendName:"cpu",kernelFunc:TT};function Lv(e,t,n,a,r){if(n==="linear")return sr({inputs:{x:t},backend:e});if(n==="relu")return NT({inputs:{x:t},backend:e});if(n==="elu")return wT({inputs:{x:t},backend:e});if(n==="relu6")return TT({inputs:{x:t},backend:e});if(n==="prelu")return IT({inputs:{x:t,alpha:a},backend:e});if(n==="leakyrelu")return kT({inputs:{x:t},backend:e,attrs:{alpha:r}});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function It(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=k.sizeFromShape(r.shape),o=k.inferFromImplicitShape(s,i),l=k.sizeFromShape(o);k.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${r.shape}) has ${i} elements. 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r=a.map(o=>n.data.get(o.dataId).values),s=Le(a[0].shape,a[0].dtype),i=s.values;for(let o=0;o<a.length;o++){let l=r[o];for(let c=0;c<i.length;c++)i[c]+=l[c]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var FG={kernelName:$s,backendName:"cpu",kernelFunc:AG};function $G(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;we(r,"all");let o=k.parseAxisParam(s,r.shape),l=o,c=_.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=ba({inputs:{x:r},backend:n,attrs:{perm:c}}),l=_.getInnerMostAxes(l.length,r.shape.length)),_.assertAxesAreInnerMostDims("all",l,u.shape.length);let[p,d]=_.computeOutAndReduceShapes(u.shape,l),h=k.sizeFromShape(d),m=k.makeZerosTypedArray(k.sizeFromShape(p),u.dtype),f=n.data.get(u.dataId).values;for(let y=0;y<m.length;++y){let b=y*h,v=f[b];for(let x=0;x<h;++x){let N=f[b+x];v=v&&N}m[y]=v}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(p,u.dtype,m);if(i){let 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i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=ba({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],_.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[u,p]=_.computeOutAndReduceShapes(l.shape,i),d=k.sizeFromShape(u),h=k.makeZerosTypedArray(d,"int32"),m=k.sizeFromShape(p),f=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*m,b=f[y],v=0;for(let x=0;x<m;++x){let N=f[y+x];N<b&&(b=N,v=x)}h[g]=v}return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var zG={kernelName:fc,backendName:"cpu",kernelFunc:LG},BG=it(Bo,e=>Math.asin(e)),WG={kernelName:Bo,backendName:"cpu",kernelFunc:BG},VG=it(Wo,e=>Math.asinh(e)),UG={kernelName:Wo,backendName:"cpu",kernelFunc:VG},GG=it(Vo,e=>Math.atan(e)),HG={kernelName:Vo,backendName:"cpu",kernelFunc:GG},jG=Rt((e,t)=>Math.atan2(e,t)),qG=Zt(Go,jG),KG={kernelName:Go,backendName:"cpu",kernelFunc:qG},XG=it(Uo,e=>Math.atanh(e)),YG={kernelName:Uo,backendName:"cpu",kernelFunc:XG};function zv(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,c=r.dilationWidth,u=r.effectiveFilterHeight,p=r.effectiveFilterWidth,d=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=Le(r.outShape,n),g=f.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],b=r.outShape[2]*r.outShape[3],v=r.outShape[3];for(let x=0;x<r.batchSize;++x){let N=x*y,T=x*a[0];for(let C=0;C<r.inChannels;++C)for(let $=0;$<r.outHeight;++$){let 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n.makeTensorInfo(r.shape,r.dtype,f)}var oH={kernelName:js,backendName:"cpu",kernelFunc:iH};function lH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;we([r],"batchToSpaceND");let o=s.reduce((y,b)=>y*b),l=_.getReshaped(r.shape,s,o),c=_.getPermuted(l.length,s.length),u=_.getReshapedPermuted(r.shape,s,o),p=_.getSliceBeginCoords(i,s.length),d=_.getSliceSize(u,i,s.length),h=It({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ba({inputs:{x:h},backend:n,attrs:{perm:c}}),f=It({inputs:{x:m},backend:n,attrs:{shape:u}}),g=Ki({inputs:{x:f},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var uH={kernelName:yc,backendName:"cpu",kernelFunc:lH};function cH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,c=Av(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var pH={kernelName:zd,backendName:"cpu",kernelFunc:cH},dH=it(Ur,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),hH={kernelName:Ur,backendName:"cpu",kernelFunc:dH},mH=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.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],p=l[c];a[c]=Math.hypot(u,p)}return n.makeOutput(a,t.shape,"float32")},fH={kernelName:bc,backendName:"cpu",kernelFunc:mH};function mu(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.imag,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var gH={kernelName:Qd,backendName:"cpu",kernelFunc:mu};function fu(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=_.computeOutShape(t.map(f=>f.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>k.sizeFromShape(f.shape)>0);if(o.length===1)return sr({inputs:{x:o[0]},backend:n});let l=o.map(f=>f.shape);if(_.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(x=>qi({inputs:{input:x},backend:n})),g=o.map(x=>mu({inputs:{input:x},backend:n})),y=fu({inputs:f,backend:n,attrs:{axis:s}}),b=fu({inputs:g,backend:n,attrs:{axis:s}}),v=zn({inputs:{real:y,imag:b},backend:n});return f.forEach(x=>n.disposeIntermediateTensorInfo(x)),g.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),v}let c=o.map(f=>{let g=k.sizeFromShape(f.shape.slice(s));return It({inputs:{x:f},backend:n,attrs:{shape:[-1,g]}})}),u=c.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=_.computeOutShape(c.map(f=>f.shape),1);let p=c[0].shape[0]===1,d=Fv(u,i,t[0].dtype,p),h=_.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,d);return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var yH={kernelName:jo,backendName:"cpu",kernelFunc:fu};function ET(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=a;we([r,s],"conv2d");let p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!1,p),h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.padInfo.left,b=d.padInfo.top,v=d.dataFormat==="channelsLast",x=new Ot(d.outShape,r.dtype),N=k.computeStrides(r.shape),T=k.computeStrides(s.shape),C=N[0],$=v?N[1]:N[2],F=v?N[2]:1,O=v?1:N[1],B=x.strides[0],W=v?x.strides[1]:x.strides[2],G=v?x.strides[2]:1,q=v?1:x.strides[1],H=n.data.get(r.dataId).values,K=n.data.get(s.dataId).values,X=x.values;for(let ne=0;ne<d.batchSize;++ne){let J=ne*C,se=ne*B;for(let Q=0;Q<d.outHeight;++Q){let oe=se+Q*W,ae=Q*d.strideHeight-b;for(let pe=0;pe<h;++pe){let ue=ae+pe*f;if(ue<0||ue>=d.inHeight)continue;let ge=pe*T[0],ye=J+ue*$;for(let ke=0;ke<d.outWidth;++ke){let Se=oe+ke*G,Ce=ke*d.strideWidth-y;for(let Re=0;Re<m;++Re){let Ke=Ce+Re*g;if(Ke<0||Ke>=d.inWidth)continue;let Ve=ge+Re*T[1],st=ye+Ke*F,lt=Ve;for(let He=0;He<d.inChannels;++He){let ct=H[st+He*O];for(let pt=0;pt<d.outChannels;++pt)X[Se+pt*q]+=ct*K[lt+pt];lt+=d.outChannels}}}}}}return n.makeTensorInfo(x.shape,x.dtype,X)}var bH={kernelName:Os,backendName:"cpu",kernelFunc:ET};function xH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=a;we([r,s],"conv2dBackpropFilter");let p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,u,i,1,o,c,!1,p),{strideHeight:h,strideWidth:m,filterHeight:f,filterWidth:g}=d,y=d.dataFormat==="channelsLast",b=new Ot(d.filterShape,"float32"),v=d.padInfo.left,x=d.padInfo.top,N=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,C=new Ot(r.shape,r.dtype,N),$=new Ot(s.shape,s.dtype,T);for(let F=0;F<f;++F){let O=Math.max(0,Math.ceil((x-F)/h)),B=Math.min(d.outHeight,(d.inHeight+x-F)/h);for(let W=0;W<g;++W){let G=Math.max(0,Math.ceil((v-W)/m)),q=Math.min(d.outWidth,(d.inWidth+v-W)/m);for(let H=0;H<d.inChannels;++H)for(let K=0;K<d.outChannels;++K){let X=0;for(let ne=0;ne<d.batchSize;++ne)for(let J=O;J<B;++J){let se=F+J*h-x;for(let Q=G;Q<q;++Q){let oe=W+Q*m-v;y?X+=C.get(ne,se,oe,H)*$.get(ne,J,Q,K):X+=C.get(ne,H,se,oe)*$.get(ne,K,J,Q)}}b.set(X,F,W,H,K)}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var vH={kernelName:Wd,backendName:"cpu",kernelFunc:xH};function wH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=a;we([r,s],"conv2dBackpropInput");let p=k.computeStrides(s.shape),d=k.computeStrides(r.shape),h=_.convertConv2DDataFormat(c),m=_.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),f=new Ot(m.inShape,"float32"),g=f.values,y=n.data.get(r.dataId).values,b=n.data.get(s.dataId).values,[v,x,N]=p,{batchSize:T,filterHeight:C,filterWidth:$,inChannels:F,inHeight:O,inWidth:B,outChannels:W,outHeight:G,outWidth:q,strideHeight:H,strideWidth:K}=m;h=m.dataFormat;let X=C-1-m.padInfo.top,ne=$-1-m.padInfo.left,J=h==="channelsLast",se=f.strides[0],Q=J?f.strides[1]:f.strides[2],oe=J?f.strides[2]:1,ae=J?1:f.strides[1],pe=d[0],ue=J?d[1]:d[2],ge=J?d[2]:1,ye=J?1:d[1];for(let ke=0;ke<T;++ke)for(let Se=0;Se<F;++Se)for(let Ce=0;Ce<O;++Ce){let Re=Ce-X,Ke=Math.max(0,Math.ceil(Re/H)),Ve=Math.min(G,(C+Re)/H);for(let st=0;st<B;++st){let lt=st-ne,He=Math.max(0,Math.ceil(lt/K)),ct=Math.min(q,($+lt)/K),pt=0;for(let Je=Ke;Je<Ve;++Je){let _n=Je*H-Re;for(let en=He;en<ct;++en){let En=en*K-lt,ra=pe*ke+ue*Je+ge*en,vn=v*(C-1-_n)+x*($-1-En)+N*Se;for(let un=0;un<W;++un){let sa=y[ra+ye*un],Wa=b[vn+un];pt+=sa*Wa}}}let Hn=se*ke+Q*Ce+oe*st+ae*Se;g[Hn]=pt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var kH={kernelName:Ls,backendName:"cpu",kernelFunc:wH};function IH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;we([r,s],"conv3d");let c=_.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=c,y=g.front,b=g.left,v=g.top,x=new Ot(c.outShape,r.dtype),N=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,C=x.values,$=k.computeStrides(r.shape),F=k.computeStrides(s.shape);for(let O=0;O<c.batchSize;++O){let B=O*$[0],W=O*x.strides[0];for(let G=0;G<c.outDepth;++G){let q=W+G*x.strides[1],H=G*c.strideDepth-y;for(let K=0;K<u;++K){let X=H+K*h;if(X<0||X>=c.inDepth)continue;let ne=K*F[0],J=B+X*$[1];for(let se=0;se<c.outHeight;++se){let Q=q+se*x.strides[2],oe=se*c.strideHeight-v;for(let ae=0;ae<p;++ae){let pe=oe+ae*m;if(pe<0||pe>=c.inHeight)continue;let ue=ne+ae*F[1],ge=J+pe*$[2];for(let ye=0;ye<c.outWidth;++ye){let ke=Q+ye*c.outChannels,Se=ye*c.strideWidth-b;for(let Ce=0;Ce<d;++Ce){let Re=Se+Ce*f;if(Re<0||Re>=c.inWidth)continue;let Ke=ue+Ce*F[2],Ve=ge+Re*c.inChannels,st=Ke;for(let lt=0;lt<c.inChannels;++lt){let He=N[Ve+lt];for(let ct=0;ct<c.outChannels;++ct)C[ke+ct]+=He*T[st+ct];st+=c.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var NH={kernelName:xc,backendName:"cpu",kernelFunc:IH};function TH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a;we([r,s],"conv3dBackpropFilterV2");let c=k.computeStrides(r.shape),u=k.computeStrides(s.shape),p=_.computeConv3DInfo(r.shape,l,i,1,o),d=p.strideDepth,h=p.strideHeight,m=p.strideWidth,f=p.filterDepth,g=p.filterHeight,y=p.filterWidth,b=new Ot(p.filterShape,"float32"),v=b.values,[x,N,T,C]=b.strides,$=n.data.get(s.dataId).values,[F,O,B,W]=u,G=n.data.get(r.dataId).values,[q,H,K,X]=c,ne=p.padInfo.front,J=p.padInfo.left,se=p.padInfo.top;for(let Q=0;Q<f;++Q){let oe=Math.max(0,Math.ceil((ne-Q)/d)),ae=Math.min(p.outDepth,(p.inDepth+ne-Q)/d),pe=Q*x;for(let ue=0;ue<g;++ue){let ge=Math.max(0,Math.ceil((se-ue)/h)),ye=Math.min(p.outHeight,(p.inHeight+se-ue)/h),ke=ue*N+pe;for(let Se=0;Se<y;++Se){let Ce=Math.max(0,Math.ceil((J-Se)/m)),Re=Math.min(p.outWidth,(p.inWidth+J-Se)/m),Ke=Se*T+ke;for(let Ve=0;Ve<p.inChannels;++Ve){let st=Ve*C+Ke;for(let lt=0;lt<p.outChannels;++lt){let He=0;for(let ct=0;ct<p.batchSize;++ct){let pt=ct*q,Hn=ct*F;for(let Je=oe;Je<ae;++Je){let _n=(Q+Je*d-ne)*H+pt,en=Je*O+Hn;for(let En=ge;En<ye;++En){let ra=(ue+En*h-se)*K+_n,vn=En*B+en;for(let un=Ce;un<Re;++un){let sa=(Se+un*m-J)*X+ra,Wa=un*W+vn;He+=G[sa+Ve]*$[Wa+lt]}}}}v[st+lt]=He}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var SH={kernelName:Vd,backendName:"cpu",kernelFunc:TH};function CH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a;we([r],"conv3dBackpropInputV2");let c=k.computeStrides(r.shape),u=k.computeStrides(s.shape),p=_.computeConv3DInfo(l,s.shape,o,1,i),d=new Ot(p.inShape,"float32"),h=d.values,[m,f,g,y]=d.strides,b=n.data.get(r.dataId).values,[v,x,N,T]=c,C=n.data.get(s.dataId).values,[$,F,O,B]=u,{batchSize:W,filterDepth:G,filterHeight:q,filterWidth:H,inChannels:K,inDepth:X,inHeight:ne,inWidth:J,outChannels:se,outDepth:Q,outHeight:oe,outWidth:ae,strideDepth:pe,strideHeight:ue,strideWidth:ge}=p,ye=G-1-p.padInfo.front,ke=q-1-p.padInfo.top,Se=H-1-p.padInfo.left;for(let Ce=0;Ce<W;++Ce)for(let Re=0;Re<K;++Re)for(let Ke=0;Ke<X;++Ke){let Ve=Ke-ye,st=Math.max(0,Math.ceil(Ve/pe)),lt=Math.min(Q,(G+Ve)/pe);for(let He=0;He<ne;++He){let ct=He-ke,pt=Math.max(0,Math.ceil(ct/ue)),Hn=Math.min(oe,(q+ct)/ue);for(let Je=0;Je<J;++Je){let _n=Je-Se,en=Math.max(0,Math.ceil(_n/ge)),En=Math.min(ae,(H+_n)/ge),ra=0;for(let vn=st;vn<lt;++vn){let un=vn*pe-Ve;for(let sa=pt;sa<Hn;++sa){let Wa=sa*ue-ct;for(let An=en;An<En;++An){let wo=An*ge-_n,Hu=v*Ce+x*vn+N*sa+T*An,ka=$*(G-1-un)+F*(q-1-Wa)+O*(H-1-wo)+B*Re;for(let ia=0;ia<se;++ia){let Ia=b[Hu+ia],ko=C[ka+ia];ra+=Ia*ko}}}}h[m*Ce+f*Ke+g*He+y*Je+Re]=ra}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var _H={kernelName:Ud,backendName:"cpu",kernelFunc:CH},EH=it(zs,e=>Math.cos(e)),AH={kernelName:zs,backendName:"cpu",kernelFunc:EH},FH=it(qo,e=>Math.cosh(e)),$H={kernelName:qo,backendName:"cpu",kernelFunc:FH};function DH(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=a,[u,p,d,h]=r.shape,m=s.shape[0],[f,g]=o,y=Le([m,f,g,h],"float32"),b=n.data.get(s.dataId).values,v=n.data.get(i.dataId).values,x=n.data.get(r.dataId).values,N=k.computeStrides(r.shape),T=k.computeStrides(y.shape);for(let C=0;C<m;C++){let $=C*4,F=b[$],O=b[$+1],B=b[$+2],W=b[$+3],G=v[C];if(G>=u)continue;let q=f>1?(B-F)*(p-1)/(f-1):0,H=g>1?(W-O)*(d-1)/(g-1):0;for(let K=0;K<f;K++){let X=f>1?F*(p-1)+K*q:.5*(F+B)*(p-1);if(X<0||X>p-1){for(let ne=0;ne<g;ne++)for(let J=0;J<h;J++){let se=J+ne*T[2]+K*T[1]+C*T[0];y.values[se]=c}continue}if(l==="bilinear"){let ne=Math.floor(X),J=Math.ceil(X),se=X-ne;for(let Q=0;Q<g;Q++){let oe=g>1?O*(d-1)+Q*H:.5*(O+W)*(d-1);if(oe<0||oe>d-1){for(let ge=0;ge<h;ge++){let ye=ge+Q*T[2]+K*T[1]+C*T[0];y.values[ye]=c}continue}let ae=Math.floor(oe),pe=Math.ceil(oe),ue=oe-ae;for(let ge=0;ge<h;ge++){let ye=ge+ae*N[2]+ne*N[1]+G*N[0],ke=x[ye];ye=ge+pe*N[2]+ne*N[1]+G*N[0];let Se=x[ye];ye=ge+ae*N[2]+J*N[1]+G*N[0];let Ce=x[ye];ye=ge+pe*N[2]+J*N[1]+G*N[0];let Re=x[ye],Ke=ke+(Se-ke)*ue,Ve=Ce+(Re-Ce)*ue;ye=ge+Q*T[2]+K*T[1]+C*T[0],y.values[ye]=Ke+(Ve-Ke)*se}}}else for(let ne=0;ne<g;++ne){let J=g>1?O*(d-1)+ne*H:.5*(O+W)*(d-1);if(J<0||J>d-1){for(let oe=0;oe<h;oe++){let ae=oe+ne*T[2]+K*T[1]+C*T[0];y.values[ae]=c}continue}let se=Math.round(J),Q=Math.round(X);for(let oe=0;oe<h;oe++){let ae=oe+se*N[2]+Q*N[1]+G*N[0],pe=oe+ne*T[2]+K*T[1]+C*T[0];y.values[pe]=x[ae]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var RH={kernelName:Ko,backendName:"cpu",kernelFunc:DH};function MH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;we(r,"cumsum");let l=_.getAxesPermutation([s],r.shape.length),c=r;l!=null&&(c=ba({inputs:{x:r},backend:n,attrs:{perm:l}}));let u=_.getInnerMostAxes(1,r.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let p=ca(c.dtype,"int32"),d=k.makeZerosTypedArray(k.sizeFromShape(c.shape),p),h=n.data.get(c.dataId).values,m=c.shape[c.shape.length-1],f=o?(y,b)=>y+m-b-1:(y,b)=>y+b;for(let y=0;y<h.length;y+=m)for(let b=0;b<m;b++){let v=f(y,b);if(b===0)d[v]=i?0:h[v];else{let x=f(y,b-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(c.shape,p,d);if(l!=null){let y=_.getUndoAxesPermutation(l),b=ba({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(c),b}return g}var PH={kernelName:Bs,backendName:"cpu",kernelFunc:MH};function OH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.data.get(r.dataId).values,c=n.data.get(s.dataId).values,u=Av(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=ZN(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var LH={kernelName:Gd,backendName:"cpu",kernelFunc:OH};function zH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=r.shape[1],c=r.shape[2],u=r.shape[3],p=l*s,d=c*s,h=u/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*p*d*h),g=0;for(let y=0;y<o;++y)for(let b=0;b<p;++b){let v=Math.floor(b/s),x=b%s;for(let N=0;N<d;++N){let T=Math.floor(N/s),C=N%s,$=(x*s+C)*h;for(let F=0;F<h;++F){let O=F+$+u*(T+c*(v+l*y));f[g++]=m[O]}}}return n.makeTensorInfo([o,p,d,h],r.dtype,f)}var BH={kernelName:Xo,backendName:"cpu",kernelFunc:zH};function AT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=a;we([r,s],"depthwiseConv2DNative");let u=k.computeStrides(r.shape),p=k.computeStrides(s.shape),d=l;d==null&&(d=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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VH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=a;we([r,s],"depthwiseConv2dNativeBackpropFilter");let p=_.computeConv2DInfo(r.shape,u,i,o,l,c,!0),{strideHeight:d,strideWidth:h,filterHeight:m,filterWidth:f}=p,g=new Ot(p.filterShape,"float32"),y=p.padInfo.left,b=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,N=new Ot(r.shape,r.dtype,x),T=n.data.get(s.dataId).values,C=new Ot(s.shape,s.dtype,T);for(let $=0;$<m;++$){let F=Math.max(0,Math.ceil((b-$)/d)),O=Math.min(p.outHeight,(p.inHeight+b-$)/d);for(let B=0;B<f;++B){let W=Math.max(0,Math.ceil((y-B)/h)),G=Math.min(p.outWidth,(p.inWidth+y-B)/h);for(let q=0;q<p.outChannels;++q){let H=Math.trunc(q/v),K=q%v,X=0;for(let ne=0;ne<p.batchSize;++ne)for(let J=F;J<O;++J){let se=$+J*d-b;for(let Q=W;Q<G;++Q){let oe=B+Q*h-y;X+=N.get(ne,se,oe,H)*C.get(ne,J,Q,q)}}g.set(X,$,B,H,K)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var UH={kernelName:Hd,backendName:"cpu",kernelFunc:VH};function GH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=a;we([r,s],"depthwiseConv2DNativeBackpropInput");let p=k.computeStrides(r.shape),d=k.computeStrides(s.shape),h=_.computeConv2DInfo(u,s.shape,i,o,l,c,!0),m=new Ot(h.inShape,"float32"),f=m.values,[g,y,b]=m.strides,v=n.data.get(r.dataId).values,[x,N,T]=p,C=n.data.get(s.dataId).values,[$,F,O]=d,{batchSize:B,filterHeight:W,filterWidth:G,inChannels:q,inHeight:H,inWidth:K,outChannels:X,outHeight:ne,outWidth:J,strideHeight:se,strideWidth:Q}=h,oe=W-1-h.padInfo.top,ae=G-1-h.padInfo.left,pe=X/q;for(let ue=0;ue<B;++ue)for(let ge=0;ge<q;++ge)for(let ye=0;ye<H;++ye){let ke=ye-oe,Se=Math.max(0,Math.ceil(ke/se)),Ce=Math.min(ne,(W+ke)/se);for(let Re=0;Re<K;++Re){let Ke=Re-ae,Ve=Math.max(0,Math.ceil(Ke/Q)),st=Math.min(J,(G+Ke)/Q),lt=0;for(let He=Se;He<Ce;++He){let ct=He*se-ke;for(let pt=Ve;pt<st;++pt){let 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O=k.toNestedArray(F,c.data.get(s.dataId).values),B=k.makeZerosNestedTypedArray(a.shape,a.dtype);for(let W=0;W<d;++W)for(let G=0;G<g;++G){let q=G*v-b.top;for(let H=0;H<y;++H){let K=H*x-b.left;for(let X=0;X<f;++X){let ne=Number.MIN_SAFE_INTEGER,J=q<0?0:q,se=K<0?0:K;for(let Q=0;Q<N;++Q){let oe=q+Q*C;if(oe>=0&&oe<h)for(let ae=0;ae<T;++ae){let pe=K+ae*$;if(pe>=0&&pe<m){let ue=u[W][oe][pe][X]+p[Q][ae][X];ue>ne&&(ne=ue,J=oe,se=pe)}}}B[W][J][se][X]+=O[W][G][H][X]}}}return{dataId:c.write(k.toTypedArray(B,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function ZH(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;we([a,r],"eluGrad");let s=new Float32Array(k.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l<i.length;++l){let c=i[l];c>=1?s[l]=o[l]:s[l]=o[l]*(c+1)}return n.makeTensorInfo(r.shape,"float32",s)}var 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s=_.mergeRealAndImagArrays(e,t),i=n/2,o=_.complexWithEvenIndex(s),l=o.real,c=o.imag,u=[l.length],p=r.makeTensorInfo(u,"float32",l),d=r.makeTensorInfo(u,"float32",c),h=zn({inputs:{real:p,imag:d},backend:r}),m=_.complexWithOddIndex(s),f=m.real,g=m.imag,y=[f.length],b=r.makeTensorInfo(y,"float32",f),v=r.makeTensorInfo(y,"float32",g),x=zn({inputs:{real:b,imag:v},backend:r}),N=Vv(l,c,i,a,r),T=N.real,C=N.imag,$=[T.length],F=r.makeTensorInfo($,"float32",T),O=r.makeTensorInfo($,"float32",C),B=zn({inputs:{real:F,imag:O},backend:r}),W=Vv(f,g,i,a,r),G=W.real,q=W.imag,H=[G.length],K=r.makeTensorInfo(H,"float32",G),X=r.makeTensorInfo(H,"float32",q),ne=zn({inputs:{real:K,imag:X},backend:r}),J=_.exponents(n,a),se=[J.real.length],Q=r.makeTensorInfo(se,"float32",J.real),oe=r.makeTensorInfo(se,"float32",J.imag),ae=zn({inputs:{real:Q,imag:oe},backend:r}),pe=Pv({inputs:{a:ae,b:ne},backend:r}),ue=kp({inputs:{a:B,b:pe},backend:r}),ge=Ov({inputs:{a:B,b:pe},backend:r}),ye=qi({inputs:{input:ue},backend:r}),ke=qi({inputs:{input:ge},backend:r}),Se=mu({inputs:{input:ue},backend:r}),Ce=mu({inputs:{input:ge},backend:r}),Re=fu({inputs:[ye,ke],backend:r,attrs:{axis:0}}),Ke=fu({inputs:[Se,Ce],backend:r,attrs:{axis:0}}),Ve=r.data.get(Re.dataId).values,st=r.data.get(Ke.dataId).values;return 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b6={kernelName:tl,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,r=n,s=k.getTypedArrayFromDType(a.dtype,k.sizeFromShape(a.shape)),[i,o,l,c]=a.shape,u=r.data.get(a.dataId).values;for(let p=0;p<i;p++){let d=p*l*o*c;for(let h=0;h<o;h++){let m=h*(l*c);for(let f=0;f<l;f++){let g=f*c;for(let y=0;y<c;y++){let b=[i,h,f,y][2],v=Math.round(l-b),x=d+m+g+y,N=u[x];if(v>=0&&v<l){let T=v*c,C=d+m+T+y;N=u[C]}s[x]=N}}}}return{dataId:r.write(s,a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},x6=Rt((e,t)=>Math.floor(e/t)),v6=Zt(Hs,x6,null,"int32"),w6={kernelName:Hs,backendName:"cpu",kernelFunc:v6};function k6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=ET({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d}});if(i){let g=f;f=kp({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=Lv(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var I6={kernelName:Ii,backendName:"cpu",kernelFunc:k6};function N6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=AT({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d}});if(i){let g=f;f=kp({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=Lv(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var T6={kernelName:Ni,backendName:"cpu",kernelFunc:N6};function S6(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=k.sizeFromShape(a.shape),i=r.shape,o=i[i.length-1],[l,c,u,p]=_.prepareAndValidate(a,r);if(c===0)return n.makeTensorInfo(l,a.dtype,[]);let 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n.makeTensorInfo(i.shape,r.dtype,g)}var sj={kernelName:th,backendName:"cpu",kernelFunc:rj};function DT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=n,l=r.shape,c=l.length,u=k.parseAxisParam(s,l),p=u,d=_.getAxesPermutation(p,c),h=o.data.get(r.dataId).values;if(d!=null){let x=new Array(c);for(let N=0;N<x.length;N++)x[N]=l[d[N]];h=Dv(h,l,r.dtype,d,x),p=_.getInnerMostAxes(p.length,c),l=x}we(r,"max"),_.assertAxesAreInnerMostDims("max",p,c);let[m,f]=_.computeOutAndReduceShapes(l,p),g=k.sizeFromShape(f),y=oT(h,g,m,r.dtype),b=o.write(y,m,r.dtype),v=m;return i&&(v=_.expandShapeToKeepDim(m,u)),{dataId:b,shape:v,dtype:r.dtype}}var ij={kernelName:Ys,backendName:"cpu",kernelFunc:DT};function oj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;we(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. 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u=_.computePool3DInfo(s.shape,i,o,1,l,c),p=n.bufferSync(s),d=ZG(p,u),h=u.strideDepth,m=u.strideHeight,f=u.strideWidth,g=u.dilationDepth,y=u.dilationHeight,b=u.dilationWidth,v=u.effectiveFilterDepth,x=u.effectiveFilterHeight,N=u.effectiveFilterWidth,T=v-1-u.padInfo.front,C=N-1-u.padInfo.left,$=x-1-u.padInfo.top,F=Le(s.shape,"float32"),O=n.bufferSync(r);for(let B=0;B<u.batchSize;++B)for(let W=0;W<u.inChannels;++W)for(let G=0;G<u.inDepth;++G)for(let q=0;q<u.inHeight;++q)for(let H=0;H<u.inWidth;++H){let K=G-T,X=q-$,ne=H-C,J=0;for(let se=0;se<v;se+=g){let Q=(K+se)/h;if(!(Q<0||Q>=u.outDepth||Math.floor(Q)!==Q))for(let oe=0;oe<x;oe+=y){let ae=(X+oe)/m;if(!(ae<0||ae>=u.outHeight||Math.floor(ae)!==ae))for(let pe=0;pe<N;pe+=b){let ue=(ne+pe)/f;if(ue<0||ue>=u.outWidth||Math.floor(ue)!==ue)continue;let ge=v*x*N-1-d.get(B,Q,ae,ue,W),ye=se*x*N+oe*N+pe,ke=ge===ye?1:0;ke!==0&&(J+=O.get(B,Q,ae,ue,W)*ke)}}}F.set(J,B,G,q,H,W)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var dj={kernelName:ah,backendName:"cpu",kernelFunc:pj};function hj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;we([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:p}=a,d=_.computePool2DInfo(o.shape,l,c,1,u,p),h=n.data.get(o.dataId).values,m=Le(d.outShape,o.dtype,CT(h,o.shape,o.dtype,d).values),f=d.strideHeight,g=d.strideWidth,y=d.dilationHeight,b=d.dilationWidth,v=d.effectiveFilterHeight,x=d.effectiveFilterWidth,N=x-1-d.padInfo.left,T=v-1-d.padInfo.top,C=Le(o.shape,"float32"),$=n.data.get(r.dataId).values,F=Le(r.shape,"float32",$);for(let O=0;O<d.batchSize;++O)for(let B=0;B<d.inChannels;++B)for(let W=0;W<d.inHeight;++W)for(let G=0;G<d.inWidth;++G){let q=W-T,H=G-N,K=0;for(let X=0;X<v;X+=y){let ne=(q+X)/f;if(!(ne<0||ne>=d.outHeight||Math.floor(ne)!==ne))for(let J=0;J<x;J+=b){let se=(H+J)/g;if(se<0||se>=d.outWidth||Math.floor(se)!==se)continue;let Q=v*x-1-m.get(O,ne,se,B),oe=X*x+J,ae=Q===oe?1:0;ae!==0&&(K+=F.get(O,ne,se,B)*ae)}}C.set(K,O,W,G,B)}return n.makeTensorInfo(C.shape,C.dtype,C.values)}var mj={kernelName:nh,backendName:"cpu",kernelFunc:hj};function fj(e,t,n,a,r){let s=k.computeStrides(t),i=zv(e,t,n,s,r,"max"),o=CT(e,t,n,r,!0,a);return[i.values,o.values]}var gj={kernelName:rh,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;we(a,"MaxPoolWithArgmax");let c=l.data.get(a.dataId).values,u=_.computePool2DInfo(a.shape,r,s,[1,1],i),[p,d]=fj(c,a.shape,a.dtype,o,u),h=l.write(p,u.outShape,a.dtype),m=l.write(d,u.outShape,a.dtype);return[{dataId:h,shape:u.outShape,dtype:a.dtype},{dataId:m,shape:u.outShape,dtype:"int32"}]}};function Zm(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;we(r,"sum");let o;r.dtype==="bool"?o=hs({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=sr({inputs:{x:r},backend:n});let 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d=hs({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});u.push(d);let h=Bv({inputs:{a:d,b:p},backend:n});u.push(h);let m=Zm({inputs:{x:h},backend:n,attrs:{axis:s,keepDims:i}});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var xj={kernelName:Qs,backendName:"cpu",kernelFunc:bj};function vj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;we(r,"min");let o=k.parseAxisParam(s,r.shape),l=o,c=_.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=ba({inputs:{x:r},backend:n,attrs:{perm:c}}),l=_.getInnerMostAxes(l.length,r.shape.length)),_.assertAxesAreInnerMostDims("min",l,u.shape.length);let[p,d]=_.computeOutAndReduceShapes(u.shape,l),h=k.sizeFromShape(d),m=k.makeZerosTypedArray(k.sizeFromShape(p),u.dtype),f=n.data.get(u.dataId).values;for(let y=0;y<m.length;++y){let b=y*h,v=f[b];for(let x=0;x<h;++x){let N=f[b+x];N<v&&(v=N)}m[y]=v}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(p,u.dtype,m);if(i){let y=_.expandShapeToKeepDim(p,o),b=It({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),b}return g}var wj={kernelName:ei,backendName:"cpu",kernelFunc:vj};function kj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,mode:i}=a;we(r,"mirrorPad");let o=s.map((b,v)=>b[0]+r.shape[v]+b[1]),l=s.map(b=>b[0]),c=s.map((b,v)=>b[0]+r.shape[v]),u=i==="reflect"?0:1,p=n.data.get(r.dataId).values,d=r.shape.length,h=k.computeStrides(r.shape),m=k.sizeFromShape(o),f=o.length,g=k.computeStrides(o),y=k.getTypedArrayFromDType(r.dtype,m);for(let b=0;b<m;b++){let v=k.indexToLoc(b,f,g);for(let N=0;N<f;N++)v[N]<l[N]?v[N]=l[N]*2-v[N]-u:v[N]>=c[N]&&(v[N]=(c[N]-1)*2-v[N]+u);v=v.map((N,T)=>N-l[T]);let x=k.locToIndex(v,d,h);y[b]=p[x]}return{dataId:n.write(y,o,r.dtype),shape:o,dtype:r.dtype}}var Ij={kernelName:Sc,backendName:"cpu",kernelFunc:kj},Nj=Rt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),Tj=Zt(hl,Nj),Sj={kernelName:hl,backendName:"cpu",kernelFunc:Tj},Cj=Ro(Ad());function RT(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=r.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. 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l=o?r:RT({inputs:{logits:r},backend:n,attrs:{dim:-1}}),c=l.shape[0],u=l.shape[1],p=n.data.get(l.dataId).values,d=[c,s],h=k.makeZerosTypedArray(k.sizeFromShape(d),"int32");for(let m=0;m<c;++m){let f=m*u,g=new Float32Array(u-1);g[0]=p[f];for(let v=1;v<g.length;++v)g[v]=g[v-1]+p[f+v];let y=Cj.alea(i.toString()),b=m*s;for(let v=0;v<s;++v){let x=y();h[b+v]=g.length;for(let N=0;N<g.length;N++)if(x<g[N]){h[b+v]=N;break}}}return o||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(d,"int32",h)}var Aj={kernelName:sh,backendName:"cpu",kernelFunc:Ej},Fj=Qa.nonMaxSuppressionV3Impl;function $j(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a;we(r,"NonMaxSuppression");let c=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,{selectedIndices:p}=Fj(c,u,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var Dj={kernelName:gl,backendName:"cpu",kernelFunc:$j},Rj=Qa.nonMaxSuppressionV4Impl;function Mj(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=a;we(r,"NonMaxSuppressionPadded");let u=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,{selectedIndices:d,validOutputs:h}=Rj(u,p,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Pj={kernelName:yl,backendName:"cpu",kernelFunc:Mj},Oj=Qa.nonMaxSuppressionV5Impl;function Lj(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=a;we(r,"NonMaxSuppressionWithScore");let u=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,d=i,h=o,m=l,f=c,{selectedIndices:g,selectedScores:y}=Oj(u,p,d,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var zj={kernelName:bl,backendName:"cpu",kernelFunc:Lj};function Bj(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a;we(r,"oneHot");let l=k.sizeFromShape(r.shape),c=new Float32Array(l*s);c.fill(o);let u=n.data.get(r.dataId).values;for(let p=0;p<l;++p)u[p]>=0&&u[p]<s&&(c[p*s+u[p]]=i);return n.makeTensorInfo([...r.shape,s],"int32",c)}var Wj={kernelName:ai,backendName:"cpu",kernelFunc:Bj};function Jm(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(a.dtype==="complex64"){let r=qi({inputs:{input:a},backend:n}),s=Jm({inputs:{x:r},backend:n}),i=mu({inputs:{input:a},backend:n}),o=Jm({inputs:{x:i},backend:n}),l=zn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Uv({backend:n,attrs:{shape:a.shape,value:0,dtype:a.dtype}})}var Vj={kernelName:Pl,backendName:"cpu",kernelFunc:Jm};function 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pe=ae*l-y,ue=pe;for(;ue<0;)ue+=p;let ge=Math.min(r.inWidth,m+pe),ye=oe+ae*F,ke=b,Se=0,Ce=0;for(let Ke=j;Ke<X;Ke+=c){let Ve=B+Ke*a[1];for(let st=se;st<Q;st+=u){let lt=Ve+st*a[2];for(let He=ue;He<ge;He+=p){let ct=lt+He*a[3],pt=e[ct+W];if(s==="max"&&pt>ke?ke=pt:s==="avg"&&(Se+=pt,Ce++),isNaN(ke))break}if(isNaN(ke))break}if(isNaN(ke))break}let Re=ye+W;x[Re]=s==="avg"?Se/Ce:ke}}}}return v}function tH(e,t){let n=Le(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f<t.batchSize;++f)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let b=y*a-d,v=b;for(;v<0;)v+=i;let x=Math.min(t.inDepth,c+b);for(let N=0;N<t.outHeight;++N){let T=N*r-h,C=T;for(;C<0;)C+=o;let F=Math.min(t.inHeight,u+T);for(let $=0;$<t.outWidth;++$){let P=$*s-m,B=P;for(;B<0;)B+=l;let 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u=_.computePool3DInfo(s.shape,i,o,1,l,c),p=u.strideDepth,d=u.strideHeight,h=u.strideWidth,m=u.filterDepth,f=u.filterHeight,g=u.filterWidth,y=u.dilationDepth,b=u.dilationHeight,v=u.dilationWidth,x=u.effectiveFilterDepth,N=u.effectiveFilterHeight,T=u.effectiveFilterWidth,C=x-1-u.padInfo.front,F=T-1-u.padInfo.left,$=N-1-u.padInfo.top,P=Le(s.shape,"float32"),B=1/(m*f*g),W=n.bufferSync(r);for(let G=0;G<u.batchSize;++G)for(let q=0;q<u.inChannels;++q)for(let j=0;j<u.inDepth;++j)for(let X=0;X<u.inHeight;++X)for(let K=0;K<u.inWidth;++K){let ne=j-C,J=X-$,se=K-F,Q=0;for(let oe=0;oe<x;oe+=y){let ae=(ne+oe)/p;if(!(ae<0||ae>=u.outDepth||Math.floor(ae)!==ae))for(let pe=0;pe<N;pe+=b){let ue=(J+pe)/d;if(!(ue<0||ue>=u.outHeight||Math.floor(ue)!==ue))for(let ge=0;ge<T;ge+=v){let ye=(se+ge)/h;ye<0||ye>=u.outWidth||Math.floor(ye)!==ye||(Q+=W.get(G,ae,ue,ye,q))}}}P.set(Q*B,G,j,X,K,q)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var oH={kernelName:Wd,backendName:"cpu",kernelFunc:iH};function 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n.makeTensorInfo(r.shape,r.dtype,f)}var pH={kernelName:qs,backendName:"cpu",kernelFunc:cH};function dH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;we([r],"batchToSpaceND");let o=s.reduce((y,b)=>y*b),l=_.getReshaped(r.shape,s,o),c=_.getPermuted(l.length,s.length),u=_.getReshapedPermuted(r.shape,s,o),p=_.getSliceBeginCoords(i,s.length),d=_.getSliceSize(u,i,s.length),h=It({inputs:{x:r},backend:n,attrs:{shape:l}}),m=xa({inputs:{x:h},backend:n,attrs:{perm:c}}),f=It({inputs:{x:m},backend:n,attrs:{shape:u}}),g=Yi({inputs:{x:f},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var hH={kernelName:gc,backendName:"cpu",kernelFunc:dH};function mH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,c=Mv(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var fH={kernelName:Vd,backendName:"cpu",kernelFunc:mH},gH=it(Gr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),yH={kernelName:Gr,backendName:"cpu",kernelFunc:gH},bH=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.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],p=l[c];a[c]=Math.hypot(u,p)}return n.makeOutput(a,t.shape,"float32")},xH={kernelName:yc,backendName:"cpu",kernelFunc:bH};function mu(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.imag,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var vH={kernelName:nh,backendName:"cpu",kernelFunc:mu};function fu(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=_.computeOutShape(t.map(f=>f.shape),s);if(k.sizeFromShape(i)===0)return 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c.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var wH={kernelName:qo,backendName:"cpu",kernelFunc:fu};function MT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=a;we([r,s],"conv2d");let p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!1,p),h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.padInfo.left,b=d.padInfo.top,v=d.dataFormat==="channelsLast",x=new Ot(d.outShape,r.dtype),N=k.computeStrides(r.shape),T=k.computeStrides(s.shape),C=N[0],F=v?N[1]:N[2],$=v?N[2]:1,P=v?1:N[1],B=x.strides[0],W=v?x.strides[1]:x.strides[2],G=v?x.strides[2]:1,q=v?1:x.strides[1],j=n.data.get(r.dataId).values,X=n.data.get(s.dataId).values,K=x.values;for(let ne=0;ne<d.batchSize;++ne){let J=ne*C,se=ne*B;for(let Q=0;Q<d.outHeight;++Q){let oe=se+Q*W,ae=Q*d.strideHeight-b;for(let pe=0;pe<h;++pe){let ue=ae+pe*f;if(ue<0||ue>=d.inHeight)continue;let ge=pe*T[0],ye=J+ue*F;for(let 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SH={kernelName:zs,backendName:"cpu",kernelFunc:TH};function CH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;we([r,s],"conv3d");let c=_.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=c,y=g.front,b=g.left,v=g.top,x=new Ot(c.outShape,r.dtype),N=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,C=x.values,F=k.computeStrides(r.shape),$=k.computeStrides(s.shape);for(let P=0;P<c.batchSize;++P){let B=P*F[0],W=P*x.strides[0];for(let G=0;G<c.outDepth;++G){let q=W+G*x.strides[1],j=G*c.strideDepth-y;for(let X=0;X<u;++X){let K=j+X*h;if(K<0||K>=c.inDepth)continue;let ne=X*$[0],J=B+K*F[1];for(let se=0;se<c.outHeight;++se){let Q=q+se*x.strides[2],oe=se*c.strideHeight-v;for(let ae=0;ae<p;++ae){let pe=oe+ae*m;if(pe<0||pe>=c.inHeight)continue;let ue=ne+ae*$[1],ge=J+pe*F[2];for(let ye=0;ye<c.outWidth;++ye){let ke=Q+ye*c.outChannels,Se=ye*c.strideWidth-b;for(let Ce=0;Ce<d;++Ce){let Re=Se+Ce*f;if(Re<0||Re>=c.inWidth)continue;let Ke=ue+Ce*$[2],Ve=ge+Re*c.inChannels,st=Ke;for(let lt=0;lt<c.inChannels;++lt){let He=N[Ve+lt];for(let ct=0;ct<c.outChannels;++ct)C[ke+ct]+=He*T[st+ct];st+=c.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var _H={kernelName:bc,backendName:"cpu",kernelFunc:CH};function EH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a;we([r,s],"conv3dBackpropFilterV2");let c=k.computeStrides(r.shape),u=k.computeStrides(s.shape),p=_.computeConv3DInfo(r.shape,l,i,1,o),d=p.strideDepth,h=p.strideHeight,m=p.strideWidth,f=p.filterDepth,g=p.filterHeight,y=p.filterWidth,b=new Ot(p.filterShape,"float32"),v=b.values,[x,N,T,C]=b.strides,F=n.data.get(s.dataId).values,[$,P,B,W]=u,G=n.data.get(r.dataId).values,[q,j,X,K]=c,ne=p.padInfo.front,J=p.padInfo.left,se=p.padInfo.top;for(let Q=0;Q<f;++Q){let oe=Math.max(0,Math.ceil((ne-Q)/d)),ae=Math.min(p.outDepth,(p.inDepth+ne-Q)/d),pe=Q*x;for(let ue=0;ue<g;++ue){let ge=Math.max(0,Math.ceil((se-ue)/h)),ye=Math.min(p.outHeight,(p.inHeight+se-ue)/h),ke=ue*N+pe;for(let Se=0;Se<y;++Se){let Ce=Math.max(0,Math.ceil((J-Se)/m)),Re=Math.min(p.outWidth,(p.inWidth+J-Se)/m),Ke=Se*T+ke;for(let Ve=0;Ve<p.inChannels;++Ve){let st=Ve*C+Ke;for(let lt=0;lt<p.outChannels;++lt){let He=0;for(let ct=0;ct<p.batchSize;++ct){let pt=ct*q,Hn=ct*$;for(let Je=oe;Je<ae;++Je){let En=(Q+Je*d-ne)*j+pt,en=Je*P+Hn;for(let An=ge;An<ye;++An){let ra=(ue+An*h-se)*X+En,vn=An*B+en;for(let un=Ce;un<Re;++un){let sa=(Se+un*m-J)*K+ra,Va=un*W+vn;He+=G[sa+Ve]*F[Va+lt]}}}}v[st+lt]=He}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var AH={kernelName:Hd,backendName:"cpu",kernelFunc:EH};function FH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a;we([r],"conv3dBackpropInputV2");let c=k.computeStrides(r.shape),u=k.computeStrides(s.shape),p=_.computeConv3DInfo(l,s.shape,o,1,i),d=new Ot(p.inShape,"float32"),h=d.values,[m,f,g,y]=d.strides,b=n.data.get(r.dataId).values,[v,x,N,T]=c,C=n.data.get(s.dataId).values,[F,$,P,B]=u,{batchSize:W,filterDepth:G,filterHeight:q,filterWidth:j,inChannels:X,inDepth:K,inHeight:ne,inWidth:J,outChannels:se,outDepth:Q,outHeight:oe,outWidth:ae,strideDepth:pe,strideHeight:ue,strideWidth:ge}=p,ye=G-1-p.padInfo.front,ke=q-1-p.padInfo.top,Se=j-1-p.padInfo.left;for(let Ce=0;Ce<W;++Ce)for(let Re=0;Re<X;++Re)for(let Ke=0;Ke<K;++Ke){let Ve=Ke-ye,st=Math.max(0,Math.ceil(Ve/pe)),lt=Math.min(Q,(G+Ve)/pe);for(let He=0;He<ne;++He){let ct=He-ke,pt=Math.max(0,Math.ceil(ct/ue)),Hn=Math.min(oe,(q+ct)/ue);for(let Je=0;Je<J;++Je){let En=Je-Se,en=Math.max(0,Math.ceil(En/ge)),An=Math.min(ae,(j+En)/ge),ra=0;for(let vn=st;vn<lt;++vn){let un=vn*pe-Ve;for(let sa=pt;sa<Hn;++sa){let Va=sa*ue-ct;for(let Fn=en;Fn<An;++Fn){let Io=Fn*ge-En,Hu=v*Ce+x*vn+N*sa+T*Fn,Ia=F*(G-1-un)+$*(q-1-Va)+P*(j-1-Io)+B*Re;for(let ia=0;ia<se;++ia){let Na=b[Hu+ia],No=C[Ia+ia];ra+=Na*No}}}}h[m*Ce+f*Ke+g*He+y*Je+Re]=ra}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var $H={kernelName:jd,backendName:"cpu",kernelFunc:FH},DH=it(Bs,e=>Math.cos(e)),RH={kernelName:Bs,backendName:"cpu",kernelFunc:DH},MH=it(Ko,e=>Math.cosh(e)),PH={kernelName:Ko,backendName:"cpu",kernelFunc:MH};function OH(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=a,[u,p,d,h]=r.shape,m=s.shape[0],[f,g]=o,y=Le([m,f,g,h],"float32"),b=n.data.get(s.dataId).values,v=n.data.get(i.dataId).values,x=n.data.get(r.dataId).values,N=k.computeStrides(r.shape),T=k.computeStrides(y.shape);for(let C=0;C<m;C++){let F=C*4,$=b[F],P=b[F+1],B=b[F+2],W=b[F+3],G=v[C];if(G>=u)continue;let q=f>1?(B-$)*(p-1)/(f-1):0,j=g>1?(W-P)*(d-1)/(g-1):0;for(let X=0;X<f;X++){let K=f>1?$*(p-1)+X*q:.5*($+B)*(p-1);if(K<0||K>p-1){for(let ne=0;ne<g;ne++)for(let J=0;J<h;J++){let se=J+ne*T[2]+X*T[1]+C*T[0];y.values[se]=c}continue}if(l==="bilinear"){let ne=Math.floor(K),J=Math.ceil(K),se=K-ne;for(let Q=0;Q<g;Q++){let oe=g>1?P*(d-1)+Q*j:.5*(P+W)*(d-1);if(oe<0||oe>d-1){for(let ge=0;ge<h;ge++){let ye=ge+Q*T[2]+X*T[1]+C*T[0];y.values[ye]=c}continue}let ae=Math.floor(oe),pe=Math.ceil(oe),ue=oe-ae;for(let ge=0;ge<h;ge++){let ye=ge+ae*N[2]+ne*N[1]+G*N[0],ke=x[ye];ye=ge+pe*N[2]+ne*N[1]+G*N[0];let Se=x[ye];ye=ge+ae*N[2]+J*N[1]+G*N[0];let Ce=x[ye];ye=ge+pe*N[2]+J*N[1]+G*N[0];let Re=x[ye],Ke=ke+(Se-ke)*ue,Ve=Ce+(Re-Ce)*ue;ye=ge+Q*T[2]+X*T[1]+C*T[0],y.values[ye]=Ke+(Ve-Ke)*se}}}else for(let ne=0;ne<g;++ne){let J=g>1?P*(d-1)+ne*j:.5*(P+W)*(d-1);if(J<0||J>d-1){for(let oe=0;oe<h;oe++){let ae=oe+ne*T[2]+X*T[1]+C*T[0];y.values[ae]=c}continue}let se=Math.round(J),Q=Math.round(K);for(let oe=0;oe<h;oe++){let ae=oe+se*N[2]+Q*N[1]+G*N[0],pe=oe+ne*T[2]+X*T[1]+C*T[0];y.values[pe]=x[ae]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var LH={kernelName:Xo,backendName:"cpu",kernelFunc:OH};function zH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;we(r,"cumsum");let l=_.getAxesPermutation([s],r.shape.length),c=r;l!=null&&(c=xa({inputs:{x:r},backend:n,attrs:{perm:l}}));let u=_.getInnerMostAxes(1,r.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let p=pa(c.dtype,"int32"),d=k.makeZerosTypedArray(k.sizeFromShape(c.shape),p),h=n.data.get(c.dataId).values,m=c.shape[c.shape.length-1],f=o?(y,b)=>y+m-b-1:(y,b)=>y+b;for(let y=0;y<h.length;y+=m)for(let b=0;b<m;b++){let v=f(y,b);if(b===0)d[v]=i?0:h[v];else{let x=f(y,b-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(c.shape,p,d);if(l!=null){let y=_.getUndoAxesPermutation(l),b=xa({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(c),b}return g}var BH={kernelName:Ws,backendName:"cpu",kernelFunc:zH};function WH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.data.get(r.dataId).values,c=n.data.get(s.dataId).values,u=Mv(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=aT(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var VH={kernelName:qd,backendName:"cpu",kernelFunc:WH};function UH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=r.shape[1],c=r.shape[2],u=r.shape[3],p=l*s,d=c*s,h=u/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*p*d*h),g=0;for(let y=0;y<o;++y)for(let b=0;b<p;++b){let v=Math.floor(b/s),x=b%s;for(let N=0;N<d;++N){let T=Math.floor(N/s),C=N%s,F=(x*s+C)*h;for(let $=0;$<h;++$){let P=$+F+u*(T+c*(v+l*y));f[g++]=m[P]}}}return n.makeTensorInfo([o,p,d,h],r.dtype,f)}var GH={kernelName:Yo,backendName:"cpu",kernelFunc:UH};function PT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=a;we([r,s],"depthwiseConv2DNative");let u=k.computeStrides(r.shape),p=k.computeStrides(s.shape),d=l;d==null&&(d=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=_.computeConv2DInfo(r.shape,s.shape,i,d,o,c,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:b}=h,v=b.left,x=b.top,N=h.outChannels/h.inChannels,T=new Ot(h.outShape,r.dtype),C=n.data.get(r.dataId).values,F=n.data.get(s.dataId).values,$=T.values;for(let P=0;P<h.batchSize;++P){let B=P*u[0],W=P*T.strides[0];for(let G=0;G<h.outHeight;++G){let q=W+G*T.strides[1],j=G*h.strideHeight-v;for(let X=0;X<m;++X){let K=j+X*g;if(K<0||K>=h.inHeight)continue;let ne=X*p[0],J=B+K*u[1];for(let se=0;se<h.outWidth;++se){let Q=q+se*T.strides[2],oe=se*h.strideWidth-x;for(let ae=0;ae<f;++ae){let pe=oe+ae*y;if(pe<0||pe>=h.inWidth)continue;let ue=ne+ae*p[1],ge=J+pe*h.inChannels,ye=Q,ke=ue;for(let Se=0;Se<h.inChannels;++Se){let Ce=C[ge+Se];for(let Re=0;Re<N;++Re)$[ye+Re]+=Ce*F[ke+Re];ye+=N,ke+=N}}}}}}return n.makeTensorInfo(T.shape,T.dtype,T.values)}var HH={kernelName:Vs,backendName:"cpu",kernelFunc:PT};function jH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=a;we([r,s],"depthwiseConv2dNativeBackpropFilter");let p=_.computeConv2DInfo(r.shape,u,i,o,l,c,!0),{strideHeight:d,strideWidth:h,filterHeight:m,filterWidth:f}=p,g=new Ot(p.filterShape,"float32"),y=p.padInfo.left,b=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,N=new Ot(r.shape,r.dtype,x),T=n.data.get(s.dataId).values,C=new Ot(s.shape,s.dtype,T);for(let F=0;F<m;++F){let $=Math.max(0,Math.ceil((b-F)/d)),P=Math.min(p.outHeight,(p.inHeight+b-F)/d);for(let B=0;B<f;++B){let W=Math.max(0,Math.ceil((y-B)/h)),G=Math.min(p.outWidth,(p.inWidth+y-B)/h);for(let q=0;q<p.outChannels;++q){let j=Math.trunc(q/v),X=q%v,K=0;for(let ne=0;ne<p.batchSize;++ne)for(let J=$;J<P;++J){let se=F+J*d-b;for(let Q=W;Q<G;++Q){let oe=B+Q*h-y;K+=N.get(ne,se,oe,j)*C.get(ne,J,Q,q)}}g.set(K,F,B,j,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var qH={kernelName:Kd,backendName:"cpu",kernelFunc:jH};function KH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=a;we([r,s],"depthwiseConv2DNativeBackpropInput");let p=k.computeStrides(r.shape),d=k.computeStrides(s.shape),h=_.computeConv2DInfo(u,s.shape,i,o,l,c,!0),m=new Ot(h.inShape,"float32"),f=m.values,[g,y,b]=m.strides,v=n.data.get(r.dataId).values,[x,N,T]=p,C=n.data.get(s.dataId).values,[F,$,P]=d,{batchSize:B,filterHeight:W,filterWidth:G,inChannels:q,inHeight:j,inWidth:X,outChannels:K,outHeight:ne,outWidth:J,strideHeight:se,strideWidth:Q}=h,oe=W-1-h.padInfo.top,ae=G-1-h.padInfo.left,pe=K/q;for(let ue=0;ue<B;++ue)for(let ge=0;ge<q;++ge)for(let ye=0;ye<j;++ye){let ke=ye-oe,Se=Math.max(0,Math.ceil(ke/se)),Ce=Math.min(ne,(W+ke)/se);for(let Re=0;Re<X;++Re){let Ke=Re-ae,Ve=Math.max(0,Math.ceil(Ke/Q)),st=Math.min(J,(G+Ke)/Q),lt=0;for(let He=Se;He<Ce;++He){let ct=He*se-ke;for(let pt=Ve;pt<st;++pt){let Hn=pt*Q-Ke,Je=x*ue+N*He+T*pt,En=F*(W-1-ct)+$*(G-1-Hn)+P*ge;for(let en=0;en<pe;++en){let An=ge*pe+en,ra=v[Je+An],vn=C[En+en];lt+=ra*vn}}}f[g*ue+y*ye+b*Re+ge]=lt}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var XH={kernelName:Xd,backendName:"cpu",kernelFunc:KH};function YH(e){let{inputs:t,backend:n}=e,{x:a}=t,r=k.sizeFromShape(a.shape),s=n.data.get(a.dataId).values,i=Le([r,r],a.dtype),o=i.values;for(let c=0;c<s.length;c++)o[c*r+c]=s[c];let l=[...a.shape,...a.shape];return n.makeTensorInfo(l,i.dtype,i.values)}var ZH={kernelName:Yd,backendName:"cpu",kernelFunc:YH},JH={kernelName:xc,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,c=l.data.get(a.dataId).values,u=a.shape.length,p=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:b,padInfo:v,strideHeight:x,strideWidth:N,filterHeight:T,filterWidth:C,dilationHeight:F,dilationWidth:$,outShape:P}=_.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),B=k.sizeFromShape(P),W=P.length,G=k.getArrayFromDType(a.dtype,B);for(let q=0;q<h;++q)for(let j=0;j<y;++j){let X=j*x-v.top;for(let K=0;K<b;++K){let ne=K*N-v.left;for(let J=0;J<g;++J){let se=Number.MIN_SAFE_INTEGER;for(let oe=0;oe<T;++oe){let ae=X+oe*F;if(ae>=0&&ae<m)for(let pe=0;pe<C;++pe){let ue=ne+pe*$;if(ue>=0&&ue<f){let ge=k.locToIndex([q,ae,ue,J],u,k.computeStrides(a.shape)),ye=k.locToIndex([oe,pe,J],d,k.computeStrides(r.shape)),ke=c[ge]+p[ye];ke>se&&(se=ke)}}}let Q=k.locToIndex([q,j,K,J],W,k.computeStrides(P));G[Q]=se}}}return{dataId:l.write(k.toTypedArray(G,a.dtype),P,a.dtype),shape:P,dtype:a.dtype}}},QH={kernelName:Jd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=k.toNestedArray(a.shape,c.data.get(a.dataId).values),p=k.toNestedArray(r.shape,c.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:v,strideWidth:x,filterHeight:N,filterWidth:T,dilationHeight:C,dilationWidth:F,outShape:$}=_.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===$.length,()=>`Error in ${Jd}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let P=k.toNestedArray($,c.data.get(s.dataId).values),B=k.makeZerosNestedTypedArray(r.shape,r.dtype);for(let W=0;W<d;++W)for(let G=0;G<g;++G){let q=G*v-b.top;for(let j=0;j<y;++j){let X=j*x-b.left;for(let K=0;K<f;++K){let ne=Number.MIN_SAFE_INTEGER,J=0,se=0;for(let Q=0;Q<N;++Q){let oe=q+Q*C;if(oe>=0&&oe<h)for(let ae=0;ae<T;++ae){let pe=X+ae*F;if(pe>=0&&pe<m){let ue=u[W][oe][pe][K]+p[Q][ae][K];ue>ne&&(ne=ue,J=Q,se=ae)}}}B[J][se][K]+=P[W][G][j][K]}}}return{dataId:c.write(k.toTypedArray(B,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},e6={kernelName:Zd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=k.toNestedArray(a.shape,c.data.get(a.dataId).values),p=k.toNestedArray(r.shape,c.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:v,strideWidth:x,filterHeight:N,filterWidth:T,dilationHeight:C,dilationWidth:F,outShape:$}=_.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===$.length,()=>`Error in ${Zd}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let P=k.toNestedArray($,c.data.get(s.dataId).values),B=k.makeZerosNestedTypedArray(a.shape,a.dtype);for(let W=0;W<d;++W)for(let G=0;G<g;++G){let q=G*v-b.top;for(let j=0;j<y;++j){let X=j*x-b.left;for(let K=0;K<f;++K){let ne=Number.MIN_SAFE_INTEGER,J=q<0?0:q,se=X<0?0:X;for(let Q=0;Q<N;++Q){let oe=q+Q*C;if(oe>=0&&oe<h)for(let ae=0;ae<T;++ae){let pe=X+ae*F;if(pe>=0&&pe<m){let ue=u[W][oe][pe][K]+p[Q][ae][K];ue>ne&&(ne=ue,J=oe,se=pe)}}}B[W][J][se][K]+=P[W][G][j][K]}}}return{dataId:c.write(k.toTypedArray(B,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function t6(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;we([a,r],"eluGrad");let s=new Float32Array(k.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l<i.length;++l){let c=i[l];c>=1?s[l]=o[l]:s[l]=o[l]*(c+1)}return n.makeTensorInfo(r.shape,"float32",s)}var 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t.makeTensorInfo([o.length],s,o)}var eq={kernelName:Sc,backendName:"cpu",kernelFunc:Qj},tq=it(kl,e=>1/e),nq={kernelName:kl,backendName:"cpu",kernelFunc:tq};function aq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;we(r,"resizeBilinear");let l=k.computeStrides(r.shape),[c,u]=o,[p,d,h,m]=r.shape,f=n.data.get(r.dataId).values,g=new Float32Array(k.sizeFromShape([p,c,u,m])),y=[s&&c>1?d-1:d,s&&u>1?h-1:h],b=[s&&c>1?c-1:c,s&&u>1?u-1:u],v=0,x=y[0]/b[0],N=y[1]/b[1];for(let T=0;T<p;T++)for(let C=0;C<c;C++){let F;i?F=x*(C+.5)-.5:F=x*C;let $=Math.max(0,Math.floor(F)),P=F-$,B=Math.min(d-1,Math.ceil(F)),W=T*l[0]+$*l[1],G=T*l[0]+B*l[1];for(let q=0;q<u;q++){let j;i?j=N*(q+.5)-.5:j=N*q;let X=Math.max(0,Math.floor(j)),K=j-X,ne=Math.min(h-1,Math.ceil(j)),J=W+X*l[2],se=G+X*l[2],Q=W+ne*l[2],oe=G+ne*l[2];for(let ae=0;ae<m;ae++){let pe=f[J+ae],ue=f[se+ae],ge=f[Q+ae],ye=f[oe+ae],ke=pe+(ge-pe)*K,Se=ue+(ye-ue)*K,Ce=ke+(Se-ke)*P;g[v++]=Ce}}}return n.makeTensorInfo([p,c,u,m],"float32",g)}var rq={kernelName:ci,backendName:"cpu",kernelFunc:aq};function sq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;we([s,r],"resizeBilinearGrad");let o=k.computeStrides(r.shape),[l,c,u,p]=r.shape,[,d,h]=s.shape,m=new Float32Array(l*c*u*p),f=[i&&d>1?c-1:c,i&&h>1?u-1:u],g=[i&&d>1?d-1:d,i&&h>1?h-1:h],y=f[0]/g[0],b=f[1]/g[1],v=n.data.get(s.dataId).values,x=0;for(let N=0;N<l;N++){let T=N*o[0];for(let C=0;C<d;C++){let F=C*y,$=Math.floor(F),P=Math.min(Math.ceil(F),c-1),B=T+$*o[1],W=T+P*o[1],G=F-$,q=1-G;for(let j=0;j<h;j++){let X=j*b,K=Math.floor(X),ne=Math.min(Math.ceil(X),u-1),J=X-K,se=1-J,Q=B+K*o[2],oe=B+ne*o[2],ae=W+K*o[2],pe=W+ne*o[2],ue=q*se,ge=q*J,ye=G*se,ke=G*J;for(let Se=0;Se<p;Se++){let Ce=v[x++];m[Q+Se]+=Ce*ue,m[oe+Se]+=Ce*ge,m[ae+Se]+=Ce*ye,m[pe+Se]+=Ce*ke}}}}return n.makeTensorInfo([l,u,c,p],"float32",m)}var iq={kernelName:ph,backendName:"cpu",kernelFunc:sq};function 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Cp(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ee=ee();Ee.registerFlag("HAS_WEBGL",()=>Ee.getNumber("WEBGL_VERSION")>0);Ee.registerFlag("WEBGL_VERSION",()=>KT(2)?2:KT(1)?1:0);Ee.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ee.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ee.get("WEBGL_VERSION")===2);Ee.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ee.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ee.registerFlag("WEBGL_PACK",()=>Ee.getBool("HAS_WEBGL"));Ee.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_CLIP",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Ee.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_REDUCE",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_CONV_IM2COL",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>O5(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>L5(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let 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       bool isnan_custom(float val) {
         return (val > 0.0 || val < 0.0) ? false : val != 0.0;
       }
@@ -86,11 +86,11 @@ 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:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function Yi(e,t,n="index"){let a=k.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / ${r}`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function Jv(e){let t=k.computeStrides(e).map(n=>n.toString());return`
+    `),{version:e,attribute:t,varyingVs:n,varyingFs:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function Ji(e,t,n="index"){let a=k.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / ${r}`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function aw(e){let t=k.computeStrides(e).map(n=>n.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
-`}var UT=`
+`}var XT=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -129,9 +129,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
-`,B5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Ip.DENSE;let t=Tp(e),n=mn();this.outputShape=e,this.userCode=`
+`,G5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Np.DENSE;let t=Sp(e),n=mn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${Yi(["r","c","d"],e)}
+        ${Ji(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -150,9 +150,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = result;
       }
-    `}},W5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Ip.DENSE;let t=Tp(e),n=mn();this.outputShape=e,this.userCode=`
+    `}},H5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Np.DENSE;let t=Sp(e),n=mn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${Yi(["r","c","d"],e)}
+        ${Ji(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -171,23 +171,23 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = result;
       }
-    `}},V5=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ea.DOWNLOAD;let t=mn();this.outputShape=e,this.userCode=`
-      ${UT}
+    `}},j5=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ea.DOWNLOAD;let t=mn();this.outputShape=e,this.userCode=`
+      ${XT}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
-    `}},U5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ea.DOWNLOAD;let t=mn();this.outputShape=e,this.userCode=`
-      ${UT}
+    `}},q5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ea.DOWNLOAD;let t=mn();this.outputShape=e,this.userCode=`
+      ${XT}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
         ${t.output} = encode_float(x);
       }
-    `}},G5=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=mn(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
-      ${Jv(e)}
+    `}},K5=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=mn(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
+      ${aw(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -216,7 +216,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${a.output} = vec4(${i}, 0., 0., 0.);
       }
-    `}},H5=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=mn(),[r,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+=`
+    `}},X5=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=mn(),[r,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] + ${c} < ${e[2]}) {
             localCoords[2] += ${c};
@@ -245,7 +245,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
             }
           }
         `}this.userCode=`
-      ${Jv(e)}
+      ${aw(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -260,7 +260,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${a.output} = ${o};
       }
-    `}};function j5(e){let t=mn(),n=`${t.version}
+    `}};function Y5(e){let t=mn(),n=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -269,22 +269,22 @@ 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 f5(e,n)}function q5(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 w5(e,t)}function K5(e){let t=new Uint16Array([0,1,2,2,1,3]);return k5(e,t)}function Cp(e,t,n,a,r,s){N5(t,n);let i=I5(e),o=e.TEXTURE_2D;return Ne(e,()=>e.bindTexture(o,i)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ne(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),Ne(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function GT(e){return e.internalFormatFloat}function X5(e,t,n,a){let[r,s]=Np(t,n);return Cp(e,r,s,GT(a),a.textureFormatFloat,e.FLOAT)}function HT(e){return e.internalFormatHalfFloat}function Y5(e,t,n,a){let[r,s]=Np(t,n);return Cp(e,r,s,HT(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function jT(e){return e.downloadTextureFormat}function Z5(e,t,n,a){let[r,s]=Np(t,n);return Cp(e,r,s,jT(a),e.RGBA,e.UNSIGNED_BYTE)}function qT(e){return e.internalFormatPackedFloat}function J5(e,t,n,a){let[r,s]=gu(t,n);return Cp(e,r,s,qT(a),e.RGBA,e.FLOAT)}function KT(e){return e.internalFormatPackedHalfFloat}function Q5(e,t,n,a){let[r,s]=gu(t,n);return Cp(e,r,s,KT(a),e.RGBA,a.textureTypeHalfFloat)}function e8(e,t,n){let a=0,r=3*4,s=3*4+2*4;return Ne(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),BT(e,t,"clipSpacePos",n,3,s,a)&&BT(e,t,"uv",n,2,s,r)}function t8(e,t,n,a,r,s){Ne(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),Ne(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),Ne(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function n8(e,t,n){Ne(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Ne(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ne(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ne(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function a8(e,t,n,a){let r=e.createBuffer();Ne(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return Ne(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),Ne(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ne(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function r8(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function s8(e,t,n,a){let[r,s]=Np(t,n),i=4,o=new Uint8Array(l5(t*n,i));return Ne(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function i8(e,t,n,a,r,s,i,o){let l=e,c=new Float32Array(u5(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 o8(e,t,n){let a=new Float32Array(t*n*4);return Ne(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var u8=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=ee().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,s5(t,e)):this.gl=ir(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(ee().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Qm(this.gl,r),xa(this.gl,s))this.textureHalfFloatExtension=Qm(this.gl,s);else if(ee().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),xa(this.gl,a))this.colorBufferHalfFloatExtension=Qm(this.gl,a);else if(ee().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",xa(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(xa(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=q5(this.gl),this.indexBuffer=K5(this.gl),this.framebuffer=T5(this.gl),this.textureConfig=Hv(this.gl,this.textureHalfFloatExtension)}get debug(){return ee().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 x5(e,n)}function Z5(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 T5(e,t)}function J5(e){let t=new Uint16Array([0,1,2,2,1,3]);return S5(e,t)}function _p(e,t,n,a,r,s){_5(t,n);let i=C5(e),o=e.TEXTURE_2D;return Ne(e,()=>e.bindTexture(o,i)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ne(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ne(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),Ne(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function YT(e){return e.internalFormatFloat}function Q5(e,t,n,a){let[r,s]=Tp(t,n);return _p(e,r,s,YT(a),a.textureFormatFloat,e.FLOAT)}function ZT(e){return e.internalFormatHalfFloat}function e8(e,t,n,a){let[r,s]=Tp(t,n);return _p(e,r,s,ZT(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function JT(e){return e.downloadTextureFormat}function t8(e,t,n,a){let[r,s]=Tp(t,n);return _p(e,r,s,JT(a),e.RGBA,e.UNSIGNED_BYTE)}function QT(e){return e.internalFormatPackedFloat}function n8(e,t,n,a){let[r,s]=gu(t,n);return _p(e,r,s,QT(a),e.RGBA,e.FLOAT)}function eS(e){return e.internalFormatPackedHalfFloat}function a8(e,t,n,a){let[r,s]=gu(t,n);return _p(e,r,s,eS(a),e.RGBA,a.textureTypeHalfFloat)}function r8(e,t,n){let a=0,r=3*4,s=3*4+2*4;return Ne(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),jT(e,t,"clipSpacePos",n,3,s,a)&&jT(e,t,"uv",n,2,s,r)}function s8(e,t,n,a,r,s){Ne(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),Ne(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),Ne(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function i8(e,t,n){Ne(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Ne(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ne(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ne(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function o8(e,t,n,a){let r=e.createBuffer();Ne(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return Ne(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),Ne(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ne(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function l8(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function u8(e,t,n,a){let[r,s]=Tp(t,n),i=4,o=new Uint8Array(d5(t*n,i));return Ne(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function c8(e,t,n,a,r,s,i,o){let l=e,c=new Float32Array(h5(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 p8(e,t,n){let a=new Float32Array(t*n*4);return Ne(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var h8=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=ee().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,u5(t,e)):this.gl=or(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(ee().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=nf(this.gl,r),va(this.gl,s))this.textureHalfFloatExtension=nf(this.gl,s);else if(ee().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),va(this.gl,a))this.colorBufferHalfFloatExtension=nf(this.gl,a);else if(ee().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",va(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(va(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Z5(this.gl),this.indexBuffer=J5(this.gl),this.framebuffer=E5(this.gl),this.textureConfig=Yv(this.gl,this.textureHalfFloatExtension)}get debug(){return ee().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;Ne(e,()=>e.finish()),Ne(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ne(e,()=>e.deleteFramebuffer(this.framebuffer)),Ne(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ne(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ne(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Q5(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),e8(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),t8(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),i8(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),s8(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),a8(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),n8(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(qT(this.gl,this.framebuffer),this.outputTexture=null),Ne(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>u8(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return c8(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return l8(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=o8(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(ee().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=a.clientWaitSync(r,0,0);return s===a.ALREADY_SIGNALED||s===a.CONDITION_SATISFIED},t=r}else ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>p8(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=w5(t,e),a=Y5(t),r=I5(t);return Ne(t,()=>t.attachShader(r,a)),Ne(t,()=>t.attachShader(r,n)),N5(t,r),this.debug&&Zv(t,r),this.vertexAttrsAreBound||(this.setProgram(r),this.vertexAttrsAreBound=r8(t,this.program,this.vertexBuffer)),r}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ne(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Zv(this.gl,this.program),Ne(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?$5(this.gl,e,t):D5(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ne(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(),R5(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=gu(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Zv(this.gl,this.program),af(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ne(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ne(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=nf(this.gl,ee().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(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(ee().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 k.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,ee().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,a=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),a=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=d8(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)&&k.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Jv(this.gl,e,this.framebuffer),this.debug&&af(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Jv(this.gl,this.outputTexture,this.framebuffer),this.debug&&af(this.gl)):qT(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;Jv(a,e,this.framebuffer),this.debug&&af(a),this.outputTexture=e,Ne(a,()=>a.viewport(0,0,t,n)),Ne(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),Ne(this.gl,()=>this.gl.scissor(e,t,n,a))}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 d8(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:tS}=_;function k8(e,t,n,a){let r=[];e.forEach(h=>{let m=k.sizeFromShape(h.shapeInfo.logicalShape);h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${m>1?`[${m}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`))});let s=r.join(`
+`),i=e.map(h=>m8(h,t,a)).join(`
+`),o=t.texShape,l=mn(),c=y8(l),u,p,d=v8(l);return t.isPacked?(u=f8(t.logicalShape,o),p=x8(l)):(u=g8(t.logicalShape,o),p=b8(l)),a&&(d+=w8),[d,c,p,s,u,i,n].join(`
+`)}function xu(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return I8(e);case 1:return N8(e);case 2:return T8(e);case 3:return S8(e);case 4:return C8(e);case 5:return _8(e);case 6:return E8(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function nS(e){switch(e.shapeInfo.logicalShape.length){case 0:return A8(e);case 1:return F8(e);case 2:return $8(e);case 3:return D8(e);default:return R8(e)}}function m8(e,t,n=!1){let a="";n?a+=nS(e):a+=xu(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=M8(e,t):a+=P8(e,t)),a}function f8(e,t){switch(e.length){case 0:return aS();case 1:return O8(e,t);case 2:return B8(e,t);case 3:return L8(e,t);default:return z8(e,t)}}function g8(e,t){switch(e.length){case 0:return aS();case 1:return W8(e,t);case 2:return j8(e,t);case 3:return V8(e,t);case 4:return U8(e,t);case 5:return G8(e,t);case 6:return H8(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function y8(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
-  `}function m8(e){return`
+  `}function b8(e){return`
     void setOutput(float val) {
       ${e.output} = vec4(val, 0, 0, 0);
     }
-  `}function f8(e){return`
+  `}function x8(e){return`
     void setOutput(vec4 val) {
       ${e.output} = val;
     }
-  `}function g8(e){return`${e.version}
+  `}function v8(e){return`${e.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
@@ -339,10 +339,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       return fract((p3.x + p3.y) * p3.z);
     }
 
-    ${U8}
-    ${G8}
-    ${H8}
-  `}var U8=`
+    ${q8}
+    ${K8}
+    ${X8}
+  `}var q8=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -354,7 +354,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,G8=`
+`,K8=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -362,7 +362,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,H8=`
+`,X8=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -371,7 +371,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,y8=`
+`,w8=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -382,11 +382,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function ZT(){return`
+`;function aS(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function D8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
+  `}function O8(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 * ${n[1]}.0);
       }
@@ -400,7 +400,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${n[0]}, ${n[1]}));
       return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
     }
-  `}function O8(e,t){return t[0]===1?`
+  `}function W8(e,t){return t[0]===1?`
       int getOutputCoords() {
         return int(resultUV.x * ${t[1]}.0);
       }
@@ -414,7 +414,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${t[0]}, ${t[1]}));
       return resTexRC.x * ${t[1]} + resTexRC.y;
     }
-  `}function R8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
+  `}function L8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${n[0]}, ${n[1]}));
@@ -428,7 +428,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function L8(e,t){let n=Yi(["r","c","d"],e);return`
+  `}function V8(e,t){let n=Ji(["r","c","d"],e);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -436,7 +436,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec3(r, c, d);
     }
-  `}function M8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
+  `}function z8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
       int b${l} = index / ${s};
       index -= b${l} * ${s};
     `+i,o=`b${l}, `+o;return`
@@ -455,7 +455,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${e.length}(${o});
     }
-  `}function z8(e,t){let n=Yi(["r","c","d","d2"],e);return`
+  `}function U8(e,t){let n=Ji(["r","c","d","d2"],e);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -463,7 +463,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec4(r, c, d, d2);
     }
-  `}function B8(e,t){let n=Yi(["r","c","d","d2","d3"],e);return`
+  `}function G8(e,t){let n=Ji(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -475,7 +475,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function W8(e,t){let n=Yi(["r","c","d","d2","d3","d4"],e);return`
+  `}function H8(e,t){let n=Ji(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -486,7 +486,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function P8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
+  `}function B8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
       ivec2 getOutputCoords() {
         return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
       }
@@ -501,7 +501,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec2(r, c);
     }
-  `}function V8(e,t){return k.arraysEqual(e,t)?`
+  `}function j8(e,t){return k.arraysEqual(e,t)?`
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
       }
@@ -528,26 +528,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function Zi(e){return`offset${e}`}function S8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=mn();return`
+  `}function Qi(e){return`offset${e}`}function A8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=mn();return`
     vec4 ${n}() {
       return ${a.texture2D}(${t}, halfCR);
     }
-  `}function x8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[a,r]=e.shapeInfo.texShape;if(a===1&&r===1)return`
+  `}function I8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[a,r]=e.shapeInfo.texShape;if(a===1&&r===1)return`
       float ${n}() {
         return sampleTexture(${t}, halfCR);
       }
-    `;let[s,i]=e.shapeInfo.texShape,o=Zi(t);return`
+    `;let[s,i]=e.shapeInfo.texShape,o=Qi(t);return`
     float ${n}() {
       vec2 uv = uvFromFlat(${s}, ${i}, ${o});
       return sampleTexture(${t}, uv);
     }
-  `}function C8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=e.shapeInfo.texShape,r=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=mn();return`
+  `}function F8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=e.shapeInfo.texShape,r=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=mn();return`
     vec4 ${n}(int index) {
       vec2 uv = packedUVfrom1D(
         ${r[0]}, ${r[1]}, index);
       return ${s.texture2D}(${t}, uv);
     }
-  `}function v8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
+  `}function N8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
       float ${n}(int index) {
         ${vu(e)}
       }
@@ -555,7 +555,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       float ${n}(int index) {
         return sampleTexture(${t}, halfCR);
       }
-    `;let i=Zi(t);return s===1?`
+    `;let i=Qi(t);return s===1?`
       float ${n}(int index) {
         vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${r}.0);
         return sampleTexture(${t}, uv);
@@ -570,7 +570,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${r}, ${s}, index + ${i});
       return sampleTexture(${t}, uv);
     }
-  `}function _8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=r[0],i=r[1],o=mn();if(r!=null&&k.arraysEqual(t,r))return`
+  `}function $8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=r[0],i=r[1],o=mn();if(r!=null&&k.arraysEqual(t,r))return`
       vec4 ${a}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
 
@@ -581,7 +581,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
       return ${o.texture2D}(${n}, uv);
     }
-  `}function w8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape;if(r!=null&&k.arraysEqual(t,r)){let p=r[0],d=r[1];return`
+  `}function T8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape;if(r!=null&&k.arraysEqual(t,r)){let p=r[0],d=r[1];return`
     float ${a}(int row, int col) {
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${p}.0);
       return sampleTexture(${n}, uv);
@@ -596,7 +596,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
         ${vu(e)}
       }
-    `;let l=r[0],c=r[1],u=Zi(n);return c===1?`
+    `;let l=r[0],c=r[1],u=Qi(n);return c===1?`
     float ${a}(int row, int col) {
       float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
       vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
@@ -615,8 +615,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${l}, ${c}, index);
     return sampleTexture(${n}, uv);
   }
-`}function E8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];if(t[0]===1){let p=t.slice(1),d=[1,2],h=wu(e,p),m=["b","row","col"];return`
-        ${YT(h)}
+`}function D8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];if(t[0]===1){let p=t.slice(1),d=[1,2],h=wu(e,p),m=["b","row","col"];return`
+        ${nS(h)}
         vec4 ${a}(int b, int row, int col) {
           return ${a}(${ku(m,d)});
         }
@@ -626,7 +626,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${i}, ${o}, ${c}, ${l}, b, row, col);
       return ${u.texture2D}(${n}, uv);
     }
-  `}function k8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=k.squeezeShape(t),l=i;if(l.length<t.length){let m=wu(e,l),f=["row","col","depth"];return`
+  `}function S8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=k.squeezeShape(t),l=i;if(l.length<t.length){let m=wu(e,l),f=["row","col","depth"];return`
         ${xu(m)}
         float ${a}(int row, int col, int depth) {
           return ${a}(${ku(f,o)});
@@ -652,14 +652,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${u}.0);
       return sampleTexture(${n}, uv);
     }
-  `;let h=Zi(n);return`
+  `;let h=Qi(n);return`
       float ${a}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
         int index = row * ${r} + col * ${s} + depth + ${h};
         vec2 uv = uvFromFlat(${u}, ${p}, index);
         return sampleTexture(${n}, uv);
       }
-  `}function A8(e){let t=e.shapeInfo.logicalShape,n=t.length,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.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),p="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let m=2;m<n-1;m++)p=`int b${m}, `+p,u*=t[n-m-1],d=`b${m} * ${u} + `+d;let h=mn();return`
+  `}function R8(e){let t=e.shapeInfo.logicalShape,n=t.length,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.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),p="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let m=2;m<n-1;m++)p=`int b${m}, `+p,u*=t[n-m-1],d=`b${m} * ${u} + `+d;let h=mn();return`
     vec4 ${r}(${p}) {
       int index = ${d};
       int texR = index / ${l};
@@ -667,7 +667,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
       return ${h.texture2D}(${a}, uv);
     }
-  `}function I8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[3],s=t[2]*r,i=t[1]*s,{newShape:o,keptDims:l}=k.squeezeShape(t);if(o.length<t.length){let m=wu(e,o),f=["row","col","depth","depth2"];return`
+  `}function C8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[3],s=t[2]*r,i=t[1]*s,{newShape:o,keptDims:l}=k.squeezeShape(t);if(o.length<t.length){let m=wu(e,o),f=["row","col","depth","depth2"];return`
       ${xu(m)}
       float ${a}(int row, int col, int depth, int depth2) {
         return ${a}(${ku(f,l)});
@@ -697,7 +697,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${d}.0, ${p}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let h=Zi(n);return`
+    `;let h=Qi(n);return`
     float ${a}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${i} + col * ${s} +
@@ -705,7 +705,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${p}, ${d}, index + ${h});
       return sampleTexture(${n}, uv);
     }
-  `}function N8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=k.squeezeShape(t);if(l.length<t.length){let f=wu(e,l),g=["row","col","depth","depth2","depth3"];return`
+  `}function _8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=k.squeezeShape(t);if(l.length<t.length){let f=wu(e,l),g=["row","col","depth","depth2","depth3"];return`
       ${xu(f)}
       float ${a}(int row, int col, int depth, int depth2, int depth3) {
         return ${a}(${ku(g,c)});
@@ -738,7 +738,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${h}.0, ${d}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let m=Zi(n);return`
+    `;let m=Qi(n);return`
     float ${a}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${o} + col * ${i} + depth * ${s} +
@@ -746,7 +746,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${d}, ${h}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function T8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=k.squeezeShape(t);if(r.length<t.length){let g=wu(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
+  `}function E8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=k.squeezeShape(t);if(r.length<t.length){let g=wu(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
       ${xu(g)}
       float ${a}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
@@ -787,7 +787,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${m}.0, ${h}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let f=Zi(n);return`
+    `;let f=Qi(n);return`
     float ${a}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -802,7 +802,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         return ${t}[i];
       }
     }
-  `}function F8(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=XT(e.shapeInfo.logicalShape,t.logicalShape),l=ft(i),c=i-s,u,p=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(g=>`coords.${p[g+c]} = 0;`).join(`
+  `}function M8(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=tS(e.shapeInfo.logicalShape,t.logicalShape),l=ft(i),c=i-s,u,p=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(g=>`coords.${p[g+c]} = 0;`).join(`
 `);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((g,y)=>`coords.${p[y+c]}`).join(", ");let h="return outputValue;",m=k.sizeFromShape(e.shapeInfo.logicalShape)===1,f=k.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=`
       return vec4(outputValue.xy, outputValue.xy);
     `;else if(m&&!f)i===1?h=`
@@ -816,22 +816,22 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec4 outputValue = get${a}(${d});
       ${h}
     }
-  `}function $8(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"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&&k.arraysEqual(i,s))return`
+  `}function P8(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"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&&k.arraysEqual(i,s))return`
       float ${r}() {
         return sampleTexture(${n}, resultUV);
       }
-    `;let c=ft(l),u=XT(e.shapeInfo.logicalShape,t.logicalShape),p=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(f=>`coords.${h[f+p]} = 0;`).join(`
+    `;let c=ft(l),u=tS(e.shapeInfo.logicalShape,t.logicalShape),p=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(f=>`coords.${h[f+p]} = 0;`).join(`
 `);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+p]}`).join(", "),`
     float ${r}() {
       ${c} coords = getOutputCoords();
       ${d}
       return get${a}(${m});
     }
-  `}function ft(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 ${e} is not yet supported`)}function wu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function ku(e,t){return t.map(n=>e[n]).join(", ")}function j8(e,t,n,a){let r=t.userCode,s=n.map((h,m)=>{let f={logicalShape:h.shape,texShape:h.isUniform?null:h.texData.texShape,isUniform:h.isUniform,isPacked:h.isUniform?!1:h.texData.isPacked,flatOffset:null};return h.texData!=null&&h.texData.slice!=null&&h.texData.slice.flatOffset>0&&(f.flatOffset=h.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),i=s.map(h=>h.shapeInfo),o={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},l=b8(s,o,r,t.packedInputs),c=e.createProgram(l),u=null,p=e.getUniformLocation(c,"NAN",!1);ee().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let h=0;h<t.variableNames.length;h++){let m=t.variableNames[h],f=!1;d[m]=e.getUniformLocation(c,m,f),d[`offset${m}`]=e.getUniformLocation(c,`offset${m}`,f)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:p}}function JT(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,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!k.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!k.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function q8(e,t,n,a,r){JT(t.inShapeInfos,n),JT([t.outShapeInfo],[a]);let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),ee().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],p=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(k.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&&p!=null&&e.gl.uniform1i(p,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),r!=null&&r(e,t.webGLProgram),e.executeProgram()}function K8(e,t,n){let a="";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;a+=`${i.shape}_${l}_${o}`});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r,s}var{addImpl:X8,bincountImpl:QT,bincountReduceImpl:Y8,ceilImpl:Z8,concatImpl:J8,expImpl:Q8,expm1Impl:eK,floorImpl:tK,gatherV2Impl:nK,greaterImpl:aK,lessImpl:rK,linSpaceImpl:sK,logImpl:iK,maxImpl:oK,maximumImpl:lK,minimumImpl:uK,multiplyImpl:cK,negImpl:pK,prodImpl:dK,rangeImpl:hK,rsqrtImpl:mK,simpleAbsImpl:eS,sliceImpl:fK,stridedSliceImpl:gK,subImpl:yK,tileImpl:bK,topKImpl:xK,transposeImpl:Qv,uniqueImpl:vK}=KN;function tS(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function fn(e,t){return t===1?[e]:tS(e,t)}function wK(e,t){if(e===1)return"rc";let n="";for(let a=0;a<e;a++)n+=t[a],a<e-1&&(n+=",");return n}var TK=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 ft(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 ${e} is not yet supported`)}function wu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function ku(e,t){return t.map(n=>e[n]).join(", ")}function Y8(e,t,n,a){let r=t.userCode,s=n.map((h,m)=>{let f={logicalShape:h.shape,texShape:h.isUniform?null:h.texData.texShape,isUniform:h.isUniform,isPacked:h.isUniform?!1:h.texData.isPacked,flatOffset:null};return h.texData!=null&&h.texData.slice!=null&&h.texData.slice.flatOffset>0&&(f.flatOffset=h.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),i=s.map(h=>h.shapeInfo),o={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},l=k8(s,o,r,t.packedInputs),c=e.createProgram(l),u=null,p=e.getUniformLocation(c,"NAN",!1);ee().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let h=0;h<t.variableNames.length;h++){let m=t.variableNames[h],f=!1;d[m]=e.getUniformLocation(c,m,f),d[`offset${m}`]=e.getUniformLocation(c,`offset${m}`,f)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:p}}function rS(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,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!k.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!k.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function Z8(e,t,n,a,r){rS(t.inShapeInfos,n),rS([t.outShapeInfo],[a]);let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),ee().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],p=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(k.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&&p!=null&&e.gl.uniform1i(p,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),r!=null&&r(e,t.webGLProgram),e.executeProgram()}function J8(e,t,n){let a="";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;a+=`${i.shape}_${l}_${o}`});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r,s}var{addImpl:Q8,bincountImpl:sS,bincountReduceImpl:eK,ceilImpl:tK,concatImpl:nK,expImpl:aK,expm1Impl:rK,floorImpl:sK,gatherV2Impl:iK,greaterImpl:oK,lessImpl:lK,linSpaceImpl:uK,logImpl:cK,maxImpl:pK,maximumImpl:dK,minimumImpl:hK,multiplyImpl:mK,negImpl:fK,prodImpl:gK,rangeImpl:yK,rsqrtImpl:bK,simpleAbsImpl:iS,sliceImpl:xK,stridedSliceImpl:vK,subImpl:wK,tileImpl:kK,topKImpl:IK,transposeImpl:rw,uniqueImpl:NK}=eT;function oS(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function fn(e,t){return t===1?[e]:oS(e,t)}function TK(e,t){if(e===1)return"rc";let n="";for(let a=0;a<e;a++)n+=t[a],a<e-1&&(n+=",");return n}var EK=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=fn("rc",t),a=ft(t),r=kK(t,e,n),s=IK(t,e[e.length-1],e[e.length-2],n),i=NK(e,n);this.userCode=`
+      `;else{let n=fn("rc",t),a=ft(t),r=SK(t,e,n),s=CK(t,e[e.length-1],e[e.length-2],n),i=_K(e,n);this.userCode=`
         void main() {
           ${a} rc = getOutputCoords();
 
@@ -843,7 +843,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             setOutput(vec4(${i}));
           }
         }
-      `}}};function SK(e,t){let n=[];for(let a=0;a<=1;a++)for(let r=0;r<=1;r++){let s=`${a===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function kK(e,t,n){if(e===1)return`rc > ${t[0]}`;let a="";for(let r=e-2;r<e;r++)a+=`${n[r]} >= ${t[r]}`,r<e-1&&(a+="||");return a}function IK(e,t,n,a){if(e===1)return"";let r=a.slice(-2);return`
+      `}}};function AK(e,t){let n=[];for(let a=0;a<=1;a++)for(let r=0;r<=1;r++){let s=`${a===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function SK(e,t,n){if(e===1)return`rc > ${t[0]}`;let a="";for(let r=e-2;r<e;r++)a+=`${n[r]} >= ${t[r]}`,r<e-1&&(a+="||");return a}function CK(e,t,n,a){if(e===1)return"";let r=a.slice(-2);return`
     int r = ${r[0]};
     int c = ${r[1]};
     int rp1 = r + 1;
@@ -851,12 +851,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
     bool cEdge = cp1 >= ${t};
     bool rEdge = rp1 >= ${n};
-  `}function NK(e,t){let n=e.length,a=SK(n,t);return n===1?`getA(rc),
+  `}function _K(e,t){let n=e.length,a=AK(n,t);return n===1?`getA(rc),
             rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
             0, 0`:`getA(${a[0]}),
           cEdge ? 0. : getA(${a[1]}),
           rEdge ? 0. : getA(${a[2]}),
-          rEdge || cEdge ? 0. : getA(${a[3]})`}var nS=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2==1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=`
+          rEdge || cEdge ? 0. : getA(${a[3]})`}var lS=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2==1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=`
         ${r}
         ${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -868,8 +868,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
         ${a>0?"}":""}
       `}this.userCode=`
-      ${CK(t)}
-      ${Jv(e)}
+      ${FK(t)}
+      ${aw(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -884,12 +884,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function CK(e){return`
+    `}};function FK(e){return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${Yi(["r","c","d"],e)}
+      ${Ji(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var _K=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=rS(t,n),r=sS(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=aS(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===nn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===nn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===nn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===nn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===nn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=rS(n,a),s=sS(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=aS(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=ee().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 EK(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 aS(e,t,n,a,r){let s=AK(t,a),i;if(r){let[l,c]=gu(e[0],e[1]);i=l*c}else{let[l,c]=Np(e[0],e[1]);i=l*c}let o=EK(n,s);return i*o}function AK(e,t){switch(e){case nn.PACKED_2X2_FLOAT32:return qT(t);case nn.PACKED_2X2_FLOAT16:return KT(t);case nn.UNPACKED_FLOAT32:return GT(t);case nn.UNPACKED_FLOAT16:return HT(t);case nn.PACKED_4X1_UNSIGNED_BYTE:return jT(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function FK(e){return ee().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?nn.PACKED_2X2_FLOAT32:nn.UNPACKED_FLOAT32:e?nn.PACKED_2X2_FLOAT16:nn.UNPACKED_FLOAT16}function rS(e,t){if(e===ea.UPLOAD)return nn.PACKED_2X2_FLOAT32;if(e===ea.RENDER||e==null)return FK(t);if(e===ea.DOWNLOAD||e===ea.PIXELS)return nn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function sS(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ms=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+  `}var $K=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=cS(t,n),r=pS(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=uS(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===nn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===nn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===nn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===nn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===nn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=cS(n,a),s=pS(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=uS(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=ee().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 DK(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 uS(e,t,n,a,r){let s=RK(t,a),i;if(r){let[l,c]=gu(e[0],e[1]);i=l*c}else{let[l,c]=Tp(e[0],e[1]);i=l*c}let o=DK(n,s);return i*o}function RK(e,t){switch(e){case nn.PACKED_2X2_FLOAT32:return QT(t);case nn.PACKED_2X2_FLOAT16:return eS(t);case nn.UNPACKED_FLOAT32:return YT(t);case nn.UNPACKED_FLOAT16:return ZT(t);case nn.PACKED_4X1_UNSIGNED_BYTE:return JT(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function MK(e){return ee().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?nn.PACKED_2X2_FLOAT32:nn.UNPACKED_FLOAT32:e?nn.PACKED_2X2_FLOAT16:nn.UNPACKED_FLOAT16}function cS(e,t){if(e===ea.UPLOAD)return nn.PACKED_2X2_FLOAT32;if(e===ea.RENDER||e==null)return MK(t);if(e===ea.DOWNLOAD||e===ea.PIXELS)return nn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function pS(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ms=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -900,11 +900,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},Ma="if (isnan(x)) return x;",$K="return x;",iS="return abs(x);",DK="return (x >= 0.0) ? x : (exp(x) - 1.0);",RK=Ma+`
+    `}},Pa="if (isnan(x)) return x;",PK="return x;",dS="return abs(x);",OK="return (x >= 0.0) ? x : (exp(x) - 1.0);",LK=Pa+`
   return (x < 0.0) ? 0.0 : x;
-`,MK=Ma+`
+`,zK=Pa+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,af="return x;",PK="return x;",OK=`
+`,of="return x;",BK="return x;",WK=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -913,7 +913,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,LK=`
+`,VK=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -923,7 +923,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,zK=`
+`,UK=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -944,14 +944,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},BK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=fn("rc",t),a=ft(t),r=wK(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
+    `}},GK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=fn("rc",t),a=ft(t),r=TK(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
       void main() {
         ${a} rc = getOutputCoords();
         vec4 packedInput = getA(${r});
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}},WK=Qa.whereImpl,VK=1e-7,UK=1e-4,ew={};function GK(e){return e in ew||(ew[e]={}),ew[e]}var HK=128,jK=600;function qK(){return ee().global.screen==null?1024:ee().global.screen.height*ee().global.screen.width*window.devicePixelRatio*jK/1024/1024}var XK=class extends dc{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!ee().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=ir(ee().getNumber("WEBGL_VERSION"));this.binaryCache=GK(ee().getNumber("WEBGL_VERSION")),this.gpgpu=new u8(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 _K(this.gpgpu),this.numMBBeforeWarning=qK(),this.texData=new Fd(this,Jr())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((ee().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||ee().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 a={};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:ea.UPLOAD,refCount:1,complexParentRefCount:0}),a}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--}}decComplexRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.complexParentRefCount>0&&t.refCount--}}move(e,t,n,a){if(ee().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:ea.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(e){let t=e.dataId;if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let p;o?p=new Iu(i,af):p=new ms(i,af);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,c;l&&(c=k.now());let u;if(a==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);u=_.mergeRealAndImagArrays(p,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=k.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Iu(a,af):h=new ms(a,af);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!ee().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&ee().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"&&ee().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...Tp(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];u=_.mergeRealAndImagArrays(m,f)}else if(l==null)u=this.getValuesFromTexture(e);else{let h=k.sizeFromShape(a);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}c!=null&&this.disposeIntermediateTensorInfo(c);let p=this.convertAndCacheOnCPU(e,u),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),p}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!m5(n))throw ee().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=k.sizeFromShape(t);if(ee().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...Tp(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let s=ee().getBool("WEBGL_PACK")&&a===!0,i=s?Kv(t):t,o=s?new U5(i):new V5(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${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 ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;if(this.texData.get(e).complexParentRefCount>0){this.texData.get(e).refCount--;return}this.releaseGPUData(e);let{complexTensorInfos:t}=this.texData.get(e);t!=null&&(this.texData.get(t.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.real),this.texData.get(t.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.imag)),this.texData.delete(e)}releaseGPUData(e){let{texture:t,dtype:n,texShape:a,usage:r,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(a,n),this.textureManager.releaseTexture(t,a,r,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 ee().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Jr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=HK){let n=this.getCPUBackend();return!ee().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(a=>this.texData.get(a.dataId).texture==null&&k.sizeFromShape(a.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){_.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return WK(e.shape,t)}packedUnaryOp(e,t,n){let a=new Iu(e.shape,t);return this.compileAndRun(a,[e],n)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=eS(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(ee().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,iS,e.dtype);let t=new ms(e.shape,iS);return this.compileAndRun(t,[e])}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:a}=this.makeTensorInfo(e,t,n);return Jr().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new BK(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new TK(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[yu(e.shape),...bu(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[yu(t),...bu(t)],s=new nS(r,n),i=!0,o=this.runWebGLProgram(s,[a],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:a,dtype:r}=t,s=Kv(a),i;n?i=new W5(s):i=new B5(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:r,dataId:e}],r,null,o);return{dtype:r,shape:a,dataId:l.dataId}}runWebGLProgram(e,t,n,a,r=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Ip.DENSE){let m=Tp(e.outputShape);i.texShape=m.map(f=>f*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),k.sizeFromShape(s.shape)===0)return i.values=k.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 f=this.texData.get(m.dataId);if(f.texture==null){if(!e.packedInputs&&k.sizeFromShape(m.shape)<=ee().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:f.values};e.packedInputs&&(f.isPacked=!0,f.shape=m.shape)}else if(!!f.isPacked!=!!e.packedInputs)m=f.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),f=this.texData.get(m.dataId);else if(f.isPacked&&!nf(f.shape,m.shape)){let g=m,y=m.shape;m.shape=f.shape,m=this.packedReshape(m,y),o.push(m),f=this.texData.get(m.dataId),g.shape=y}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:f,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=K8(e,l,c),p=this.getAndSaveBinary(u,()=>j8(this.gpgpu,e,l,c)),d=this.activeTimers!=null,h;if(d&&(h=this.startTimer()),q8(this.gpgpu,p,l,c,a),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)})),!ee().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,a,r=!1){n=n||t[0].dtype;let s=this.runWebGLProgram(e,t,n,a,r);return Jr().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(ee().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=D(()=>{if(!ee().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=ee().getBool("DEBUG");ee().set("DEBUG",!1);let t=this.abs(de(1e-8)).dataSync()[0];if(ee().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?VK:UK}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=k.now());let u=t.texShape;if(u==null&&(u=$5(n,o),t.texShape=u),r!=null){let p=Kv(n),d,h=u[1],m=u[0],f=r instanceof Uint8Array;o?([h,m]=gu(u[0],u[1]),d=new H5(p,[m,h],f)):d=new G5(p,[m,h],f);let g=this.makeTensorInfo([m,h],a);f?this.texData.get(g.dataId).usage=ea.PIXELS:this.texData.get(g.dataId).usage=ea.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,m,r);let y=!0,b=this.runWebGLProgram(d,[g],a,null,y),v=this.texData.get(b.dataId);t.texture=v.texture,t.texShape=v.texShape,t.isPacked=v.isPacked,t.usage=v.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(b.dataId),t.values=null,l&&(this.uploadWaitMs+=k.now()-c)}else{let p=this.acquireTexture(u,i,a,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=KK(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}};function KK(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 a=0;a<n.length;++a)n[a]=Math.round(e[a]);return n}else throw new Error(`Unknown dtype ${t}`)}var YK="3.0.0";bh.isBrowser()&&Nh("webgl",()=>new XK,2);var oS=`
+    `}},HK=er.whereImpl,jK=1e-7,qK=1e-4,sw={};function KK(e){return e in sw||(sw[e]={}),sw[e]}var XK=128,YK=600;function ZK(){return ee().global.screen==null?1024:ee().global.screen.height*ee().global.screen.width*window.devicePixelRatio*YK/1024/1024}var iw=class extends pc{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!ee().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=or(ee().getNumber("WEBGL_VERSION"));this.binaryCache=KK(ee().getNumber("WEBGL_VERSION")),this.gpgpu=new h8(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 $K(this.gpgpu),this.numMBBeforeWarning=ZK(),this.texData=new Rd(this,qa())}nextDataId(){return iw.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((ee().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||ee().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 a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:ea.UPLOAD,refCount:1}),a}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,a,r){if(ee().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:ea.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let p;o?p=new Iu(i,of):p=new ms(i,of);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,c;l&&(c=k.now());let u;if(a==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);u=_.mergeRealAndImagArrays(p,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=k.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Iu(a,of):h=new ms(a,of);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!ee().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&ee().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"&&ee().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...Sp(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];u=_.mergeRealAndImagArrays(m,f)}else if(l==null)u=this.getValuesFromTexture(e);else{let h=k.sizeFromShape(a);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}c!=null&&this.disposeIntermediateTensorInfo(c);let p=this.convertAndCacheOnCPU(e,u),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&qa().removeDataId(e,this),this.pendingDeletes--),p}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!b5(n))throw ee().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=k.sizeFromShape(t);if(ee().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...Sp(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let s=ee().getBool("WEBGL_PACK")&&a===!0,i=s?Qv(t):t,o=s?new q5(i):new j5(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${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 ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(ee().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:a,usage:r,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(a,n),this.textureManager.releaseTexture(t,a,r,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 ee().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=qa().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=XK){let n=this.getCPUBackend();return!ee().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(a=>this.texData.get(a.dataId).texture==null&&k.sizeFromShape(a.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){_.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return HK(e.shape,t)}packedUnaryOp(e,t,n){let a=new Iu(e.shape,t),r=this.compileAndRun(a,[e],n);return qa().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let a=iS(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,a)}if(ee().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,dS,e.dtype);let t=new ms(e.shape,dS),n=this.compileAndRun(t,[e]);return qa().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:a}=this.makeTensorInfo(e,t,n);return qa().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new GK(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new EK(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[yu(e.shape),...bu(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[yu(t),...bu(t)],s=new lS(r,n),i=!0,o=this.runWebGLProgram(s,[a],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:a,dtype:r}=t,s=Qv(a),i;n?i=new H5(s):i=new G5(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:r,dataId:e}],r,null,o);return{dtype:r,shape:a,dataId:l.dataId}}runWebGLProgram(e,t,n,a,r=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Np.DENSE){let m=Sp(e.outputShape);i.texShape=m.map(f=>f*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),k.sizeFromShape(s.shape)===0)return i.values=k.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 f=this.texData.get(m.dataId);if(f.texture==null){if(!e.packedInputs&&k.sizeFromShape(m.shape)<=ee().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:f.values};e.packedInputs&&(f.isPacked=!0,f.shape=m.shape)}else if(!!f.isPacked!=!!e.packedInputs)m=f.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),f=this.texData.get(m.dataId);else if(f.isPacked&&!sf(f.shape,m.shape)){let g=m,y=m.shape;m.shape=f.shape,m=this.packedReshape(m,y),o.push(m),f=this.texData.get(m.dataId),g.shape=y}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:f,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=J8(e,l,c),p=this.getAndSaveBinary(u,()=>Y8(this.gpgpu,e,l,c)),d=this.activeTimers!=null,h;if(d&&(h=this.startTimer()),Z8(this.gpgpu,p,l,c,a),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)})),!ee().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(ee().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=D(()=>{if(!ee().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=ee().getBool("DEBUG");ee().set("DEBUG",!1);let t=this.abs(de(1e-8)).dataSync()[0];if(ee().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?jK:qK}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=k.now());let u=t.texShape;if(u==null&&(u=P5(n,o),t.texShape=u),r!=null){let p=Qv(n),d,h=u[1],m=u[0],f=r instanceof Uint8Array;o?([h,m]=gu(u[0],u[1]),d=new X5(p,[m,h],f)):d=new K5(p,[m,h],f);let g=this.makeTensorInfo([m,h],a);f?this.texData.get(g.dataId).usage=ea.PIXELS:this.texData.get(g.dataId).usage=ea.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,m,r);let y=!0,b=this.runWebGLProgram(d,[g],a,null,y),v=this.texData.get(b.dataId);t.texture=v.texture,t.texShape=v.texShape,t.isPacked=v.isPacked,t.usage=v.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(b.dataId),t.values=null,l&&(this.uploadWaitMs+=k.now()-c)}else{let p=this.acquireTexture(u,i,a,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=JK(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}};iw.nextDataId=0;function JK(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 a=0;a<n.length;++a)n[a]=Math.round(e[a]);return n}else throw new Error(`Unknown dtype ${t}`)}var QK="3.1.0";wh.isBrowser()&&Ch("webgl",()=>new iw,2);var hS=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `,Nu=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.userCode=`
@@ -964,12 +964,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float b = getBAtOutCoords();
         setOutput(binaryOperation(a, b));
       }
-    `}},rf=`
+    `}},lf=`
   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;
-`,_p=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=_.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length,s="";if(a)if(r===0||k.sizeFromShape(this.outputShape)===1)s=`
+`,Ep=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=_.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length,s="";if(a)if(r===0||k.sizeFromShape(this.outputShape)===1)s=`
           result.y = 0.;
           result.z = 0.;
           result.w = 0.;
@@ -1001,21 +1001,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function Bn(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var ZK={kernelName:sl,backendName:"webgl",kernelFunc:Bn};function fs(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=Bn({inputs:{x:a},backend:n}),l=n.texData.get(o.dataId);l.complexParentRefCount++;let c=Bn({inputs:{x:r},backend:n}),u=n.texData.get(c.dataId);return u.complexParentRefCount++,i.complexTensorInfos={real:o,imag:c},s}var JK={kernelName:Bd,backendName:"webgl",kernelFunc:fs},lS="return (a < 0.) ? b * a : a;",uS=`
+    `}};function Wn(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var eX={kernelName:Xs,backendName:"webgl",kernelFunc:Wn};function fs(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=Wn({inputs:{x:a},backend:n}),l=Wn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var tX={kernelName:Ud,backendName:"webgl",kernelFunc:fs},mS="return (a < 0.) ? b * a : a;",fS=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function QK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _p(uS,r.shape,i.shape):new Nu(lS,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),l}var eX={kernelName:Ks,backendName:"webgl",kernelFunc:QK},cS="return (a < 0.) ? b * a : a;",pS=`
+`;function nX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ep(fS,r.shape,i.shape):new Nu(mS,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),l}var aX={kernelName:Ys,backendName:"webgl",kernelFunc:nX},gS="return (a < 0.) ? b * a : a;",yS=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function tX(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _p(pS,a.shape,r.shape):new Nu(cS,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var nX={kernelName:ii,backendName:"webgl",kernelFunc:tX},dS="if (isnan(x)) return x;",aX=`
+`;function rX(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ep(yS,a.shape,r.shape):new Nu(gS,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var sX={kernelName:li,backendName:"webgl",kernelFunc:rX},bS="if (isnan(x)) return x;",iX=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,rX=`
+`,oX=`
   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 Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,l);return o.makeTensorInfo(i.shape,l,d)}let c=ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new Iu(i.shape,t):u=new ms(i.shape,e),o.runWebGLProgram(u,[i],l)}}function an({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(a&&l.dtype==="complex64"){let m=u.texData.get(l.dataId),f=u.texData.get(c.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(v=>{let[x,N]=v,T={dataId:x.dataId,dtype:x.dtype,shape:l.shape},C={dataId:N.dataId,dtype:N.dtype,shape:c.shape},$=new Nu(e,l.shape,c.shape);return u.runWebGLProgram($,[T,C],ca(x.dtype,N.dtype))}),b=fs({inputs:{real:g,imag:y},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(y),b}let p=s||ca(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&r!=null){let m=u.texData.get(l.dataId),f=u.texData.get(c.dataId),[g,y]=r(l.shape,c.shape,m.values,f.values,p),b=u.makeTensorInfo(y,p),v=u.texData.get(b.dataId);return v.values=g,b}let d=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new _p(t,l.shape,c.shape,n):h=new Nu(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],p)}}function sf(e,t=!1){if(e==="linear")return t?PK:$K;if(e==="relu")return t?LK:RK;if(e==="elu")return t?OK:DK;if(e==="relu6")return t?zK:MK;if(e==="prelu")return t?pS:cS;if(e==="leakyrelu")return t?uS:lS;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var hS=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=a?e[1]:e[2],u=Math.ceil(c/2),p=a?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) {
+`;function Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,l);return o.makeTensorInfo(i.shape,l,d)}let c=ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new Iu(i.shape,t):u=new ms(i.shape,e),o.runWebGLProgram(u,[i],l)}}function an({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(a&&l.dtype==="complex64"){let m=u.texData.get(l.dataId),f=u.texData.get(c.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(v=>{let[x,N]=v,T={dataId:x.dataId,dtype:x.dtype,shape:l.shape},C={dataId:N.dataId,dtype:N.dtype,shape:c.shape},F=new Nu(e,l.shape,c.shape);return u.runWebGLProgram(F,[T,C],pa(x.dtype,N.dtype))}),b=fs({inputs:{real:g,imag:y},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(y),b}let p=s||pa(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&r!=null){let m=u.texData.get(l.dataId),f=u.texData.get(c.dataId),[g,y]=r(l.shape,c.shape,m.values,f.values,p),b=u.makeTensorInfo(y,p),v=u.texData.get(b.dataId);return v.values=g,b}let d=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Ep(t,l.shape,c.shape,n):h=new Nu(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],p)}}function uf(e,t=!1){if(e==="linear")return t?BK:PK;if(e==="relu")return t?VK:LK;if(e==="elu")return t?WK:OK;if(e==="relu6")return t?UK:zK;if(e==="prelu")return t?yS:gS;if(e==="leakyrelu")return t?fS:mS;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var xS=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=a?e[1]:e[2],u=Math.ceil(c/2),p=a?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:l?f=`vec4 activation(vec4 a) {
@@ -1054,7 +1054,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}},mS={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},fS=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.userCode=`
+    `}},vS={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},wS=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1067,7 +1067,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},gS="return a * b;";function yS(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=_.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),c=new fS(mS.REAL,a.shape,r.shape),u=new fS(mS.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=fs({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[c,u]=cK(a.shape,r.shape,o.values,l.values,s),p=n.makeTensorInfo(u,s),d=n.texData.get(p.dataId);return d.values=c,p}let i;return ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new _p(gS,a.shape,r.shape):i=new Nu(gS,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var sX={kernelName:ni,backendName:"webgl",kernelFunc:yS};function iX(e,t,n){let a=[yu(e.shape),...bu(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[yu(t),...bu(t)],i=new nS(s,a),o=!0,l=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function be(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=k.sizeFromShape(r.shape),l=k.inferFromImplicitShape(s,o),c=k.sizeFromShape(l);k.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(r.dataId);return u.isPacked&&!nf(r.shape,l)&&!(u.texture!==null&&nf(u.shape,l))?iX(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var oX={kernelName:Il,backendName:"webgl",kernelFunc:be},bS=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,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 * ${k.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";r%n>0&&(c=`
+    `}},kS="return a * b;";function IS(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=_.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),c=new wS(vS.REAL,a.shape,r.shape),u=new wS(vS.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=fs({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[c,u]=mK(a.shape,r.shape,o.values,l.values,s),p=n.makeTensorInfo(u,s),d=n.texData.get(p.dataId);return d.values=c,p}let i;return ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Ep(kS,a.shape,r.shape):i=new Nu(kS,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var lX={kernelName:ri,backendName:"webgl",kernelFunc:IS};function uX(e,t,n){let a=[yu(e.shape),...bu(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[yu(t),...bu(t)],i=new lS(s,a),o=!0,l=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function be(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=k.sizeFromShape(r.shape),l=k.inferFromImplicitShape(s,o),c=k.sizeFromShape(l);k.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(r.dataId);return u.isPacked&&!sf(r.shape,l)&&!(u.texture!==null&&sf(u.shape,l))?uX(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var cX={kernelName:Il,backendName:"webgl",kernelFunc:be},NS=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,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 * ${k.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";r%n>0&&(c=`
         if (inIdx < 0 || inIdx >= ${r}) {
           return 0.0;
         }
@@ -1120,7 +1120,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(sumValue);
       }
-    `}},lX=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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,p=`
+    `}},pX=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,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}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,p=`
       if (${t==="sum"}) {
         sumValue += dot(values, ones);
       } else if (${t==="prod"}) {
@@ -1205,12 +1205,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${l});
       }
-    `}};function uX(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=_.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function Ji(e,t,n,a){let r=uX(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:c}=r[i],u,p;n==="mean"?u=i===0?new bS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new bS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new lX({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),p=s,s=a.runWebGLProgram(u,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var pX=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 a=ft(this.rank),r=cX(t);this.userCode=`
+    `}};function dX(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=_.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function eo(e,t,n,a){let r=dX(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:c}=r[i],u,p;n==="mean"?u=i===0?new NS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new NS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new pX({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),p=s,s=a.runWebGLProgram(u,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var mX=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 a=ft(this.rank),r=hX(t);this.userCode=`
     void main() {
       ${a} resRC = getOutputCoords();
       setOutput(getA(${r}));
     }
-    `}};function cX(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"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var dX=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 ${this.rank} is not yet supported.`);let a=ft(this.rank),r=tS("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=r[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
+    `}};function hX(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"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var fX=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 ${this.rank} is not yet supported.`);let a=ft(this.rank),r=oS("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=r[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
     void main() {
       ${a} rc = getOutputCoords();
       vec4 result = vec4(0.);
@@ -1227,14 +1227,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function of(e,t,n){let a=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new dX(e.shape,t):new pX(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function hX(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,l=_.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=of(e,l,a),o=_.getInnerMostAxes(o.length,s)),_.assertAxesAreInnerMostDims("sum",o,s);let[p,d]=_.computeOutAndReduceShapes(u.shape,o),h=p;n&&(h=_.expandShapeToKeepDim(p,i));let m=k.sizeFromShape(d),f=k.sizeFromShape(e.shape)/m,g=be({inputs:{x:u},attrs:{shape:[f,m]},backend:a}),y=yh(e.dtype),b=Ji(g,y,"sum",a),v=be({inputs:{x:b},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(b),c&&a.disposeIntermediateTensorInfo(u),v}function tw(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return hX(r,s,i,n)}var mX={kernelName:gi,backendName:"webgl",kernelFunc:tw};function Sn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=r.shape[s[u]];let c;if(i.shouldExecuteOnCPU([r])){let u=i.texData.get(r.dataId).values,p=Qv(u,r.shape,r.dtype,s,l);c=i.makeTensorInfo(l,r.dtype);let d=i.texData.get(c.dataId);d.values=p}else c=of(r,s,i);return c}var fX={kernelName:wi,backendName:"webgl",kernelFunc:Sn},xS=1e3;function lf({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,p=n?e.shape[c-2]:e.shape[c-1],d=a?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],m=a?t.shape[u-2]:t.shape[u-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=k.sizeFromShape(f),b=k.sizeFromShape(g),v=y===b||y===1||b===1;k.assert(c>=2&&u>=2&&v,()=>`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 (${f}) and (${g}).`);let x=(y>b?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);k.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let N=n?[y,p,h]:[y,h,p],T=a?[b,m,d]:[b,d,m],C=be({inputs:{x:e},backend:r,attrs:{shape:N}}),$=be({inputs:{x:t},backend:r,attrs:{shape:T}}),F=[C,$],O=Math.max(y,b),B=n?C.shape[1]:C.shape[2],W=s!=null,G=i!=null,q=l==="leakyrelu",H=l!=null?sf(l,!0):null,K=W||G||q||H!=null,X;if((h===1||m===1)&&B>xS&&K===!1){let J=C,se=$;n&&(J=Sn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),F.push(J)),a&&(se=Sn({inputs:{x:$},backend:r,attrs:{perm:[0,2,1]}}),F.push(se));let Q=m!==1,oe=m===1,ae=J;Q&&(ae=be({inputs:{x:J},backend:r,attrs:{shape:[O,B,1]}}),F.push(ae));let pe=m===1?2:1,ue=se;oe&&(ue=be({inputs:{x:se},backend:r,attrs:{shape:[O,1,B]}}),F.push(ue));let ge=yS({inputs:{a:ae,b:ue},backend:r});X=tw({inputs:{x:ge},backend:r,attrs:{axis:pe,keepDims:!0}}),F.push(ge)}else{let J=ca(e.dtype,t.dtype),se=new hS(N,T,[O,h,m],n,a,W,H,G,q),Q=[C,$];if(s!=null&&Q.push(s),G&&Q.push(i),q){let oe=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));Q.push(oe),F.push(oe)}X=r.runWebGLProgram(se,Q,J)}let ne=be({inputs:{x:X},backend:r,attrs:{shape:x}});F.push(X);for(let J of F)r.disposeIntermediateTensorInfo(J);return ne}function gX(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:p}=a;return lf({a:r,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:u})}var yX={kernelName:ki,backendName:"webgl",kernelFunc:gX},vS="return abs(x);";function bX(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=eS(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Iu(a.shape,vS):r=new ms(a.shape,vS),n.runWebGLProgram(r,[a],a.dtype)}var xX={kernelName:Oo,backendName:"webgl",kernelFunc:bX},vX=Ma+`
+    `}};function cf(e,t,n){let a=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fX(e.shape,t):new mX(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function gX(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,l=_.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=cf(e,l,a),o=_.getInnerMostAxes(o.length,s)),_.assertAxesAreInnerMostDims("sum",o,s);let[p,d]=_.computeOutAndReduceShapes(u.shape,o),h=p;n&&(h=_.expandShapeToKeepDim(p,i));let m=k.sizeFromShape(d),f=k.sizeFromShape(e.shape)/m,g=be({inputs:{x:u},attrs:{shape:[f,m]},backend:a}),y=vh(e.dtype),b=eo(g,y,"sum",a),v=be({inputs:{x:b},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(b),c&&a.disposeIntermediateTensorInfo(u),v}function ow(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return gX(r,s,i,n)}var yX={kernelName:bi,backendName:"webgl",kernelFunc:ow};function Sn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=r.shape[s[u]];let c;if(i.shouldExecuteOnCPU([r])){let u=i.texData.get(r.dataId).values,p=rw(u,r.shape,r.dtype,s,l);c=i.makeTensorInfo(l,r.dtype);let d=i.texData.get(c.dataId);d.values=p}else c=cf(r,s,i);return c}var bX={kernelName:Ii,backendName:"webgl",kernelFunc:Sn},TS=1e3;function pf({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,p=n?e.shape[c-2]:e.shape[c-1],d=a?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],m=a?t.shape[u-2]:t.shape[u-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=k.sizeFromShape(f),b=k.sizeFromShape(g),v=y===b||y===1||b===1;k.assert(c>=2&&u>=2&&v,()=>`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 (${f}) and (${g}).`);let x=(y>b?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);k.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let N=n?[y,p,h]:[y,h,p],T=a?[b,m,d]:[b,d,m],C=be({inputs:{x:e},backend:r,attrs:{shape:N}}),F=be({inputs:{x:t},backend:r,attrs:{shape:T}}),$=[C,F],P=Math.max(y,b),B=n?C.shape[1]:C.shape[2],W=s!=null,G=i!=null,q=l==="leakyrelu",j=l!=null?uf(l,!0):null,X=W||G||q||j!=null,K;if((h===1||m===1)&&B>TS&&X===!1){let J=C,se=F;n&&(J=Sn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),$.push(J)),a&&(se=Sn({inputs:{x:F},backend:r,attrs:{perm:[0,2,1]}}),$.push(se));let Q=m!==1,oe=m===1,ae=J;Q&&(ae=be({inputs:{x:J},backend:r,attrs:{shape:[P,B,1]}}),$.push(ae));let pe=m===1?2:1,ue=se;oe&&(ue=be({inputs:{x:se},backend:r,attrs:{shape:[P,1,B]}}),$.push(ue));let ge=IS({inputs:{a:ae,b:ue},backend:r});K=ow({inputs:{x:ge},backend:r,attrs:{axis:pe,keepDims:!0}}),$.push(ge)}else{let J=pa(e.dtype,t.dtype),se=new xS(N,T,[P,h,m],n,a,W,j,G,q),Q=[C,F];if(s!=null&&Q.push(s),G&&Q.push(i),q){let oe=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));Q.push(oe),$.push(oe)}K=r.runWebGLProgram(se,Q,J)}let ne=be({inputs:{x:K},backend:r,attrs:{shape:x}});$.push(K);for(let J of $)r.disposeIntermediateTensorInfo(J);return ne}function xX(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:p}=a;return pf({a:r,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:u})}var vX={kernelName:Ni,backendName:"webgl",kernelFunc:xX},SS="return abs(x);";function wX(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=iS(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Iu(a.shape,SS):r=new ms(a.shape,SS),n.runWebGLProgram(r,[a],a.dtype)}var kX={kernelName:zo,backendName:"webgl",kernelFunc:wX},IX=Pa+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,wX=Ze({opSnippet:vX}),kX={kernelName:Lo,backendName:"webgl",kernelFunc:wX},IX=Ma+`
+`,NX=Ze({opSnippet:IX}),TX={kernelName:Bo,backendName:"webgl",kernelFunc:NX},SX=Pa+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,NX=Ze({opSnippet:IX}),TX={kernelName:zo,backendName:"webgl",kernelFunc:NX},wS="return a + b;",SX=an({opSnippet:wS,packedOpSnippet:wS,supportsComplex:!0,cpuKernelImpl:X8}),CX={kernelName:Vr,backendName:"webgl",kernelFunc:SX},_X=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,CX=Ze({opSnippet:SX}),_X={kernelName:Wo,backendName:"webgl",kernelFunc:CX},CS="return a + b;",EX=an({opSnippet:CS,packedOpSnippet:CS,supportsComplex:!0,cpuKernelImpl:Q8}),AX={kernelName:Ur,backendName:"webgl",kernelFunc:EX},FX=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1242,7 +1242,7 @@ return log(x + sqrt(x * x - 1.0));`,NX=Ze({opSnippet:IX}),TX={kernelName:zo,back
         float result = ${a};
         setOutput(result);
       }
-    `}},EX=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
+    `}},$X=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1250,7 +1250,7 @@ return log(x + sqrt(x * x - 1.0));`,NX=Ze({opSnippet:IX}),TX={kernelName:zo,back
         vec4 result = ${a};
         setOutput(result);
       }
-    `}};function uf(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Bn({inputs:{x:a[0]},backend:n});if(a.length>ee().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=uf({inputs:a.slice(0,o),backend:n}),c=uf({inputs:a.slice(o),backend:n});return uf({inputs:[l,c],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ca(o,l)),s=a.map(o=>o.shape),i=ee().getBool("WEBGL_PACK")?new EX(a[0].shape,s):new _X(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var AX={kernelName:$s,backendName:"webgl",kernelFunc:uf};function FX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=r;u!=null&&(p=Sn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,o)),_.assertAxesAreInnerMostDims("all",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=be({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Ji(f,f.dtype,"all",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=be({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var $X={kernelName:Md,backendName:"webgl",kernelFunc:FX};function DX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=r;u!=null&&(p=Sn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,o)),_.assertAxesAreInnerMostDims("any",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=be({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Ji(f,f.dtype,"any",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=be({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var RX={kernelName:Pd,backendName:"webgl",kernelFunc:DX},MX=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function df(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Wn({inputs:{x:a[0]},backend:n});if(a.length>ee().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=df({inputs:a.slice(0,o),backend:n}),c=df({inputs:a.slice(o),backend:n});return df({inputs:[l,c],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>pa(o,l)),s=a.map(o=>o.shape),i=ee().getBool("WEBGL_PACK")?new $X(a[0].shape,s):new FX(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var DX={kernelName:$s,backendName:"webgl",kernelFunc:df};function RX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=r;u!=null&&(p=Sn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,o)),_.assertAxesAreInnerMostDims("all",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=be({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=eo(f,f.dtype,"all",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=be({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var MX={kernelName:Ld,backendName:"webgl",kernelFunc:RX};function PX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=r;u!=null&&(p=Sn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,o)),_.assertAxesAreInnerMostDims("any",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=be({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=eo(f,f.dtype,"any",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=be({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var OX={kernelName:zd,backendName:"webgl",kernelFunc:PX},LX=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,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];
@@ -1270,7 +1270,7 @@ return log(x + sqrt(x * x - 1.0));`,NX=Ze({opSnippet:IX}),TX={kernelName:zo,back
         }
         setOutput(float(bestIndex));
       }
-    `}},PX=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ft(o),c=fn("coords",o),u,p;if(s===1){p=o+1;let C=ft(p);u=`
+    `}},zX=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ft(o),c=fn("coords",o),u,p;if(s===1){p=o+1;let C=ft(p);u=`
         ${C} sourceLocR = ${C}(${c.join()}, 0);
         ++${c[o-1]};
         ${C} sourceLocG = ${C}(${c.join()}, 0);
@@ -1332,23 +1332,23 @@ return log(x + sqrt(x * x - 1.0));`,NX=Ze({opSnippet:IX}),TX={kernelName:zo,back
         }
         setOutput(bestIndex);
       }
-    `}};function kS(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=_.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new MX(o,n,a==null),c=[t];a!=null&&c.push(a);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let p=kS(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}function IS(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=_.computeOptimalWindowSize(s),o=new PX(r,i,n,a==null),l=a==null?[t]:[t,a],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=IS(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function NS(e,t,n,a){let r=[n];if(_.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!ee().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=_.computeOutAndReduceShapes(t.shape,r),l=k.sizeFromShape(o),c=be({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=kS(e,c,a);s.push(u);let p=be({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),p}return IS(e,t,a)}function OX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=NS(n,l,i[0],"max");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var LX={kernelName:Ds,backendName:"webgl",kernelFunc:OX};function zX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=NS(n,l,i[0],"min");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var BX={kernelName:fc,backendName:"webgl",kernelFunc:zX},WX=Ma+`
+    `}};function _S(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=_.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new LX(o,n,a==null),c=[t];a!=null&&c.push(a);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let p=_S(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}function ES(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=_.computeOptimalWindowSize(s),o=new zX(r,i,n,a==null),l=a==null?[t]:[t,a],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=ES(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function AS(e,t,n,a){let r=[n];if(_.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!ee().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=_.computeOutAndReduceShapes(t.shape,r),l=k.sizeFromShape(o),c=be({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=_S(e,c,a);s.push(u);let p=be({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),p}return ES(e,t,a)}function BX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=AS(n,l,i[0],"max");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var WX={kernelName:Ds,backendName:"webgl",kernelFunc:BX};function VX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=AS(n,l,i[0],"min");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var UX={kernelName:mc,backendName:"webgl",kernelFunc:VX},GX=Pa+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,VX=Ze({opSnippet:WX}),UX={kernelName:Bo,backendName:"webgl",kernelFunc:VX},GX=Ma+"return log(x + sqrt(x * x + 1.0));",HX=Ze({opSnippet:GX}),jX={kernelName:Wo,backendName:"webgl",kernelFunc:HX},qX=Ma+`
+`,HX=Ze({opSnippet:GX}),jX={kernelName:Vo,backendName:"webgl",kernelFunc:HX},qX=Pa+"return log(x + sqrt(x * x + 1.0));",KX=Ze({opSnippet:qX}),XX={kernelName:Uo,backendName:"webgl",kernelFunc:KX},YX=Pa+`
   return atan(x);
-`,KX=Ze({opSnippet:qX}),XX={kernelName:Vo,backendName:"webgl",kernelFunc:KX},YX=aX+`
+`,ZX=Ze({opSnippet:YX}),JX={kernelName:Go,backendName:"webgl",kernelFunc:ZX},QX=iX+`
   return atan(a, b);
-`,ZX=`
+`,eY=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+rX+`
+  `+oX+`
   return result;
-`,JX=an({opSnippet:YX,packedOpSnippet:ZX}),QX={kernelName:Go,backendName:"webgl",kernelFunc:JX},eY=Ma+`
+`,tY=an({opSnippet:QX,packedOpSnippet:eY}),nY={kernelName:jo,backendName:"webgl",kernelFunc:tY},aY=Pa+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelName:Uo,backendName:"webgl",kernelFunc:tY},Ep=class{constructor(e,t,n,a=!1,r=!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,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,rY=Ze({opSnippet:aY}),sY={kernelName:Ho,backendName:"webgl",kernelFunc:rY},Ap=class{constructor(e,t,n,a=!1,r=!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,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${o});
         const ivec2 pads = ivec2(${d}, ${h});
 
@@ -1489,7 +1489,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         }
         setOutput(${v});
       }
-    `}},nw=class{constructor(e,t,n,a=!1,r=!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,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",v="0.0";if(b||(v="-1.0 / 1e-20"),n){let F=">=";this.userCode=`
+    `}},lw=class{constructor(e,t,n,a=!1,r=!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,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",v="0.0";if(b||(v="-1.0 / 1e-20"),n){let $=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${i}, ${o}, ${l});
         const ivec3 pads = ivec3(${f}, ${g}, ${y});
@@ -1540,7 +1540,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
                 // use the current value.
                 float currMinMaxValue = mix(
                     value, minMaxValue, minMaxValueFound);
-                if (value ${F} currMinMaxValue) {
+                if (value ${$} currMinMaxValue) {
                   minMaxValue = value;
                   minMaxValueFound = 1.0;
                   minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} +
@@ -1551,7 +1551,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
           }
           setOutput(float(minMaxPosition));
         }
-      `;return}let x="max",N=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(N="avgValue / count");let T=Math.floor(s/4)*4,C=s%4,$=`
+      `;return}let x="max",N=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(N="avgValue / count");let T=Math.floor(s/4)*4,C=s%4,F=`
       if (${b}) {
         avgValue += dot(values, ones);
       } else {
@@ -1616,7 +1616,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
                 getValue(batch, xD, xR, xC + 3 * ${p}, ch)
               );
 
-              ${$}
+              ${F}
             }
 
             int xC = xCCorner + ${T};
@@ -1628,7 +1628,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
                 initializationValue
               );
 
-              ${$}
+              ${F}
             } else if (${C===2}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
@@ -1637,7 +1637,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
                 initializationValue
               );
 
-              ${$}
+              ${F}
             } else if (${C===3}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
@@ -1646,13 +1646,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
                 initializationValue
               );
 
-              ${$}
+              ${F}
             }
           }
           setOutput(${N});
         }
       }
-    `}};function aY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Sp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.computePool2DInfo(r.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Bn({inputs:{x:r},backend:n});let p=new Ep(u,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var rY={kernelName:Rs,backendName:"webgl",kernelFunc:aY};function sY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=a,u=[1,1,1],p=_.computePool3DInfo(r.shape,s,i,u,o,l,c),d=new nw(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var iY={kernelName:gc,backendName:"webgl",kernelFunc:sY},oY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=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,p=1/(t*n);this.userCode=`
+    `}};function iY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Cp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.computePool2DInfo(r.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Wn({inputs:{x:r},backend:n});let p=new Ap(u,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var oY={kernelName:Rs,backendName:"webgl",kernelFunc:iY};function lY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=a,u=[1,1,1],p=_.computePool3DInfo(r.shape,s,i,u,o,l,c),d=new lw(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var uY={kernelName:fc,backendName:"webgl",kernelFunc:lY},cY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=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,p=1/(t*n);this.userCode=`
       const ivec2 pads = ivec2(${c}, ${u});
       const float avgMultiplier = float(${p});
 
@@ -1694,7 +1694,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},lY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=u-1-e.padInfo.front,m=p-1-e.padInfo.top,f=d-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
+    `}},pY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=u-1-e.padInfo.front,m=p-1-e.padInfo.top,f=d-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
       const ivec3 pads = ivec3(${h}, ${m}, ${f});
       const float avgMultiplier = float(${g});
 
@@ -1750,7 +1750,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function uY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=a,p=[1,1,1],d=_.computePool3DInfo(i.shape,o,l,p,c,u),h=new lY(d);return n.runWebGLProgram(h,[r],i.dtype)}var cY={kernelName:Ld,backendName:"webgl",kernelFunc:uY};function pY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Sp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=a,u=_.computePool2DInfo(i.shape,o,l,1,c),p=new oY(u);return n.runWebGLProgram(p,[r],i.dtype)}var dY={kernelName:Od,backendName:"webgl",kernelFunc:pY};function hY(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return lf({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var mY={kernelName:Ms,backendName:"webgl",kernelFunc:hY},fY=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function dY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=a,p=[1,1,1],d=_.computePool3DInfo(i.shape,o,l,p,c,u),h=new pY(d);return n.runWebGLProgram(h,[r],i.dtype)}var hY={kernelName:Wd,backendName:"webgl",kernelFunc:dY};function mY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Cp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=a,u=_.computePool2DInfo(i.shape,o,l,1,c),p=new cY(u);return n.runWebGLProgram(p,[r],i.dtype)}var fY={kernelName:Bd,backendName:"webgl",kernelFunc:mY};function gY(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return pf({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var yY={kernelName:Ms,backendName:"webgl",kernelFunc:gY},bY=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -1760,7 +1760,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         float inv = scale * inversesqrt(variance + float(${s}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}},gY=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}},xY=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         vec4 offset = ${i};
         vec4 scale = ${o};
@@ -1773,7 +1773,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
 
         setOutput((x - mean) * inv + offset);
       }
-    `}},yY=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;k.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||r.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=[a,r,s],u=null;i!=null&&(u=i.shape,c.push(i));let p=null;o!=null&&(p=o.shape,c.push(o));let d=ee().getBool("WEBGL_PACK_NORMALIZATION")?new gY(a.shape,r.shape,s.shape,u,p,l):new fY(a.shape,r.shape,s.shape,u,p,l);return t.runWebGLProgram(d,c,c[0].dtype)},bY={kernelName:js,backendName:"webgl",kernelFunc:yY},vY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ft(this.rank),n=`uniform int start[${this.rank}];`,a=xY(this.rank),r,s=e.map((i,o)=>`sourceLoc.${aw[o]} = start[${o}] + coords.${aw[o]};`);r=`
+    `}},vY=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;k.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||r.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=[a,r,s],u=null;i!=null&&(u=i.shape,c.push(i));let p=null;o!=null&&(p=o.shape,c.push(o));let d=ee().getBool("WEBGL_PACK_NORMALIZATION")?new xY(a.shape,r.shape,s.shape,u,p,l):new bY(a.shape,r.shape,s.shape,u,p,l);return t.runWebGLProgram(d,c,c[0].dtype)},wY={kernelName:qs,backendName:"webgl",kernelFunc:vY},IY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ft(this.rank),n=`uniform int start[${this.rank}];`,a=kY(this.rank),r,s=e.map((i,o)=>`sourceLoc.${uw[o]} = start[${o}] + coords.${uw[o]};`);r=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${s.join(`
@@ -1784,7 +1784,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         ${r}
         setOutput(getSource(${a}));
       }
-    `}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)}}},aw=["x","y","z","w","u","v"];function xY(e){if(e===1)return"sourceLoc";if(e<=6)return aw.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var wY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ft(this.rank),n=fn("coords",this.rank),a=fn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${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)}}},uw=["x","y","z","w","u","v"];function kY(e){if(e===1)return"sourceLoc";if(e<=6)return uw.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var NY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ft(this.rank),n=fn("coords",this.rank),a=fn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=`
       result.x = ${s};
       if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
         ++${a[this.rank-1]};
@@ -1814,7 +1814,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={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 kY(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.complexParentRefCount=0,i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=pn.computeFlatOffset(t,k.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Ap(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=pn.parseSliceParams(r,s,i);if(pn.assertParamsValid(r,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=fK(p.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}let{isPacked:c}=n.texData.get(r.dataId),u=pn.isSliceContinous(r.shape,o,l);if(c||!u){let p=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new wY(l):new vY(l),d=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),kY(r,o,l,n)}var IY={kernelName:Cl,backendName:"webgl",kernelFunc:Ap},NY=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;k.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,v)=>b*v),l=_.getReshaped(r.shape,s,o),c=_.getPermuted(l.length,s.length),u=_.getReshapedPermuted(r.shape,s,o),p=_.getSliceBeginCoords(i,s.length),d=_.getSliceSize(u,i,s.length),h=[],m=be({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Sn({inputs:{x:m},backend:n,attrs:{perm:c}}),g=be({inputs:{x:f},backend:n,attrs:{shape:u}}),y=Ap({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y},TY={kernelName:yc,backendName:"webgl",kernelFunc:NY};function SY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),c=QT(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var CY={kernelName:zd,backendName:"webgl",kernelFunc:SY},_Y="return float(a != b);",TS=an({opSnippet:_Y,dtype:"bool"}),EY={kernelName:fl,backendName:"webgl",kernelFunc:TS};function Fp(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Bn({inputs:{x:r.complexTensorInfos.real},backend:n})}var AY={kernelName:ih,backendName:"webgl",kernelFunc:Fp},FY="return float(int(x));";function $Y(e,t){let n=new ms(e.shape,FY),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function rw(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Bn({inputs:{x:r},backend:n});let i=vt(r.shape),o=rw({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=fs({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Fp({inputs:{input:r},backend:n}),o=rw({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=Bn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return $Y(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=TS({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var DY={kernelName:Ps,backendName:"webgl",kernelFunc:rw},SS="return ceil(x);",RY=Ze({opSnippet:SS,packedOpSnippet:SS,cpuKernelImpl:Z8}),MY={kernelName:Ho,backendName:"webgl",kernelFunc:RY},PY=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${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 TY(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=pn.computeFlatOffset(t,k.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Fp(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=pn.parseSliceParams(r,s,i);if(pn.assertParamsValid(r,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=xK(p.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}let{isPacked:c}=n.texData.get(r.dataId),u=pn.isSliceContinous(r.shape,o,l);if(c||!u){let p=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new NY(l):new IY(l),d=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),TY(r,o,l,n)}var SY={kernelName:Cl,backendName:"webgl",kernelFunc:Fp},CY=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;k.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,v)=>b*v),l=_.getReshaped(r.shape,s,o),c=_.getPermuted(l.length,s.length),u=_.getReshapedPermuted(r.shape,s,o),p=_.getSliceBeginCoords(i,s.length),d=_.getSliceSize(u,i,s.length),h=[],m=be({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Sn({inputs:{x:m},backend:n,attrs:{perm:c}}),g=be({inputs:{x:f},backend:n,attrs:{shape:u}}),y=Fp({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y},_Y={kernelName:gc,backendName:"webgl",kernelFunc:CY};function EY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),c=sS(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var AY={kernelName:Vd,backendName:"webgl",kernelFunc:EY},FY="return float(a != b);",FS=an({opSnippet:FY,dtype:"bool"}),$Y={kernelName:fl,backendName:"webgl",kernelFunc:FS};function $p(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Wn({inputs:{x:r.complexTensorInfos.real},backend:n})}var DY={kernelName:uh,backendName:"webgl",kernelFunc:$p},RY="return float(int(x));";function MY(e,t){let n=new ms(e.shape,RY),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function cw(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Wn({inputs:{x:r},backend:n});let i=vt(r.shape),o=cw({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=fs({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=$p({inputs:{input:r},backend:n}),o=cw({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=Wn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return MY(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=FS({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var PY={kernelName:Ps,backendName:"webgl",kernelFunc:cw},$S="return ceil(x);",OY=Ze({opSnippet:$S,packedOpSnippet:$S,cpuKernelImpl:tK}),LY={kernelName:Os,backendName:"webgl",kernelFunc:OY},zY=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1827,7 +1827,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},OY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},BY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1841,7 +1841,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function LY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;ee().getBool("WEBGL_PACK_CLIP")?o=new OY(r.shape):o=new PY(r.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,l)}var zY={kernelName:Ur,backendName:"webgl",kernelFunc:LY},BY=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function WY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;ee().getBool("WEBGL_PACK_CLIP")?o=new BY(r.shape):o=new zY(r.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,l)}var VY={kernelName:Gr,backendName:"webgl",kernelFunc:WY},UY=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -1854,7 +1854,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function CS(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function WY(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new BY(a.shape),i=[CS(a,r.complexTensorInfos.real),CS(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var VY={kernelName:bc,backendName:"webgl",kernelFunc:WY},UY=class{constructor(e){this.outputShape=[],this.outputShape=_.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-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
+    `}};function DS(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function GY(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new UY(a.shape),i=[DS(a,r.complexTensorInfos.real),DS(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var HY={kernelName:yc,backendName:"webgl",kernelFunc:GY},jY=class{constructor(e){this.outputShape=[],this.outputShape=_.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${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-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -1863,18 +1863,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         ${n.join(`
         `)}
       }
-    `}},GY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=_.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=ft(a),s=fn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],c=i.slice(-2),u=i.join(),p=`if (${l} < ${o[0]}) {
+    `}},qY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=_.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=ft(a),s=fn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],c=i.slice(-2),u=i.join(),p=`if (${l} < ${o[0]}) {
         return getChannel(
             getT0(${u}), vec2(${c.join()}));
         }`;for(let m=1;m<o.length;m++){let f=o[m-1];p+=`
         if (${l} < ${o[m]}  && ${l} >= ${o[m-1]}) {
           return getChannel(
-            getT${m}(${cf(i,l,f)}),
-            vec2(${cf(c,l,f)}));
+            getT${m}(${hf(i,l,f)}),
+            vec2(${hf(c,l,f)}));
         }`}let d=o.length,h=o[o.length-1];p+=`
         return getChannel(
-          getT${d}(${cf(i,l,h)}),
-          vec2(${cf(c,l,h)}));`,this.userCode=`
+          getT${d}(${hf(i,l,h)}),
+          vec2(${hf(c,l,h)}));`,this.userCode=`
       float getValue(${i.map(m=>"int "+m)}) {
         ${p}
       }
@@ -1900,7 +1900,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         }
         setOutput(result);
       }
-    `}};function cf(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function pf(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Bn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var HY={kernelName:Qd,backendName:"webgl",kernelFunc:pf};function Tu(e,t,n){let a=e[0].dtype;if(a==="complex64"){let c=e.map(m=>Fp({inputs:{input:m},backend:n})),u=e.map(m=>pf({inputs:{input:m},backend:n})),p=Tu(c,t,n),d=Tu(u,t,n),h=fs({inputs:{real:p,imag:d},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),u.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(a==="string"){let{tensors2D:c,outShape:u}=_S(e,t,n),p=c.map(g=>({vals:n.readSync(g.dataId),shape:g.shape})),d=c[0].shape[0]===1,h=J8(p,u,a,d),m=_.computeOutShape(e.map(g=>g.shape),t),f=n.makeTensorInfo(m,a,h);return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),f}if(e.length>ee().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=Tu(e.slice(0,c),t,n),p=Tu(e.slice(c),t,n),d=Tu([u,p],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),d}if(ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new GY(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,a)}let{tensors2D:r,outShape:s}=_S(e,t,n),i=new UY(r.map(c=>c.shape)),o=n.runWebGLProgram(i,r,a);r.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=be({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function _S(e,t,n){let a=_.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>be({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function ES(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=_.computeOutShape(t.map(c=>c.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>k.sizeFromShape(c.shape)>0);if(o.length===1)return Bn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return _.assertParamsConsistent(l,s),Tu(o,s,n)}var jY={kernelName:jo,backendName:"webgl",kernelFunc:ES},AS=class{constructor(e,t=!1,n=null,a=!1,r=!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,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,b=f?3:1,v="",x="";n&&(a?v=`float activation(float a) {
+    `}};function hf(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function mf(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Wn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var KY={kernelName:nh,backendName:"webgl",kernelFunc:mf};function Tu(e,t,n){let a=e[0].dtype;if(a==="complex64"){let c=e.map(m=>$p({inputs:{input:m},backend:n})),u=e.map(m=>mf({inputs:{input:m},backend:n})),p=Tu(c,t,n),d=Tu(u,t,n),h=fs({inputs:{real:p,imag:d},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),u.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(a==="string"){let{tensors2D:c,outShape:u}=RS(e,t,n),p=c.map(g=>({vals:n.readSync(g.dataId),shape:g.shape})),d=c[0].shape[0]===1,h=nK(p,u,a,d),m=_.computeOutShape(e.map(g=>g.shape),t),f=n.makeTensorInfo(m,a,h);return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),f}if(e.length>ee().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=Tu(e.slice(0,c),t,n),p=Tu(e.slice(c),t,n),d=Tu([u,p],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),d}if(ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new qY(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,a)}let{tensors2D:r,outShape:s}=RS(e,t,n),i=new jY(r.map(c=>c.shape)),o=n.runWebGLProgram(i,r,a);r.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=be({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function RS(e,t,n){let a=_.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>be({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function MS(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=_.computeOutShape(t.map(c=>c.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>k.sizeFromShape(c.shape)>0);if(o.length===1)return Wn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return _.assertParamsConsistent(l,s),Tu(o,s,n)}var XY={kernelName:qo,backendName:"webgl",kernelFunc:MS},PS=class{constructor(e,t=!1,n=null,a=!1,r=!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,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,b=f?3:1,v="",x="";n&&(a?v=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:r?v=`float activation(float a) {
@@ -2034,7 +2034,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         ${x}
         setOutput(result);
       }
-    `}},qY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
+    `}},YY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
       const ivec3 strides = ivec3(${r}, ${s}, ${i});
       const ivec3 pads = ivec3(${t}, ${n}, ${a});
 
@@ -2122,7 +2122,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},KY=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:a,inChannels:r,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:p}=n,{left:d,top:h}=o,m=r*a,f=mn(),g=p==="channelsLast",y=g?0:1,b=g?1:2,v="";for(let x=0;x<=1;x++)for(let N=0;N<=1;N++)v+=`
+    `}},ZY=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:a,inChannels:r,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:p}=n,{left:d,top:h}=o,m=r*a,f=mn(),g=p==="channelsLast",y=g?0:1,b=g?1:2,v="";for(let x=0;x<=1;x++)for(let N=0;N<=1;N++)v+=`
           blockIndex = rc.y + ${N};
           pos = rc.x + ${x};
 
@@ -2166,7 +2166,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
 
         ${f.output} = result;
       }
-    `}};function FS({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=a.texData.get(e.dataId),u=n.inChannels,p=l[0]*l[1]*l[2],d=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[],b=(p===1||d===1)&&u>xS,v=l[2]%2!=0&&!!c.isPacked;if(b||!ee().getBool("WEBGL_LAZILY_UNPACK")||!ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!v){let x=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],N=be({inputs:{x:e},backend:a,attrs:{shape:[1,x,n.inChannels]}}),T=be({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),C=lf({a:N,b:T,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=be({inputs:{x:C},backend:a,attrs:{shape:n.outShape}}),y.push(N),y.push(T),y.push(C)}else{let x=h?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),N={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k.assert(nf(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);let C=be({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(C);let $=lf({a:N,b:C,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),F=a.texData.get($.dataId);k.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=T,F.shape=n.outShape,g=Bn({inputs:{x:$},backend:a}),g.shape=n.outShape,y.push($)}for(let x of y)a.disposeIntermediateTensorInfo(x);return g}function $S({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:p,outHeight:d,dataFormat:h}=n,m=h==="channelsLast",f=l*c*u,g=d*p,y=[f,g],b=!0,v=!1,x=[],N=be({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),T=be({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});x.push(N),x.push(T);let C=new KY(y,N.shape,n),$=a.runWebGLProgram(C,[N],"float32"),F=be({inputs:{x:$},backend:a,attrs:{shape:[1,y[0],y[1]]}});x.push($),x.push(F);let O=r!=null,B=s!=null,W=o==="leakyrelu",G=o?sf(o,!0):null,q=new hS(F.shape,T.shape,[1,g,n.outChannels],b,v,O,G,B,W),H=[F,T];if(r&&H.push(r),B&&H.push(s),W){let J=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));H.push(J),x.push(J)}let K=a.runWebGLProgram(q,H,"float32"),X=m?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],ne=be({inputs:{x:K},backend:a,attrs:{shape:X}});x.push(K);for(let J of x)a.disposeIntermediateTensorInfo(J);return ne}function XY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=a,p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!1,p),h;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"))h=FS({x:r,filter:s,convInfo:d,backend:n});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=$S({x:r,filter:s,convInfo:d,backend:n});else{let f=new AS(d);h=n.runWebGLProgram(f,[r,s],"float32")}let m=be({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),m}var YY={kernelName:Os,backendName:"webgl",kernelFunc:XY},ZY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function OS({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=a.texData.get(e.dataId),u=n.inChannels,p=l[0]*l[1]*l[2],d=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[],b=(p===1||d===1)&&u>TS,v=l[2]%2!=0&&!!c.isPacked;if(b||!ee().getBool("WEBGL_LAZILY_UNPACK")||!ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!v){let x=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],N=be({inputs:{x:e},backend:a,attrs:{shape:[1,x,n.inChannels]}}),T=be({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),C=pf({a:N,b:T,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=be({inputs:{x:C},backend:a,attrs:{shape:n.outShape}}),y.push(N),y.push(T),y.push(C)}else{let x=h?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),N={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k.assert(sf(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);let C=be({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(C);let F=pf({a:N,b:C,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),$=a.texData.get(F.dataId);k.assert($.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=T,$.shape=n.outShape,g=Wn({inputs:{x:F},backend:a}),g.shape=n.outShape,y.push(F)}for(let x of y)a.disposeIntermediateTensorInfo(x);return g}function LS({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:p,outHeight:d,dataFormat:h}=n,m=h==="channelsLast",f=l*c*u,g=d*p,y=[f,g],b=!0,v=!1,x=[],N=be({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),T=be({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});x.push(N),x.push(T);let C=new ZY(y,N.shape,n),F=a.runWebGLProgram(C,[N],"float32"),$=be({inputs:{x:F},backend:a,attrs:{shape:[1,y[0],y[1]]}});x.push(F),x.push($);let P=r!=null,B=s!=null,W=o==="leakyrelu",G=o?uf(o,!0):null,q=new xS($.shape,T.shape,[1,g,n.outChannels],b,v,P,G,B,W),j=[$,T];if(r&&j.push(r),B&&j.push(s),W){let J=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));j.push(J),x.push(J)}let X=a.runWebGLProgram(q,j,"float32"),K=m?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],ne=be({inputs:{x:X},backend:a,attrs:{shape:K}});x.push(X);for(let J of x)a.disposeIntermediateTensorInfo(J);return ne}function JY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=a,p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!1,p),h;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"))h=OS({x:r,filter:s,convInfo:d,backend:n});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=LS({x:r,filter:s,convInfo:d,backend:n});else{let f=new PS(d);h=n.runWebGLProgram(f,[r,s],"float32")}let m=be({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),m}var QY={kernelName:Ls,backendName:"webgl",kernelFunc:JY},e7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2208,7 +2208,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},JY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=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=`
+    `}},t7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=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() {
@@ -2261,7 +2261,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},QY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
+    `}},n7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -2303,7 +2303,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},e7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=a-1-e.padInfo.left;this.userCode=`
+    `}},a7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=a-1-e.padInfo.left;this.userCode=`
       const ivec3 pads = ivec3(${o}, ${l}, ${c});
 
       void main() {
@@ -2360,12 +2360,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function t7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=a,p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,u,i,1,o,c,!1,p),h=new ZY(d);return n.runWebGLProgram(h,[r,s],"float32")}var n7={kernelName:Wd,backendName:"webgl",kernelFunc:t7};function a7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=a,p=_.convertConv2DDataFormat(c),d=_.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),h=new JY(d);return n.runWebGLProgram(h,[r,s],"float32")}var r7={kernelName:Ls,backendName:"webgl",kernelFunc:a7};function s7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=_.computeConv3DInfo(r.shape,s.shape,i,l,o),u=new qY(c);return n.runWebGLProgram(u,[r,s],"float32")}var i7={kernelName:xc,backendName:"webgl",kernelFunc:s7};function o7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,c=_.computeConv3DInfo(r.shape,l,i,1,o),u=new QY(c);return n.runWebGLProgram(u,[r,s],"float32")}var l7={kernelName:Vd,backendName:"webgl",kernelFunc:o7};function u7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,c=_.computeConv3DInfo(l,s.shape,o,1,i),u=new e7(c);return n.runWebGLProgram(u,[r,s],"float32")}var c7={kernelName:Ud,backendName:"webgl",kernelFunc:u7},p7=dS+`
+    `}};function r7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=a,p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,u,i,1,o,c,!1,p),h=new e7(d);return n.runWebGLProgram(h,[r,s],"float32")}var s7={kernelName:Gd,backendName:"webgl",kernelFunc:r7};function i7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=a,p=_.convertConv2DDataFormat(c),d=_.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),h=new t7(d);return n.runWebGLProgram(h,[r,s],"float32")}var o7={kernelName:zs,backendName:"webgl",kernelFunc:i7};function l7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=_.computeConv3DInfo(r.shape,s.shape,i,l,o),u=new YY(c);return n.runWebGLProgram(u,[r,s],"float32")}var u7={kernelName:bc,backendName:"webgl",kernelFunc:l7};function c7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,c=_.computeConv3DInfo(r.shape,l,i,1,o),u=new n7(c);return n.runWebGLProgram(u,[r,s],"float32")}var p7={kernelName:Hd,backendName:"webgl",kernelFunc:c7};function d7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,c=_.computeConv3DInfo(l,s.shape,o,1,i),u=new a7(c);return n.runWebGLProgram(u,[r,s],"float32")}var h7={kernelName:jd,backendName:"webgl",kernelFunc:d7},m7=bS+`
   return cos(x);
-`,d7=Ze({opSnippet:p7}),h7={kernelName:zs,backendName:"webgl",kernelFunc:d7},m7=`
+`,f7=Ze({opSnippet:m7}),g7={kernelName:Bs,backendName:"webgl",kernelFunc:f7},y7=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,f7=Ze({opSnippet:m7}),g7={kernelName:qo,backendName:"webgl",kernelFunc:f7},y7=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,p]=n;this.outputShape=[c,u,p,l];let d=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,v,x]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
+`,b7=Ze({opSnippet:y7}),x7={kernelName:Ko,backendName:"webgl",kernelFunc:b7},v7=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,p]=n;this.outputShape=[c,u,p,l];let d=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,v,x]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
       const float height_ratio = float(${f});
       const float width_ratio = float(${b});
       void main() {
@@ -2426,21 +2426,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
           setOutput(newValue);
         }
       }
-    `}},b7=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=a,u=new y7(r.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[r,s,i],"float32")},x7={kernelName:Ko,backendName:"webgl",kernelFunc:b7},MS=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${DS(a,"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 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
+    `}},w7=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=a,u=new v7(r.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[r,s,i],"float32")},k7={kernelName:Xo,backendName:"webgl",kernelFunc:w7},WS=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${zS(a,"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() {
         ${ft(a)} coords = getOutputCoords();
-        int end = ${RS(a,"coords")};
+        int end = ${BS(a,"coords")};
         float val = ${r};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${RS(a,"coords")} = idx;
-          val += getX(${DS(a,"coords")});
+          ${BS(a,"coords")} = idx;
+          val += getX(${zS(a,"coords")});
         }
         setOutput(val);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function DS(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 ${e} is not yet supported`)}function RS(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function v7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,c=_.getAxesPermutation([s],l),u=r;c!=null&&(u=Sn({inputs:{x:r},backend:n,attrs:{perm:c}}));let p=_.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let d=r.shape[p],h=Bn({inputs:{x:u},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new MS(u.shape,!1,o),g=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new MS(u.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(c!=null){let m=_.getUndoAxesPermutation(c),f=Sn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),f}return h}var w7={kernelName:Bs,backendName:"webgl",kernelFunc:v7};function k7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(s.dataId),u=QT(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=Y8(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var I7={kernelName:Gd,backendName:"webgl",kernelFunc:k7},N7=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 zS(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 ${e} is not yet supported`)}function BS(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function I7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,c=_.getAxesPermutation([s],l),u=r;c!=null&&(u=Sn({inputs:{x:r},backend:n,attrs:{perm:c}}));let p=_.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let d=u.shape[p],h=Wn({inputs:{x:u},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new WS(u.shape,!1,o),g=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new WS(u.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(c!=null){let m=_.getUndoAxesPermutation(c),f=Sn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),f}return h}var N7={kernelName:Ws,backendName:"webgl",kernelFunc:I7};function T7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(s.dataId),u=sS(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=eK(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var S7={kernelName:qd,backendName:"webgl",kernelFunc:T7},C7=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];
@@ -2459,7 +2459,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={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 T7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=new N7(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var S7={kernelName:Xo,backendName:"webgl",kernelFunc:T7},PS=class{constructor(e,t=!1,n=null,a=!1,r=!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,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,g="",y="";n&&(a?g=`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 _7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=new C7(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var E7={kernelName:Yo,backendName:"webgl",kernelFunc:_7},VS=class{constructor(e,t=!1,n=null,a=!1,r=!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,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,g="",y="";n&&(a?g=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:r?g=`float activation(float a) {
@@ -2515,7 +2515,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         ${y}
         setOutput(result);
       }
-    `}},OS=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=m,g="int xR; int xC; int xCOffset;";for(let x=0;x<h;x++)for(let N=0;N<m;N++)g+=`
+    `}},US=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=m,g="int xR; int xC; int xCOffset;";for(let x=0;x<h;x++)for(let N=0;N<m;N++)g+=`
           vec4 xTexelR${x}C${N*2} = vec4(0.);
           vec4 wR${x}C${N} = vec4(0.);
           vec4 xR${x}C${N} = vec4(0.);`;for(let x=0;x<h;x++)for(let N=0;N<f;N++){let T=N*2;if(g+=`
@@ -2668,7 +2668,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         ${b}
         setOutput(result);
       }
-    `}};function C7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=a,u=l;u==null&&(u=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=_.computeConv2DInfo(r.shape,s.shape,i,u,o,c,!0),d;return ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?d=new OS(p):d=new PS(p),n.runWebGLProgram(d,[r,s],"float32")}var _7={kernelName:Ws,backendName:"webgl",kernelFunc:C7},E7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
+    `}};function A7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=a,u=l;u==null&&(u=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=_.computeConv2DInfo(r.shape,s.shape,i,u,o,c,!0),d;return ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?d=new US(p):d=new VS(p),n.runWebGLProgram(d,[r,s],"float32")}var F7={kernelName:Vs,backendName:"webgl",kernelFunc:A7},$7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2703,7 +2703,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},A7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
+    `}},D7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
       const ivec2 pads = ivec2(${s}, ${i});
 
       void main() {
@@ -2748,13 +2748,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function F7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=a,p=_.computeConv2DInfo(r.shape,u,i,o,l,c,!0),d=new E7(p);return n.runWebGLProgram(d,[r,s],"float32")}var $7={kernelName:Hd,backendName:"webgl",kernelFunc:F7};function D7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=a,p=_.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new A7(p);return n.runWebGLProgram(d,[r,s],"float32")}var R7={kernelName:jd,backendName:"webgl",kernelFunc:D7},M7=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function R7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=a,p=_.computeConv2DInfo(r.shape,u,i,o,l,c,!0),d=new $7(p);return n.runWebGLProgram(d,[r,s],"float32")}var M7={kernelName:Kd,backendName:"webgl",kernelFunc:R7};function P7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=a,p=_.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new D7(p);return n.runWebGLProgram(d,[r,s],"float32")}var O7={kernelName:Xd,backendName:"webgl",kernelFunc:P7},L7=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
       void main() {
           ivec2 coords = getOutputCoords();
           float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
           setOutput(val);
       }
-    `}};function P7(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=be({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new M7(s),l=n.runWebGLProgram(o,[i],i.dtype),c=be({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var O7={kernelName:qd,backendName:"webgl",kernelFunc:P7},L7=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:p}=a;this.userCode=`
+    `}};function z7(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=be({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new L7(s),l=n.runWebGLProgram(o,[i],i.dtype),c=be({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var B7={kernelName:Yd,backendName:"webgl",kernelFunc:z7},W7=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:p}=a;this.userCode=`
       const ivec2 strides = ivec2(${r}, ${s});
       const ivec2 pads = ivec2(${u}, ${p});
       const float neg_infinity = -3.4e38;
@@ -2792,7 +2792,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         float result = curVal;
         setOutput(result);
       }
-    `}};function z7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=_.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),u,p=new L7(c);u=n.runWebGLProgram(p,[r,s],"float32");let d=be({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var B7={kernelName:vc,backendName:"webgl",kernelFunc:z7},W7="return (x >= 0.0) ? x : (exp(x) - 1.0);",V7=`
+    `}};function V7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=_.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),u,p=new W7(c);u=n.runWebGLProgram(p,[r,s],"float32");let d=be({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var U7={kernelName:xc,backendName:"webgl",kernelFunc:V7},G7="return (x >= 0.0) ? x : (exp(x) - 1.0);",H7=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -2801,12 +2801,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,U7=Ze({opSnippet:W7,packedOpSnippet:V7}),G7={kernelName:Yo,backendName:"webgl",kernelFunc:U7},H7="return (b >= 1.0) ? a : a * (b + 1.0);",j7=`
+`,j7=Ze({opSnippet:G7,packedOpSnippet:H7}),q7={kernelName:Zo,backendName:"webgl",kernelFunc:j7},K7="return (b >= 1.0) ? a : a * (b + 1.0);",X7=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,q7=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _p(j7,a.shape,r.shape):new Nu(H7,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},K7={kernelName:Yd,backendName:"webgl",kernelFunc:q7},X7=`
+`,Y7=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ep(X7,a.shape,r.shape):new Nu(K7,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},Z7={kernelName:Qd,backendName:"webgl",kernelFunc:Y7},J7=`
   return vec4(equal(a, b));
-`,Y7="return float(a == b);",Z7=an({opSnippet:Y7,packedOpSnippet:X7,dtype:"bool"}),J7={kernelName:Jo,backendName:"webgl",kernelFunc:Z7},Q7=`
+`,Q7="return float(a == b);",eZ=an({opSnippet:Q7,packedOpSnippet:J7,dtype:"bool"}),tZ={kernelName:Qo,backendName:"webgl",kernelFunc:eZ},nZ=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -2821,7 +2821,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={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));
-`,eZ=Ze({opSnippet:Q7}),tZ={kernelName:Zo,backendName:"webgl",kernelFunc:eZ},LS="return exp(x);",zS=Ze({opSnippet:LS,packedOpSnippet:LS,cpuKernelImpl:Q8}),nZ={kernelName:Us,backendName:"webgl",kernelFunc:zS};function sw(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),be({inputs:{x:s},backend:a,attrs:{shape:o}})}var aZ={kernelName:Qo,backendName:"webgl",kernelFunc:sw},BS="return exp(x) - 1.0;",rZ=Ze({opSnippet:BS,packedOpSnippet:BS,cpuKernelImpl:eK}),sZ={kernelName:el,backendName:"webgl",kernelFunc:rZ},WS=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.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 ${e}.`);this.userCode=`
+`,aZ=Ze({opSnippet:nZ}),rZ={kernelName:Jo,backendName:"webgl",kernelFunc:aZ},GS="return exp(x);",HS=Ze({opSnippet:GS,packedOpSnippet:GS,cpuKernelImpl:aK}),sZ={kernelName:Gs,backendName:"webgl",kernelFunc:HS};function pw(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),be({inputs:{x:s},backend:a,attrs:{shape:o}})}var iZ={kernelName:el,backendName:"webgl",kernelFunc:pw},jS="return exp(x) - 1.0;",oZ=Ze({opSnippet:jS,packedOpSnippet:jS,cpuKernelImpl:rK}),lZ={kernelName:tl,backendName:"webgl",kernelFunc:oZ},qS=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.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 = ${r};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -2854,13 +2854,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function VS(e,t,n){let a=n.texData.get(e.dataId),r=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=be({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new WS("real",l,t),u=new WS("imag",l,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=fs({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let f=be({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function iZ(e){let{inputs:t,backend:n}=e,{input:a}=t;return VS(a,!1,n)}var oZ={kernelName:Zd,backendName:"webgl",kernelFunc:iZ},lZ=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function KS(e,t,n){let a=n.texData.get(e.dataId),r=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=be({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new qS("real",l,t),u=new qS("imag",l,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=fs({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let f=be({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function uZ(e){let{inputs:t,backend:n}=e,{input:a}=t;return KS(a,!1,n)}var cZ={kernelName:eh,backendName:"webgl",kernelFunc:uZ},pZ=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 iw(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||k.inferDtype(r),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new lZ(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var uZ={kernelName:wc,backendName:"webgl",kernelFunc:iw},cZ=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 dw(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||k.inferDtype(r),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new pZ(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var dZ={kernelName:vc,backendName:"webgl",kernelFunc:dw},hZ=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];
@@ -2874,7 +2874,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
           }
           setOutput(outputValue);
         }
-    `}},pZ={kernelName:tl,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new cZ(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},US="return floor(x);",dZ=Ze({opSnippet:US,packedOpSnippet:US,cpuKernelImpl:tK}),hZ={kernelName:Gs,backendName:"webgl",kernelFunc:dZ},mZ=`
+    `}},mZ={kernelName:nl,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new hZ(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},XS="return floor(x);",fZ=Ze({opSnippet:XS,packedOpSnippet:XS,cpuKernelImpl:sK}),gZ={kernelName:Hs,backendName:"webgl",kernelFunc:fZ},yZ=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -2884,7 +2884,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
   } else {
     return NAN;
   }
-`,fZ=`
+`,bZ=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -2905,7 +2905,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,gZ=an({opSnippet:mZ,packedOpSnippet:fZ,dtype:"int32"}),yZ={kernelName:Hs,backendName:"webgl",kernelFunc:gZ},bZ=class{constructor(e){this.variableNames=["A"];let t=mn(),[n,a]=e;this.outputShape=e,this.userCode=`
+`,xZ=an({opSnippet:yZ,packedOpSnippet:bZ,dtype:"int32"}),vZ={kernelName:js,backendName:"webgl",kernelFunc:xZ},wZ=class{constructor(e){this.variableNames=["A"];let t=mn(),[n,a]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2927,7 +2927,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}},xZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=mn(),[n,a]=e;this.outputShape=e,this.userCode=`
+    `}},kZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=mn(),[n,a]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2961,7 +2961,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
 
         ${t.output} = result;
       }
-    `}},wZ={kernelName:ph,backendName:"webgl",kernelFunc:vZ},Su;function vZ(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,c],d=[u,c,s];(o||i||l)&&(Su==null&&(Su=document.createElement("canvas").getContext("2d")),Su.canvas.width=c,Su.canvas.height=u,Su.drawImage(r,0,0,c,u),r=Su.canvas);let h=n.makeTensorInfo(p,"int32");n.texData.get(h.dataId).usage=ea.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(h.dataId),r);let m=ee().getBool("WEBGL_PACK")?new xZ(d):new bZ(d),f=n.runWebGLProgram(m,[h],"int32");return n.disposeData(h.dataId),f}function kZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=_.convertConv2DDataFormat(u),g=_.computeConv2DInfo(r.shape,s.shape,l,p,c,d,!1,f),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=FS({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=$S({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let x=i!=null,N=o!=null,T=h==="leakyrelu",C=h?sf(h,!1):null,$=new AS(g,x,C,N,T),F=[r,s];if(i&&F.push(i),o&&F.push(o),T){let O=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));F.push(O),b.push(O)}y=n.runWebGLProgram($,F,"float32")}let v=be({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var IZ={kernelName:Ii,backendName:"webgl",kernelFunc:kZ};function NZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=a,m=[],f=u;f==null&&(f=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=_.computeConv2DInfo(r.shape,s.shape,l,f,c,p,!0),y=ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=d?sf(d,y):null,v=[r,s],x=i!=null,N=o!=null,T=d==="leakyrelu";if(x&&v.push(i),N&&v.push(o),T){let F=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));v.push(F),m.push(F)}let C;y?C=new OS(g,x,b,N,T):C=new PS(g,x,b,N,T);let $=n.runWebGLProgram(C,v,"float32");return m.forEach(F=>n.disposeIntermediateTensorInfo(F)),$}var TZ={kernelName:Ni,backendName:"webgl",kernelFunc:NZ},SZ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=ft(t.length),r=ft(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}},NZ={kernelName:mh,backendName:"webgl",kernelFunc:IZ},Su;function IZ(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,c],d=[u,c,s];(o||i||l)&&(Su==null&&(Su=document.createElement("canvas").getContext("2d")),Su.canvas.width=c,Su.canvas.height=u,Su.drawImage(r,0,0,c,u),r=Su.canvas);let h=n.makeTensorInfo(p,"int32");n.texData.get(h.dataId).usage=ea.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(h.dataId),r);let m=ee().getBool("WEBGL_PACK")?new kZ(d):new wZ(d),f=n.runWebGLProgram(m,[h],"int32");return n.disposeData(h.dataId),f}function TZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=_.convertConv2DDataFormat(u),g=_.computeConv2DInfo(r.shape,s.shape,l,p,c,d,!1,f),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=OS({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=LS({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let x=i!=null,N=o!=null,T=h==="leakyrelu",C=h?uf(h,!1):null,F=new PS(g,x,C,N,T),$=[r,s];if(i&&$.push(i),o&&$.push(o),T){let P=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));$.push(P),b.push(P)}y=n.runWebGLProgram(F,$,"float32")}let v=be({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var SZ={kernelName:Ti,backendName:"webgl",kernelFunc:TZ};function CZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=a,m=[],f=u;f==null&&(f=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=_.computeConv2DInfo(r.shape,s.shape,l,f,c,p,!0),y=ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=d?uf(d,y):null,v=[r,s],x=i!=null,N=o!=null,T=d==="leakyrelu";if(x&&v.push(i),N&&v.push(o),T){let $=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));v.push($),m.push($)}let C;y?C=new US(g,x,b,N,T):C=new VS(g,x,b,N,T);let F=n.runWebGLProgram(C,v,"float32");return m.forEach($=>n.disposeIntermediateTensorInfo($)),F}var _Z={kernelName:Si,backendName:"webgl",kernelFunc:CZ},EZ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=ft(t.length),r=ft(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${a} strides = ${a}(${this.strides});
          void main() {
           ${r} coords = getOutputCoords();
@@ -2972,21 +2972,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}};function CZ(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],[o,l,c,u]=_.prepareAndValidate(a,r),p=be({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),d=be({inputs:{x:a},backend:n,attrs:{shape:[k.sizeFromShape(a.shape)/c,c]}}),h=new SZ(i,u,[l,c]),m=n.runWebGLProgram(h,[d,p],d.dtype),f=be({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var _Z={kernelName:al,backendName:"webgl",kernelFunc:CZ},AZ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ft(this.rank),a=EZ(e,2);this.userCode=`
+      `}};function AZ(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],[o,l,c,u]=_.prepareAndValidate(a,r),p=be({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),d=be({inputs:{x:a},backend:n,attrs:{shape:[k.sizeFromShape(a.shape)/c,c]}}),h=new EZ(i,u,[l,c]),m=n.runWebGLProgram(h,[d,p],d.dtype),f=be({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var FZ={kernelName:rl,backendName:"webgl",kernelFunc:AZ},DZ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ft(this.rank),a=$Z(e,2);this.userCode=`
       void main() {
         ${n} resRC = getOutputCoords();
         setOutput(getA(${a}));
       }
-    `}};function EZ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("int(getIndices(resRC.x, resRC.z))"):a.push(`${n[r]}`);return a.join()}function FZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=k.sizeFromShape(s.shape),p=[],d=be({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=be({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});p.push(d),p.push(h);let m=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.bufferSync(h),v=n.bufferSync(d),x=nK(v,b,m);return p.forEach(N=>n.disposeIntermediateTensorInfo(N)),n.makeTensorInfo(c.outputShape,x.dtype,x.values)}let f=new AZ(d.shape,m),g=n.runWebGLProgram(f,[d,h],d.dtype);p.push(g);let y=be({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var $Z={kernelName:nl,backendName:"webgl",kernelFunc:FZ},DZ="return float(a > b);",RZ=`
+    `}};function $Z(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("int(getIndices(resRC.x, resRC.z))"):a.push(`${n[r]}`);return a.join()}function RZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=k.sizeFromShape(s.shape),p=[],d=be({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=be({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});p.push(d),p.push(h);let m=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.bufferSync(h),v=n.bufferSync(d),x=iK(v,b,m);return p.forEach(N=>n.disposeIntermediateTensorInfo(N)),n.makeTensorInfo(c.outputShape,x.dtype,x.values)}let f=new DZ(d.shape,m),g=n.runWebGLProgram(f,[d,h],d.dtype);p.push(g);let y=be({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var MZ={kernelName:al,backendName:"webgl",kernelFunc:RZ},PZ="return float(a > b);",OZ=`
   return vec4(greaterThan(a, b));
-`,MZ=an({opSnippet:DZ,packedOpSnippet:RZ,cpuKernelImpl:aK,dtype:"bool"}),PZ={kernelName:rl,backendName:"webgl",kernelFunc:MZ},OZ="return float(a >= b);",LZ=`
+`,LZ=an({opSnippet:PZ,packedOpSnippet:OZ,cpuKernelImpl:oK,dtype:"bool"}),zZ={kernelName:sl,backendName:"webgl",kernelFunc:LZ},BZ="return float(a >= b);",WZ=`
   return vec4(greaterThanEqual(a, b));
-`,zZ=an({opSnippet:OZ,packedOpSnippet:LZ,dtype:"bool"}),BZ={kernelName:qs,backendName:"webgl",kernelFunc:zZ};function WZ(e){let{inputs:t,backend:n}=e,{input:a}=t;return VS(a,!0,n)}var VZ={kernelName:Jd,backendName:"webgl",kernelFunc:WZ},UZ="return float(!isnan(x) && !isinf(x));",GZ=Ze({opSnippet:UZ,dtype:"bool"}),HZ={kernelName:il,backendName:"webgl",kernelFunc:GZ},jZ="return float(isinf(x));",qZ=Ze({opSnippet:jZ,dtype:"bool"}),KZ={kernelName:ol,backendName:"webgl",kernelFunc:qZ},XZ="return float(isnan(x));",YZ=Ze({opSnippet:XZ,dtype:"bool"}),ZZ={kernelName:ll,backendName:"webgl",kernelFunc:YZ},JZ="return float(a < b);",QZ=`
+`,VZ=an({opSnippet:BZ,packedOpSnippet:WZ,dtype:"bool"}),UZ={kernelName:Ks,backendName:"webgl",kernelFunc:VZ};function GZ(e){let{inputs:t,backend:n}=e,{input:a}=t;return KS(a,!0,n)}var HZ={kernelName:th,backendName:"webgl",kernelFunc:GZ},jZ="return float(!isnan(x) && !isinf(x));",qZ=Ze({opSnippet:jZ,dtype:"bool"}),KZ={kernelName:il,backendName:"webgl",kernelFunc:qZ},XZ="return float(isinf(x));",YZ=Ze({opSnippet:XZ,dtype:"bool"}),ZZ={kernelName:ol,backendName:"webgl",kernelFunc:YZ},JZ="return float(isnan(x));",QZ=Ze({opSnippet:JZ,dtype:"bool"}),e9={kernelName:ll,backendName:"webgl",kernelFunc:QZ},t9="return float(a < b);",n9=`
   return vec4(lessThan(a, b));
-`,e9=an({opSnippet:JZ,packedOpSnippet:QZ,cpuKernelImpl:rK,dtype:"bool"}),t9={kernelName:ul,backendName:"webgl",kernelFunc:e9},n9="return float(a <= b);",a9=`
+`,a9=an({opSnippet:t9,packedOpSnippet:n9,cpuKernelImpl:lK,dtype:"bool"}),r9={kernelName:ul,backendName:"webgl",kernelFunc:a9},s9="return float(a <= b);",i9=`
   return vec4(lessThanEqual(a, b));
-`,r9=an({opSnippet:n9,packedOpSnippet:a9,dtype:"bool"}),s9={kernelName:cl,backendName:"webgl",kernelFunc:r9};function i9(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=sK(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var o9={kernelName:eh,backendName:"webgl",kernelFunc:i9},l9=`if (x < 0.0) return NAN;
-  return log(x);`,u9=`
+`,o9=an({opSnippet:s9,packedOpSnippet:i9,dtype:"bool"}),l9={kernelName:cl,backendName:"webgl",kernelFunc:o9};function u9(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=uK(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var c9={kernelName:ah,backendName:"webgl",kernelFunc:u9},p9=`if (x < 0.0) return NAN;
+  return log(x);`,d9=`
   vec4 result = log(x);
   vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
   result.r = isNaN.r == 1.0 ? NAN : result.r;
@@ -2995,16 +2995,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
   result.a = isNaN.a == 1.0 ? NAN : result.a;
 
   return result;
-`,c9=Ze({opSnippet:l9,packedOpSnippet:u9,cpuKernelImpl:iK}),p9={kernelName:Xs,backendName:"webgl",kernelFunc:c9},d9="return log(1.0 + x);",h9=Ze({opSnippet:d9}),m9={kernelName:pl,backendName:"webgl",kernelFunc:h9},f9="return float(a >= 1.0 && b >= 1.0);",g9=`
+`,h9=Ze({opSnippet:p9,packedOpSnippet:d9,cpuKernelImpl:cK}),m9={kernelName:Zs,backendName:"webgl",kernelFunc:h9},f9="return log(1.0 + x);",g9=Ze({opSnippet:f9}),y9={kernelName:pl,backendName:"webgl",kernelFunc:g9},b9="return float(a >= 1.0 && b >= 1.0);",x9=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,y9=an({opSnippet:f9,packedOpSnippet:g9,dtype:"bool"}),b9={kernelName:dl,backendName:"webgl",kernelFunc:y9},x9="return float(!(x >= 1.0));",v9=Ze({opSnippet:x9}),w9={kernelName:kc,backendName:"webgl",kernelFunc:v9},k9="return float(a >= 1.0 || b >= 1.0);",I9=`
+`,v9=an({opSnippet:b9,packedOpSnippet:x9,dtype:"bool"}),w9={kernelName:dl,backendName:"webgl",kernelFunc:v9},k9="return float(!(x >= 1.0));",I9=Ze({opSnippet:k9}),N9={kernelName:wc,backendName:"webgl",kernelFunc:I9},T9="return float(a >= 1.0 || b >= 1.0);",S9=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,N9=an({opSnippet:k9,packedOpSnippet:I9,dtype:"bool"}),T9={kernelName:Ic,backendName:"webgl",kernelFunc:N9},S9=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
+`,C9=an({opSnippet:T9,packedOpSnippet:S9,dtype:"bool"}),_9={kernelName:kc,backendName:"webgl",kernelFunc:C9},E9=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3023,7 +3023,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         float val = x * ${o};
         setOutput(val);
       }
-    `}},C9=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
+    `}},A9=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -3085,7 +3085,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         vec4 result = xAtOutputCoords * ${o};
         setOutput(result);
       }
-    `}},_9=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,c=ee().getBool("WEBGL_PACK_NORMALIZATION")?new C9(r.shape,s,i,o,l):new S9(r.shape,s,i,o,l);return n.runWebGLProgram(c,[r],r.dtype)},E9={kernelName:Nc,backendName:"webgl",kernelFunc:_9},A9=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=`
+    `}},F9=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,c=ee().getBool("WEBGL_PACK_NORMALIZATION")?new A9(r.shape,s,i,o,l):new E9(r.shape,s,i,o,l);return n.runWebGLProgram(c,[r],r.dtype)},$9={kernelName:Ic,backendName:"webgl",kernelFunc:F9},D9=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3140,14 +3140,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
       }
       setOutput(result);
       }
-    `}},F9=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=a,p=new A9(r.shape,o,l,c,u);return n.runWebGLProgram(p,[r,s,i],r.dtype)},$9={kernelName:th,backendName:"webgl",kernelFunc:F9};function D9(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Ji(i,e.dtype,"max",a),l=be({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function GS(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=u!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let b=n.texData.get(h.dataId).values,v=new Array(o);for(let T=0;T<v.length;T++)v[T]=r.shape[u[T]];let x=Qv(b,r.shape,r.dtype,u,v);h=n.makeTensorInfo(v,r.dtype);let N=n.texData.get(h.dataId);N.values=x}else h=of(r,u,n);c=_.getInnerMostAxes(c.length,o)}_.assertAxesAreInnerMostDims("max",c,o);let[m,f]=_.computeOutAndReduceShapes(h.shape,c),g=m;i&&(g=_.expandShapeToKeepDim(m,l));let y;if(d){let b=n.texData.get(h.dataId).values,v=oK(b,k.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let x=n.texData.get(y.dataId);x.values=v}else y=D9(h,f,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var R9={kernelName:Ys,backendName:"webgl",kernelFunc:GS},M9=oS+`
+    `}},R9=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=a,p=new D9(r.shape,o,l,c,u);return n.runWebGLProgram(p,[r,s,i],r.dtype)},M9={kernelName:rh,backendName:"webgl",kernelFunc:R9};function P9(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=eo(i,e.dtype,"max",a),l=be({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function YS(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=u!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let b=n.texData.get(h.dataId).values,v=new Array(o);for(let T=0;T<v.length;T++)v[T]=r.shape[u[T]];let x=rw(b,r.shape,r.dtype,u,v);h=n.makeTensorInfo(v,r.dtype);let N=n.texData.get(h.dataId);N.values=x}else h=cf(r,u,n);c=_.getInnerMostAxes(c.length,o)}_.assertAxesAreInnerMostDims("max",c,o);let[m,f]=_.computeOutAndReduceShapes(h.shape,c),g=m;i&&(g=_.expandShapeToKeepDim(m,l));let y;if(d){let b=n.texData.get(h.dataId).values,v=pK(b,k.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let x=n.texData.get(y.dataId);x.values=v}else y=P9(h,f,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var O9={kernelName:Js,backendName:"webgl",kernelFunc:YS},L9=hS+`
   return max(a, b);
-`,P9=`
+`,z9=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+rf+`
+  `+lf+`
   return result;
-`,O9=an({opSnippet:M9,packedOpSnippet:P9,cpuKernelImpl:lK}),L9={kernelName:Zs,backendName:"webgl",kernelFunc:O9};function z9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Sp(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.computePool2DInfo(r.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Bn({inputs:{x:r},backend:n});let p=new Ep(u,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var B9={kernelName:Js,backendName:"webgl",kernelFunc:z9};function W9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=a,u=[1,1,1],p=_.computePool3DInfo(r.shape,s,i,u,o,c,l),d=new nw(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var V9={kernelName:Tc,backendName:"webgl",kernelFunc:W9},U9=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=`
+`,B9=an({opSnippet:L9,packedOpSnippet:z9,cpuKernelImpl:dK}),W9={kernelName:Qs,backendName:"webgl",kernelFunc:B9};function V9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Cp(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.computePool2DInfo(r.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Wn({inputs:{x:r},backend:n});let p=new Ap(u,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var U9={kernelName:ei,backendName:"webgl",kernelFunc:V9};function G9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=a,u=[1,1,1],p=_.computePool3DInfo(r.shape,s,i,u,o,c,l),d=new lw(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var H9={kernelName:Nc,backendName:"webgl",kernelFunc:G9},j9=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -3193,7 +3193,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},G9=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,p=l-1-e.padInfo.top,d=c-1-e.padInfo.left,h=o*l*c-1;this.userCode=`
+    `}},q9=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,p=l-1-e.padInfo.top,d=c-1-e.padInfo.left,h=o*l*c-1;this.userCode=`
       const ivec3 pads = ivec3(${u}, ${p}, ${d});
 
       void main() {
@@ -3257,14 +3257,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function H9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=a,p=[1,1,1],d=_.computePool3DInfo(i.shape,o,l,p,c,u),h=new nw(d,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new G9(d),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var j9={kernelName:ah,backendName:"webgl",kernelFunc:H9};function q9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;Sp([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:p}=a,d=_.computePool2DInfo(o.shape,l,c,1,u,p),h=!0,m=new Ep(d,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new U9(d),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var K9={kernelName:nh,backendName:"webgl",kernelFunc:q9};function X9(e,t,n,a){let r=new Ep(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Ep(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var Y9={kernelName:rh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;k.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let c=[1,1];k.assert(_.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=_.computePool2DInfo(a.shape,r,s,c,i),[p,d]=X9(a,o,u,l);return[p,d]}};function Z9(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Ji(i,"float32","mean",a),l=be({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var J9={kernelName:Qs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=_.getAxesPermutation(c,o),p=u!=null,d=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(d){let v=i.texData.get(m.dataId).values,x=new Array(o);for(let C=0;C<x.length;C++)x[C]=a.shape[u[C]];let N=Qv(v,a.shape,a.dtype,u,x);m=i.makeTensorInfo(x,a.dtype);let T=i.texData.get(m.dataId);T.values=N}else m=of(a,u,i);h.push(m),c=_.getInnerMostAxes(c.length,o)}_.assertAxesAreInnerMostDims("sum",c,o);let[f,g]=_.computeOutAndReduceShapes(m.shape,c),y=f;r&&(y=_.expandShapeToKeepDim(f,l));let b=Z9(m,g,y,i);for(let v of h)i.disposeIntermediateTensorInfo(v);return b}};function Q9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=r;u!=null&&(p=Sn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,r.shape.length)),_.assertAxesAreInnerMostDims("min",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=be({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Ji(f,f.dtype,"min",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=be({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var eJ={kernelName:ei,backendName:"webgl",kernelFunc:Q9},tJ=oS+`
+    `}};function K9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=a,p=[1,1,1],d=_.computePool3DInfo(i.shape,o,l,p,c,u),h=new lw(d,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new q9(d),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var X9={kernelName:ih,backendName:"webgl",kernelFunc:K9};function Y9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;Cp([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:p}=a,d=_.computePool2DInfo(o.shape,l,c,1,u,p),h=!0,m=new Ap(d,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new j9(d),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var Z9={kernelName:sh,backendName:"webgl",kernelFunc:Y9};function J9(e,t,n,a){let r=new Ap(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Ap(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var Q9={kernelName:oh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;k.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let c=[1,1];k.assert(_.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=_.computePool2DInfo(a.shape,r,s,c,i),[p,d]=J9(a,o,u,l);return[p,d]}};function eJ(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=eo(i,"float32","mean",a),l=be({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var tJ={kernelName:ti,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=_.getAxesPermutation(c,o),p=u!=null,d=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(d){let v=i.texData.get(m.dataId).values,x=new Array(o);for(let C=0;C<x.length;C++)x[C]=a.shape[u[C]];let N=rw(v,a.shape,a.dtype,u,x);m=i.makeTensorInfo(x,a.dtype);let T=i.texData.get(m.dataId);T.values=N}else m=cf(a,u,i);h.push(m),c=_.getInnerMostAxes(c.length,o)}_.assertAxesAreInnerMostDims("sum",c,o);let[f,g]=_.computeOutAndReduceShapes(m.shape,c),y=f;r&&(y=_.expandShapeToKeepDim(f,l));let b=eJ(m,g,y,i);for(let v of h)i.disposeIntermediateTensorInfo(v);return b}};function nJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=r;u!=null&&(p=Sn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,r.shape.length)),_.assertAxesAreInnerMostDims("min",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=be({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=eo(f,f.dtype,"min",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=be({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var aJ={kernelName:ni,backendName:"webgl",kernelFunc:nJ},rJ=hS+`
   return min(a, b);
-`,nJ=`
+`,sJ=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+rf+`
+  `+lf+`
   return result;
-`,aJ=an({opSnippet:tJ,packedOpSnippet:nJ,cpuKernelImpl:uK}),rJ={kernelName:ti,backendName:"webgl",kernelFunc:aJ},sJ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let a=e.length,r=ft(a),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,a),l=n==="reflect"?0:1;if(a===1){this.userCode=`
+`,iJ=an({opSnippet:rJ,packedOpSnippet:sJ,cpuKernelImpl:hK}),oJ={kernelName:ai,backendName:"webgl",kernelFunc:iJ},lJ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let a=e.length,r=ft(a),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,a),l=n==="reflect"?0:1;if(a===1){this.userCode=`
         int start = ${s};
         int end = ${i};
 
@@ -3293,7 +3293,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         ${r} coords = outC - start;
         setOutput(getX(${o}));
       }
-    `}},iJ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=ft(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=fn("rc",a),l=fn("source",a),c=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(a===1){let h=`
+    `}},uJ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=ft(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=fn("rc",a),l=fn("source",a),c=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(a===1){let h=`
         ${r} source = rc;
         if (source < start) {
           source = start * 2 - source - ${p};
@@ -3349,13 +3349,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={kernelNam
         ${d}
         setOutput(result);
       }
-    `}},oJ=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new iJ(a.shape,r,s):new sJ(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},lJ={kernelName:Sc,backendName:"webgl",kernelFunc:oJ},uJ=`if (b == 0.0) return NAN;
-  return mod(a, b);`,cJ=`
+    `}},cJ=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new uJ(a.shape,r,s):new lJ(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},pJ={kernelName:Tc,backendName:"webgl",kernelFunc:cJ},dJ=`if (b == 0.0) return NAN;
+  return mod(a, b);`,hJ=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
-  `+rf+`
+  `+lf+`
   return result;
-`,pJ=an({opSnippet:uJ,packedOpSnippet:cJ}),dJ={kernelName:hl,backendName:"webgl",kernelFunc:pJ},hJ=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
+`,mJ=an({opSnippet:dJ,packedOpSnippet:hJ}),fJ={kernelName:hl,backendName:"webgl",kernelFunc:mJ},gJ=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
       uniform float seed;
 
       void main() {
@@ -3377,11 +3377,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tY=Ze({opSnippet:eY}),nY={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)}}},mJ=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},yJ=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,fJ=`
+return a / b;`,bJ=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3399,14 +3399,14 @@ return a / b;`,fJ=`
   }
 
   return result;
-`,HS=an({opSnippet:mJ,packedOpSnippet:fJ,checkOutOfBounds:!0}),gJ={kernelName:Vs,backendName:"webgl",kernelFunc:HS},jS="return a - b;",qS=an({opSnippet:jS,packedOpSnippet:jS,supportsComplex:!0,cpuKernelImpl:yK}),yJ={kernelName:xi,backendName:"webgl",kernelFunc:qS};function KS(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=GS({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=_.expandShapeToKeepDim(o.shape,i),c=be({inputs:{x:o},backend:n,attrs:{shape:l}}),u=qS({inputs:{a:r,b:c},backend:n}),p=zS({inputs:{x:u},backend:n}),d=tw({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=be({inputs:{x:d},backend:n,attrs:{shape:l}}),m=HS({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}var bJ={kernelName:yi,backendName:"webgl",kernelFunc:KS};function xJ(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:KS({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],p=new hJ(c,u,s),d=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),h}var vJ={kernelName:sh,backendName:"webgl",kernelFunc:xJ},XS="return -x;";function wJ(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=pK(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Iu(a.shape,XS):r=new ms(a.shape,XS),n.runWebGLProgram(r,[a],a.dtype)}var kJ={kernelName:ml,backendName:"webgl",kernelFunc:wJ},IJ=Qa.nonMaxSuppressionV3Impl;function NJ(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,c=n.readSync(r.dataId),u=n.readSync(s.dataId),{selectedIndices:p}=IJ(c,u,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var TJ={kernelName:gl,backendName:"webgl",kernelFunc:NJ},SJ=Qa.nonMaxSuppressionV4Impl;function CJ(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d,validOutputs:h}=SJ(u,p,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var _J={kernelName:yl,backendName:"webgl",kernelFunc:CJ},EJ=Qa.nonMaxSuppressionV5Impl;function AJ(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=i,h=o,m=l,f=c,{selectedIndices:g,selectedScores:y}=EJ(u,p,d,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var FJ={kernelName:bl,backendName:"webgl",kernelFunc:AJ},$J=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`,ZS=an({opSnippet:yJ,packedOpSnippet:bJ,checkOutOfBounds:!0}),xJ={kernelName:Us,backendName:"webgl",kernelFunc:ZS},JS="return a - b;",QS=an({opSnippet:JS,packedOpSnippet:JS,supportsComplex:!0,cpuKernelImpl:wK}),vJ={kernelName:wi,backendName:"webgl",kernelFunc:QS};function e2(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=YS({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=_.expandShapeToKeepDim(o.shape,i),c=be({inputs:{x:o},backend:n,attrs:{shape:l}}),u=QS({inputs:{a:r,b:c},backend:n}),p=HS({inputs:{x:u},backend:n}),d=ow({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=be({inputs:{x:d},backend:n,attrs:{shape:l}}),m=ZS({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}var wJ={kernelName:xi,backendName:"webgl",kernelFunc:e2};function kJ(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:e2({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],p=new gJ(c,u,s),d=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),h}var IJ={kernelName:lh,backendName:"webgl",kernelFunc:kJ},t2="return -x;";function NJ(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=fK(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Iu(a.shape,t2):r=new ms(a.shape,t2),n.runWebGLProgram(r,[a],a.dtype)}var TJ={kernelName:ml,backendName:"webgl",kernelFunc:NJ},SJ=er.nonMaxSuppressionV3Impl;function CJ(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,c=n.readSync(r.dataId),u=n.readSync(s.dataId),{selectedIndices:p}=SJ(c,u,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var _J={kernelName:gl,backendName:"webgl",kernelFunc:CJ},EJ=er.nonMaxSuppressionV4Impl;function AJ(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d,validOutputs:h}=EJ(u,p,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var FJ={kernelName:yl,backendName:"webgl",kernelFunc:AJ},$J=er.nonMaxSuppressionV5Impl;function DJ(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=i,h=o,m=l,f=c,{selectedIndices:g,selectedScores:y}=$J(u,p,d,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var RJ={kernelName:bl,backendName:"webgl",kernelFunc:DJ},MJ=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${a}), float(${n}),
                       float(index == coords.y)));
       }
-    `}},DJ=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=k.sizeFromShape(r.shape),c=new $J(l,s,i,o),u=be({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let d=[...r.shape,s],h=be({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},RJ={kernelName:ai,backendName:"webgl",kernelFunc:DJ};function df(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Fp({inputs:{input:a},backend:n}),s=df({inputs:{x:r},backend:n}),i=pf({inputs:{input:a},backend:n}),o=df({inputs:{x:i},backend:n}),l=fs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return iw({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var MJ={kernelName:Pl,backendName:"webgl",kernelFunc:df};function YS(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=Fp({inputs:{input:a},backend:n}),s=YS({inputs:{x:r},backend:n}),i=pf({inputs:{input:a},backend:n}),o=df({inputs:{x:i},backend:n}),l=fs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return iw({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var PJ={kernelName:xl,backendName:"webgl",kernelFunc:YS};function OJ(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return sw({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{k.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let p=sw({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),c=ES({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var LJ={kernelName:vl,backendName:"webgl",kernelFunc:OJ},zJ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let a=e.length,r=ft(a),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,a);if(a===1){this.userCode=`
+    `}},PJ=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=k.sizeFromShape(r.shape),c=new MJ(l,s,i,o),u=be({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let d=[...r.shape,s],h=be({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},OJ={kernelName:si,backendName:"webgl",kernelFunc:PJ};function ff(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=$p({inputs:{input:a},backend:n}),s=ff({inputs:{x:r},backend:n}),i=mf({inputs:{input:a},backend:n}),o=ff({inputs:{x:i},backend:n}),l=fs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return dw({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var LJ={kernelName:Pl,backendName:"webgl",kernelFunc:ff};function n2(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=$p({inputs:{input:a},backend:n}),s=n2({inputs:{x:r},backend:n}),i=mf({inputs:{input:a},backend:n}),o=ff({inputs:{x:i},backend:n}),l=fs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return dw({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var zJ={kernelName:xl,backendName:"webgl",kernelFunc:n2};function BJ(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return pw({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{k.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let p=pw({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),c=MS({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var WJ={kernelName:vl,backendName:"webgl",kernelFunc:BJ},VJ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let a=e.length,r=ft(a),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,a);if(a===1){this.userCode=`
         int start = ${s};
         int end = ${i};
 
@@ -3431,7 +3431,7 @@ return a / b;`,fJ=`
           setOutput(getX(${o}));
         }
       }
-    `}},BJ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ft(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=fn("rc",a),l=fn("source",a),c=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
+    `}},UJ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ft(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=fn("rc",a),l=fn("source",a),c=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
        if(${c}) {
       `,a===1?"":`}
        rc = outputLoc;
@@ -3455,7 +3455,7 @@ return a / b;`,fJ=`
         ${h}
         setOutput(result);
       }
-    `}},ZS=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new BJ(r.shape,s,i):new zJ(r.shape,s,i);return n.runWebGLProgram(o,[r],r.dtype)},WJ={kernelName:ri,backendName:"webgl",kernelFunc:ZS},VJ=`
+    `}},a2=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new UJ(r.shape,s,i):new VJ(r.shape,s,i);return n.runWebGLProgram(o,[r],r.dtype)},GJ={kernelName:ii,backendName:"webgl",kernelFunc:a2},HJ=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3464,7 +3464,7 @@ return a / b;`,fJ=`
   }
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,UJ=`
+`,jJ=`
   // 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);
@@ -3478,11 +3478,11 @@ return a / b;`,fJ=`
   result.a = isExpZero.a ? 1.0 : result.a;
 
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
-  `+rf+`
+  `+lf+`
   return result;
-`,GJ=an({opSnippet:VJ,packedOpSnippet:UJ}),HJ={kernelName:si,backendName:"webgl",kernelFunc:GJ};function jJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],c=k.parseAxisParam(s,r.shape),u=c,p=_.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=_.getInnerMostAxes(u.length,o),l.push(d)),_.assertAxesAreInnerMostDims("prod",u,o);let h;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:g,outDtype:y}=dK(d.shape,d.dtype,m,u);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=_.computeOutAndReduceShapes(d.shape,u),g=k.sizeFromShape(f),y=be({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),b=yh(r.dtype),v=Ji(y,b,"prod",n);h=be({inputs:{x:v},backend:n,attrs:{shape:m}}),l.push(y),l.push(v)}if(i){l.push(h);let m=_.expandShapeToKeepDim(h.shape,c);h=be({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var qJ={kernelName:wl,backendName:"webgl",kernelFunc:jJ},JS=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=hK(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},KJ={kernelName:Cc,backendName:"webgl",kernelFunc:JS},XJ="return 1.0 / x;",YJ=Ze({opSnippet:XJ}),ZJ={kernelName:kl,backendName:"webgl",kernelFunc:YJ},JJ=Ma+`
+`,qJ=an({opSnippet:HJ,packedOpSnippet:jJ}),KJ={kernelName:oi,backendName:"webgl",kernelFunc:qJ};function XJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],c=k.parseAxisParam(s,r.shape),u=c,p=_.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=_.getInnerMostAxes(u.length,o),l.push(d)),_.assertAxesAreInnerMostDims("prod",u,o);let h;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:g,outDtype:y}=gK(d.shape,d.dtype,m,u);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=_.computeOutAndReduceShapes(d.shape,u),g=k.sizeFromShape(f),y=be({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),b=vh(r.dtype),v=eo(y,b,"prod",n);h=be({inputs:{x:v},backend:n,attrs:{shape:m}}),l.push(y),l.push(v)}if(i){l.push(h);let m=_.expandShapeToKeepDim(h.shape,c);h=be({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var YJ={kernelName:wl,backendName:"webgl",kernelFunc:XJ},r2=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=yK(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},ZJ={kernelName:Sc,backendName:"webgl",kernelFunc:r2},JJ="return 1.0 / x;",QJ=Ze({opSnippet:JJ}),eQ={kernelName:kl,backendName:"webgl",kernelFunc:QJ},tQ=Pa+`
   return (x < 0.0) ? 0.0 : x;
-`,QJ=`
+`,nQ=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3492,9 +3492,9 @@ return a / b;`,fJ=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,eQ=Ze({opSnippet:JJ,packedOpSnippet:QJ}),tQ={kernelName:oi,backendName:"webgl",kernelFunc:eQ},nQ=Ma+`
+`,aQ=Ze({opSnippet:tQ,packedOpSnippet:nQ}),rQ={kernelName:ui,backendName:"webgl",kernelFunc:aQ},sQ=Pa+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,aQ=`
+`,iQ=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3504,7 +3504,7 @@ return a / b;`,fJ=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,rQ=Ze({opSnippet:nQ,packedOpSnippet:aQ}),sQ={kernelName:ui,backendName:"webgl",kernelFunc:rQ},iQ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+`,oQ=Ze({opSnippet:sQ,packedOpSnippet:iQ}),lQ={kernelName:pi,backendName:"webgl",kernelFunc:oQ},uQ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${c[0]/u[0]},
           ${c[1]/u[1]});
@@ -3537,7 +3537,7 @@ return a / b;`,fJ=`
 
         setOutput(newValue);
       }
-    `}},oQ=class{constructor(e,t,n,a,r){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=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},cQ=class{constructor(e,t,n,a,r){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=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${c[0]/u[0]},
           ${c[1]/u[1]},
@@ -3614,7 +3614,7 @@ return a / b;`,fJ=`
 
         setOutput(newValue);
       }
-    `}};function lQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=ee().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new oQ(r.shape,l,c,s,i):new iQ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],"float32")}var uQ={kernelName:li,backendName:"webgl",kernelFunc:lQ},cQ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],p=1/c,d=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
+    `}};function pQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=ee().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new cQ(r.shape,l,c,s,i):new uQ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],"float32")}var dQ={kernelName:ci,backendName:"webgl",kernelFunc:pQ},hQ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],p=1/c,d=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3695,7 +3695,7 @@ return a / b;`,fJ=`
 
         setOutput(accumulator);
       }
-    `}};function pQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new cQ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var dQ={kernelName:lh,backendName:"webgl",kernelFunc:pQ},hQ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function mQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new hQ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var fQ={kernelName:ph,backendName:"webgl",kernelFunc:mQ},gQ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",d;r?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]});
@@ -3717,7 +3717,7 @@ return a / b;`,fJ=`
 
         setOutput(newValue);
       }
-    `}};function mQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=new hQ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],r.dtype)}var fQ={kernelName:_c,backendName:"webgl",kernelFunc:mQ},gQ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],p=1/c,d=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
+    `}};function yQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=new gQ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],r.dtype)}var bQ={kernelName:Cc,backendName:"webgl",kernelFunc:yQ},xQ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],p=1/c,d=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3787,7 +3787,7 @@ return a / b;`,fJ=`
 
         setOutput(accumulator);
       }
-    `}};function yQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new gQ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var bQ={kernelName:oh,backendName:"webgl",kernelFunc:yQ},xQ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
+    `}};function vQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new xQ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var wQ={kernelName:ch,backendName:"webgl",kernelFunc:vQ},kQ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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));
@@ -3797,7 +3797,7 @@ return a / b;`,fJ=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${r}));
       }
-    `}},vQ=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-${n} tensor is not yet supported`);this.outputShape=e;let a=fn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ft(n);n===1?this.userCode=`
+    `}},IQ=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let a=fn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ft(n);n===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
@@ -3825,7 +3825,7 @@ return a / b;`,fJ=`
           }
           setOutput(result);
         }
-    `;function o(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((y,b)=>d(b,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function d(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function wQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return Bn({inputs:{x:r},backend:n});let l=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new vQ(r.shape,o):new xQ(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var kQ={kernelName:ci,backendName:"webgl",kernelFunc:wQ},IQ=class{constructor(e,t,n,a){this.variableNames=["Image"],this.outputShape=[];let r=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,c]=_.getImageCenter(a,r,s),u=l.toFixed(3),p=c.toFixed(3),d="";typeof n=="number"?d=`float outputValue = ${n.toFixed(2)};`:d=`
+    `;function o(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((y,b)=>d(b,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function d(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function NQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return Wn({inputs:{x:r},backend:n});let l=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new IQ(r.shape,o):new kQ(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var TQ={kernelName:di,backendName:"webgl",kernelFunc:NQ},SQ=class{constructor(e,t,n,a){this.variableNames=["Image"],this.outputShape=[];let r=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,c]=_.getImageCenter(a,r,s),u=l.toFixed(3),p=c.toFixed(3),d="";typeof n=="number"?d=`float outputValue = ${n.toFixed(2)};`:d=`
         vec3 fill = vec3(${n.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         void main() {
@@ -3842,7 +3842,7 @@ return a / b;`,fJ=`
           }
           setOutput(outputValue);
         }
-    `}},NQ={kernelName:Ol,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new IQ(a.shape,r,s,i);return o.runWebGLProgram(l,[a],a.dtype)}},TQ=`
+    `}},CQ={kernelName:Ol,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new SQ(a.shape,r,s,i);return o.runWebGLProgram(l,[a],a.dtype)}},_Q=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -3857,7 +3857,7 @@ return a / b;`,fJ=`
       return base + 1.0;
     }
   }
-`,SQ=Ze({opSnippet:TQ}),CQ={kernelName:pi,backendName:"webgl",kernelFunc:SQ},_Q="return inversesqrt(x);",EQ=Ze({opSnippet:_Q,cpuKernelImpl:mK}),AQ={kernelName:di,backendName:"webgl",kernelFunc:EQ},QS=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ft(r.length),l=ft(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
+`,EQ=Ze({opSnippet:_Q}),AQ={kernelName:hi,backendName:"webgl",kernelFunc:EQ},FQ="return inversesqrt(x);",$Q=Ze({opSnippet:FQ,cpuKernelImpl:bK}),DQ={kernelName:mi,backendName:"webgl",kernelFunc:$Q},s2=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ft(r.length),l=ft(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
         ${o} strides = ${o}(${r});
 
         void main() {
@@ -3877,7 +3877,7 @@ return a / b;`,fJ=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function FQ(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:p}=_.calculateShapes(s,r,i),d=[p/c,c];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=be({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=be({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new QS(l,o,h.shape.length,m.shape.length,u,d),y=n.runWebGLProgram(g,[m,h,f],m.dtype),b=be({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),b}var $Q={kernelName:Nl,backendName:"webgl",kernelFunc:FQ},DQ=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);a=o.join(),r=l.join()}let s=ft(n);this.userCode=`
+      `}};function RQ(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:p}=_.calculateShapes(s,r,i),d=[p/c,c];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=be({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=be({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new s2(l,o,h.shape.length,m.shape.length,u,d),y=n.runWebGLProgram(g,[m,h,f],m.dtype),b=be({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),b}var MQ={kernelName:Nl,backendName:"webgl",kernelFunc:RQ},PQ=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);a=o.join(),r=l.join()}let s=ft(n);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         float cVal = getC(${a});
@@ -3887,21 +3887,21 @@ return a / b;`,fJ=`
           setOutput(getB(${r}));
         }
       }
-    `}};function RQ(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new DQ(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],ca(r.dtype,s.dtype))}var MQ={kernelName:Tl,backendName:"webgl",kernelFunc:RQ},PQ=`
+    `}};function OQ(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new PQ(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],pa(r.dtype,s.dtype))}var LQ={kernelName:Tl,backendName:"webgl",kernelFunc:OQ},zQ=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
   float scaleAlpha = ${_.SELU_SCALEALPHA};
   float scale = ${_.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,OQ=Ze({opSnippet:PQ}),LQ={kernelName:Sl,backendName:"webgl",kernelFunc:OQ},zQ="return 1.0 / (1.0 + exp(-1.0 * x));",BQ=Ze({opSnippet:zQ}),WQ={kernelName:mi,backendName:"webgl",kernelFunc:BQ},VQ=`
+`,BQ=Ze({opSnippet:zQ}),WQ={kernelName:Sl,backendName:"webgl",kernelFunc:BQ},VQ="return 1.0 / (1.0 + exp(-1.0 * x));",UQ=Ze({opSnippet:VQ}),GQ={kernelName:gi,backendName:"webgl",kernelFunc:UQ},HQ=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,UQ=Ze({opSnippet:VQ}),GQ={kernelName:El,backendName:"webgl",kernelFunc:UQ},HQ=dS+`
+`,jQ=Ze({opSnippet:HQ}),qQ={kernelName:El,backendName:"webgl",kernelFunc:jQ},KQ=bS+`
   return sin(x);
-`,jQ=Ze({opSnippet:HQ}),qQ={kernelName:hi,backendName:"webgl",kernelFunc:jQ},KQ=`
+`,XQ=Ze({opSnippet:KQ}),YQ={kernelName:fi,backendName:"webgl",kernelFunc:XQ},ZQ=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,XQ=Ze({opSnippet:KQ}),YQ={kernelName:_l,backendName:"webgl",kernelFunc:XQ},ZQ=`
+`,JQ=Ze({opSnippet:ZQ}),QQ={kernelName:_l,backendName:"webgl",kernelFunc:JQ},eee=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -3921,9 +3921,9 @@ return a / b;`,fJ=`
     result = log(exp_x + 1.0);
   }
   return result;
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@@ -3931,15 +3931,15 @@ return a / b;`,fJ=`
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@@ -4045,7 +4045,7 @@ return a / b;`,fJ=`
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r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return a2(s,r.length,Wn[a.dtype],i),a}var Kee={kernelName:$s,backendName:"wasm",setupFunc:jee,kernelFunc:qee};function hf(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var Xee={kernelName:sl,backendName:"wasm",kernelFunc:hf},r2;function Yee(e){r2=e.wasm.cwrap(wi,null,["number","array","number","number","number","array","number"])}function mf(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=Jee(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=Zee(t.x.shape,a.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=hf({inputs:t,backend:n});return m.shape=o,m}let c=n.makeOutput(o,l.dtype),u=n.dataIdMap.get(l.dataId).id,p=n.dataIdMap.get(c.dataId).id,d=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return r2(u,h,l.shape.length,Wn[l.dtype],p,d,s.length),c}function Zee(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function Jee(e,t){let n=[],a=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&a.push(t[r]);for(let r=0;r<a.length;++r){let s=-1;for(let i=0;i<a.length;++i)a[i]>=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var Qee={kernelName:wi,backendName:"wasm",kernelFunc:mf,setupFunc:Yee};function Cu(e,t,n){let a=e.shape,r=e.shape.length,s=k.parseAxisParam(t,a),i=s,o=_.getAxesPermutation(i,r),l=null,c=!1;if(o!=null){let u=new Array(r);for(let d=0;d<u.length;d++)u[d]=a[o[d]];i=_.getInnerMostAxes(i.length,r),l=mf({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==p&&(c=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:c}}var s2;function ete(e){s2=e.wasm.cwrap(Ds,null,["number","number","number","number","number"])}function tte(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:c,axes:u,inputWasTransposed:p}=Cu(s,r,t);if(p){let y=t.dataIdMap.get(c.dataId).id;y!==i&&(l=c,o=y)}let d=l.shape.slice(0,-1),h=t.makeOutput(d,"int32"),m=t.dataIdMap.get(h.dataId).id,f=k.sizeFromShape(h.shape),g=l.shape[u[0]];return s2(o,Wn[l.dtype],f,g,m),p&&t.disposeData(c.dataId),h}var nte={kernelName:Ds,backendName:"wasm",kernelFunc:tte,setupFunc:ete},i2;function ate(e){i2=e.wasm.cwrap(Rs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function rte(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=_.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.strideHeight,b=u.strideWidth,v=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. 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Please use 'channelsLast'.`);let W=a.makeOutput(m.outShape,"float32"),G=a.dataIdMap.get(W.dataId).id;return c2(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,b,v,x,B,N,T,C,$,F,O,G),W}var yte={kernelName:Os,backendName:"wasm",setupFunc:fte,kernelFunc:gte},p2;function bte(e){p2=e.wasm.cwrap(Ls,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function xte(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,inputShape:u}=a,p=1,d=_.convertConv2DDataFormat(l),h=_.computeConv2DInfo(u,s.shape,i,p,o,c,!1,d),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:b,inWidth:v,outChannels:x,outHeight:N,outWidth:T,strideHeight:C,strideWidth:$}=h,F=f-1-h.padInfo.top,O=g-1-h.padInfo.left,B=h.dataFormat==="channelsLast",W=k.computeStrides(h.inShape),G=k.computeStrides(r.shape),[q,H,K]=k.computeStrides(s.shape),X=W[0],ne=B?W[1]:W[2],J=B?W[2]:1,se=B?1:W[1],Q=G[0],oe=B?G[1]:G[2],ae=B?G[2]:1,pe=B?1:G[1],ue=t.makeOutput(h.inShape,"float32"),ge=t.dataIdMap.get(ue.dataId).id,ye=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(s.dataId).id;return p2(ye,ke,m,f,g,b,v,y,N,T,x,C,$,F,O,q,H,K,X,ne,J,se,Q,oe,ae,pe,ge),ue}var vte={kernelName:Ls,backendName:"wasm",setupFunc:bte,kernelFunc:xte},wte=Vn(zs),ow;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(ow||(ow={}));var d2;function kte(e){d2=e.wasm.cwrap(Ko,null,["number","number","number","number","array","number","number","number","number","number"])}function Ite(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:c}=n,u=l.shape[0],[p,d]=i,h=[u,p,d,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=ff({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(l.dataId).id,b=t.dataIdMap.get(c.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,N=new Uint8Array(new Int32Array(o.shape).buffer);return d2(g,y,b,u,N,p,d,ow[r],s,x),f!=null&&t.disposeData(f.dataId),v}var Nte={kernelName:Ko,backendName:"wasm",setupFunc:kte,kernelFunc:Ite},h2;function Tte(e){h2=e.wasm.cwrap(Bs,null,["number","number","number","number","number","number"])}function Ste(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;k.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=_.getAxesPermutation([s],l),u=r;c!==null&&(u=mf({inputs:{x:r},attrs:{perm:c},backend:n}));let p=_.getInnerMostAxes(1,l)[0];_.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(u.shape,u.dtype),h=u.shape[p],m=n.dataIdMap.get(u.dataId).id,f=n.dataIdMap.get(d.dataId).id;h2(m,i?1:0,o?1:0,h,f,Wn[r.dtype]);let g=d;if(c!==null){let y=_.getUndoAxesPermutation(c);g=mf({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(d.dataId)}return g}var Cte={kernelName:Bs,backendName:"wasm",setupFunc:Tte,kernelFunc:Ste},m2;function _te(e){m2=e.wasm.cwrap(Xo,null,["number","number","number","array","number","array","array","number","number"])}function Ete(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),v=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),x=t.dataIdMap.get(f.dataId).id;return m2(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,v,m.length,x),f}var Ate={kernelName:Xo,backendName:"wasm",setupFunc:_te,kernelFunc:Ete},f2;function Fte(e){f2=e.wasm.cwrap(Ws,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function $te(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:p}=n,d=c==null?[1,1]:c,h=_.computeConv2DInfo(r.shape,s.shape,l,d,u,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,N=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,$=h.inChannels,F=h.outChannels,O=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let B=a.makeOutput(h.outShape,"float32"),W=a.dataIdMap.get(B.dataId).id;return f2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,v,O,x,N,T,C,$,F,W),B}var Dte={kernelName:Ws,backendName:"wasm",setupFunc:Fte,kernelFunc:$te},Rte=!1,Mte=gn(Jo,Rte,"bool"),Pte=Vn(Us);function lw(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Pa({inputs:{x:r},backend:a,attrs:{shape:o}})}var Ote={kernelName:Qo,backendName:"wasm",kernelFunc:lw};function Lte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var zte={kernelName:wc,backendName:"wasm",kernelFunc:Lte},g2;function Bte(e){g2=e.wasm.cwrap(tl,null,["number","number","number","number","number","number"])}function Wte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return g2(s,o,l,c,u,i),r}var Vte={kernelName:tl,backendName:"wasm",kernelFunc:Wte,setupFunc:Bte},Ute=Vn(Gs),Gte=!1,Hte=gn(Hs,Gte),y2;function jte(e){y2=e.wasm.cwrap(js,null,["number","number","number","number","number","number","number"])}function qte(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return y2(u,p,d,h,m,r,g),f}var Kte={kernelName:js,backendName:"wasm",setupFunc:jte,kernelFunc:qte},b2;function Xte(e){b2=e.wasm.cwrap(Ii,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Yte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=$p[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,$=f.padInfo.right,F=f.padInfo.bottom,O=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,H=f.inChannels,K=f.padInfo.type==="SAME"?1:0,X=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return b2(y,X,ne,J,b,N,T,x,C,$,F,O,K,B,W,G,q,H,v,g,oe,m||0,Q),se}var Zte={kernelName:Ii,backendName:"wasm",setupFunc:Xte,kernelFunc:Yte},x2;function Jte(e){x2=e.wasm.cwrap(Ni,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d,!0),g=$p[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,$=f.padInfo.right,F=f.padInfo.bottom,O=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,H=f.inChannels,K=f.padInfo.type==="SAME"?1:0,X=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return x2(y,X,ne,J,b,N,T,x,C,$,F,O,K,B,W,G,q,H,v,g,oe,m||0,Q),se}var ene={kernelName:Ni,backendName:"wasm",setupFunc:Jte,kernelFunc:Qte},v2;function tne(e){v2=e.wasm.cwrap(al,null,["number","number","number","number","number","number","array","number"])}function nne(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Fy.prepareAndValidate(a,r),c=t.makeOutput(s,a.dtype);if(i===0)return c;let u=r.shape,p=u[u.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(c.dataId).id;return v2(d,Wn[a.dtype],h,i,p,o,m,f),c}var ane={kernelName:al,backendName:"wasm",setupFunc:tne,kernelFunc:nne},w2;function rne(e){w2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function sne(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=Pa({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=Pa({inputs:{x:s},attrs:{shape:[c.batchSize,p/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize],m=t.makeOutput(h,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=u.shape.length-1,g=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return w2(g,Wn[r.dtype],v,f,y,c.batchSize,x,b),m.shape=c.outputShape,m}var ine={kernelName:nl,backendName:"wasm",setupFunc:rne,kernelFunc:sne},one=!1,lne=gn(rl,one,"bool"),une=!1,cne=gn(qs,une,"bool"),k2;function pne(e){k2=e.wasm.cwrap(Ks,null,["number","number","number"])}function dne(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;k2(r,n,i)}return s}var hne={kernelName:Ks,backendName:"wasm",setupFunc:pne,kernelFunc:dne},mne=!1,fne=gn(ul,mne,"bool"),gne=!1,yne=gn(cl,gne,"bool"),bne=Vn(Xs),xne=!1,vne=gn(dl,xne,"bool"),I2;function wne(e){I2=e.wasm.cwrap(Ys,null,["number, number, number"])}function kne(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:p,inputWasTransposed:d}=Cu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;_.assertAxesAreInnerMostDims("max",u,h);let[m,f]=_.computeOutAndReduceShapes(l.shape,u),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;I2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Ine={kernelName:Ys,backendName:"wasm",setupFunc:wne,kernelFunc:kne},Nne=!1,Tne=gn(Zs,Nne),N2;function Sne(e){N2=e.wasm.cwrap(Js,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Cne(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=_.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,b=u.dilationWidth,v=u.strideHeight,x=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(u.outShape,"float32"),$=a.dataIdMap.get(C.dataId).id;return N2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,v,x,N,T,$),C}var _ne={kernelName:Js,backendName:"wasm",setupFunc:Sne,kernelFunc:Cne},T2;function Ene(e){T2=e.wasm.cwrap(Qs,null,["number, number, number"])}function Ane(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=Cu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=ff({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let v=t.makeOutput(f,"float32");if(k.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(v.dataId).id;T2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var Fne={kernelName:Qs,backendName:"wasm",setupFunc:Ene,kernelFunc:Ane},S2;function $ne(e){S2=e.wasm.cwrap(ei,null,["number, number, number"])}function Dne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=Cu(i,r,t);if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v)}let m=c.shape.length;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.computeOutAndReduceShapes(c.shape,p),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;S2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var Rne={kernelName:ei,backendName:"wasm",setupFunc:$ne,kernelFunc:Dne},Mne=!1,Pne=gn(ti,Mne),One=!0,Lne=gn(ni,One),zne=Vn(ml);function uw(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var C2;function Bne(e){C2=e.wasm.cwrap(gl,"number",["number","number","number","number","number"])}function Wne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,c=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(l.dataId).id,p=C2(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=uw(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var Vne={kernelName:gl,backendName:"wasm",setupFunc:Bne,kernelFunc:Wne},_2;function Une(e){_2=e.wasm.cwrap(yl,"number",["number","number","number","number","number","bool"])}function Gne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=_2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=uw(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var Hne={kernelName:yl,backendName:"wasm",setupFunc:Une,kernelFunc:Gne},E2;function jne(e){E2=e.wasm.cwrap(bl,"number",["number","number","number","number","number","number"])}function qne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=E2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=uw(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var 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mae={kernelName:wl,backendName:"wasm",setupFunc:dae,kernelFunc:hae},fae=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=Rv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},gae={kernelName:Cc,backendName:"wasm",kernelFunc:fae},yae=!0,bae=gn(Vs,yae),xae=Vn(oi),vae=Vn(ui),R2;function wae(e){R2=e.wasm.cwrap(li,null,["number","number","number","number","number","number","number","number","number","number"])}function kae(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,[u,p,d,h]=r.shape,m=[u,l,c,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=ff({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return b;let v=t.dataIdMap.get(b.dataId).id;return R2(y,u,p,d,h,l,c,s?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),b}var Iae={kernelName:li,backendName:"wasm",setupFunc:wae,kernelFunc:kae},M2;function Nae(e){M2=e.wasm.cwrap(ci,null,["number","array","number","array","number","number"])}function Tae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=k.parseAxisParam(s,r.shape);if(r.shape.length===0)return hf({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);return M2(l,u,i.length,p,r.shape.length,c),Pa({inputs:{x:o},attrs:{shape:r.shape},backend:n})}var Sae={kernelName:ci,backendName:"wasm",kernelFunc:Tae,setupFunc:Nae},P2;function Cae(e){P2=e.wasm.cwrap(Ol,null,["number","number","number","number","number","number","number","number","array","number","number"])}function 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Yae={kernelName:Fl,backendName:"wasm",kernelFunc:Xae},Zae=Vn(fi),Jae=Vn(Ac),Qae=!0,ere=gn(bi,Qae),W2;function tre(e){W2=e.wasm.cwrap(Hr,null,["number","number","number"])}function nre(e){let{backend:t,inputs:n,attrs:a}=e,{alpha:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return W2(i,r,l),o}var are={kernelName:Hr,backendName:"wasm",setupFunc:tre,kernelFunc:nre},V2;function rre(e){V2=e.wasm.cwrap($l,null,["number","array","number","array","array","array","array","array","number","number"])}function sre(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{begin:s,end:i,strides:o}=a;o==null&&(o=new Array(s.length));let{beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:p,shrinkAxisMask:d}=a,h=_.slice_util.maskToAxes(u);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(u!==0&&p!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(u!==0&&d!==0)throw new Error("Using both 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this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new lr(t.label,n)}};var gw=class extends Fu{static assertIsValidPredictedBox(t,n){if(Fu.assertIsValidLabeledBox(t,n),!_u(t.score)||!_u(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,a,r){super(t,n);this._score=a,this._classScore=r}get score(){return this._score}get classScore(){return this._classScore}};function La(e){return e.detection instanceof yt}function gs(e,t){return{...e,...{detection:t}}}function yw(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser environment");let t=()=>{throw new Error("readFile - filesystem not available for browser 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ur;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(ur||(ur={}));var Pp=class{constructor(t={}){let{anchorPosition:n,backgroundColor:a,fontColor:r,fontSize:s,fontStyle:i,padding:o}=t;this.anchorPosition=n||ur.TOP_LEFT,this.backgroundColor=a||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=s||14,this.fontStyle=i||"Georgia",this.padding=o||4}},bs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof bs?t.text:t,this.anchor=n,this.options=new Pp(a)}measureWidth(t){let{padding:n}=this.options;return this.text.map(a=>t.measureText(a).width).reduce((a,r)=>a<r?r:a,0)+2*n}measureHeight(){let{fontSize:t,padding:n}=this.options;return this.text.length*t+2*n}getUpperLeft(t,n){let{anchorPosition:a}=this.options,r=a===ur.BOTTOM_RIGHT||a===ur.TOP_RIGHT,s=a===ur.BOTTOM_LEFT||a===ur.BOTTOM_RIGHT,i=this.measureWidth(t),o=this.measureHeight(),l=r?this.anchor.x-i:this.anchor.x,c=s?this.anchor.y-o:this.anchor.y;if(n){let{width:u,height:p}=n,d=Math.max(Math.min(l,u-i),0),h=Math.max(Math.min(c,p-o),0);return{x:d,y:h}}return{x:l,y:c}}draw(t){let n=ys(t),a=yn(n),{backgroundColor:r,fontColor:s,fontSize:i,fontStyle:o,padding:l}=this.options;a.font=`${i}px ${o}`;let c=this.measureWidth(a),u=this.measureHeight();a.fillStyle=r;let p=this.getUpperLeft(a,n);a.fillRect(p.x,p.y,c,u),a.fillStyle=s,this.text.forEach((d,h)=>{let m=l+p.x,f=l+p.y+(h+1)*i;a.fillText(d,m,f)})}};var Iw=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:ur.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Pp({...i,...s})}},Sf=class{constructor(t,n={}){this.box=new ot(t),this.options=new Iw(n)}draw(t){let n=yn(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:c}=this.options;c&&new bs([c],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function Vre(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof yt?a.score:La(a)?a.detection.score:void 0,s=a instanceof yt?a.box:La(a)?a.detection.box:new ot(a),i=r?`${eo(r)}`:void 0;new Sf(s,{label:i}).draw(e)})}function $u(e){let{Image:t,Video:n}=rt.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function Cf(e){return new Promise((t,n)=>{if(e instanceof rt.getEnv().Canvas||$u(e))return t(null);function a(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function 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d=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:d,separable_conv:h};return bn(e,n),{params:{entry_flow:u,middle_flow:p,exit_flow:m},paramMappings:n}}function uC(e,t,n){return Z(At(e,t.filters,n,"same"),t.bias)}function Sw(e,t,n=!0){let a=n?Ye(e):e;return a=Cn(a,t.separable_conv0,[1,1]),a=Cn(Ye(a),t.separable_conv1,[1,1]),a=Ft(a,[3,3],[2,2],"same"),a=Z(a,uC(e,t.expansion_conv,[2,2])),a}function qre(e,t){let n=Cn(Ye(e),t.separable_conv0,[1,1]);return n=Cn(Ye(n),t.separable_conv1,[1,1]),n=Cn(Ye(n),t.separable_conv2,[1,1]),n=Z(n,e),n}var Cw=class extends Qt{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return D(()=>{let a=ce(t.toBatchTensor(112,!0),"float32"),s=va(a,[122.782,117.001,104.298]).div(de(256)),i=Ye(uC(s,n.entry_flow.conv_in,[2,2]));return i=Sw(i,n.entry_flow.reduction_block_0,!1),i=Sw(i,n.entry_flow.reduction_block_1),or(this._numMainBlocks,0,1).forEach(o=>{i=qre(i,n.middle_flow[`main_block_${o}`])}),i=Sw(i,n.exit_flow.reduction_block),i=Ye(Cn(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await gt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return lC(t,this._numMainBlocks)}extractParams(t){return oC(t,this._numMainBlocks)}};function cC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),r=Mf(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function pC(e){let t=[],n=Gn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return bn(e,t),{params:r,paramMappings:t}}var pr;(function(e){e.FEMALE="female",e.MALE="male"})(pr||(pr={}));var Gp=class extends Qt{constructor(t=new Cw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof cr?this.faceFeatureExtractor.forwardInput(t):t,r=Yn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Bp(r,n.fc.age).as1D(),i=Bp(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return D(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:Ta(a)}})}async forward(t){return this.forwardInput(await gt(t))}async predictAgeAndGender(t){let n=await gt(t),a=await this.forwardInput(n),r=dt(a.age),s=dt(a.gender),i=r.map((l,c)=>({ageTensor:l,genderTensor:s[c]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:c})=>{let u=(await l.data())[0],p=(await c.data())[0],d=p>.5,h=d?pr.MALE:pr.FEMALE,m=d?p:1-p;return 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RC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=xn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=rse(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,v]=a,x=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),C=c(m,f,"conv3"),$=c(f,g,"conv4"),F=c(g,y,"conv5"),O=b?c(y,b,"conv6"):void 0,B=v?c(b,v,"conv7"):void 0,W=o(v||b||y,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:B,conv8:W}}else{let[p,d,h,m,f,g,y,b,v]=a,x=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),$=l(f,g,"conv4"),F=l(g,y,"conv5"),O=l(y,b,"conv6"),B=l(b,v,"conv7"),W=o(v,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:B,conv8:W}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function sse(e,t){let n=Gn(e,t);function a(o){let l=n(`${o}/sub`,1),c=n(`${o}/truediv`,1);return{sub:l,truediv:c}}function r(o){let l=n(`${o}/filters`,4),c=n(`${o}/bias`,1);return{filters:l,bias:c}}function s(o){let l=r(`${o}/conv`),c=a(`${o}/bn`);return{conv:l,bn:c}}let i=Pu(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function MC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=sse(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return bn(e,n),{params:i,paramMappings:n}}var Ba=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Rw=class extends Qt{constructor(t){super("TinyYolov2");jf(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=$r(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=$r(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=$r(a,n.conv6),a=$r(a,n.conv7),lo(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Ou(lo(t,n.conv0,"valid",!1)):Dr(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=n.conv6?Dr(a,n.conv6):a,a=n.conv7?Dr(a,n.conv7):a,lo(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return D(()=>{let r=ce(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?va(r,this.config.meanRgb):r,r=r.div(de(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await gt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Ba(n),s=await gt(t),i=await this.forwardInput(s,a),o=D(()=>dt(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},c=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let u=c.map(g=>g.box),p=c.map(g=>g.score),d=c.map(g=>g.classScore),h=c.map(g=>this.config.classes[g.label]);return If(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new Ar(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return MC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Rw.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return RC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?Ta(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):de(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;y<c;y++)for(let b=0;b<c;b++)for(let v=0;v<u;v++){let x=Eu(f[y][b][v][0]);if(!a||x>a){let N=(b+Eu(g[y][b][v][0]))/c*o,T=(y+Eu(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,$=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,F=N-C/2,O=T-$/2,B={row:y,col:b,anchor:v},{classScore:W,label:G}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new no(F,O,F+C,O+$),score:x,classScore:x*W,label:G,...B})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Lu=Rw;Lu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var mo=class extends Lu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:_C,classes:["face"],...t?{anchors:AC,meanRgb:FC}:{anchors:EC,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?DC:$C}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function PC(e,t=!0){let n=new mo(t);return n.extractWeights(e),n}var Zp=class extends Ba{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var aa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function fo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>ws(l)?r(l):l.detection),i=a||(t instanceof Ae?await oo(t,s):await io(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ae&&l.dispose()),o}async function zu(e,t,n,a,r){return fo([e],t,async s=>n(s[0]),a,r)}var OC=.4,LC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],zC=[117.001,114.697,97.404];var go=class extends Lu{constructor(){let t={withSeparableConvs:!0,iouThreshold:OC,classes:["face"],anchors:LC,meanRgb:zC,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var et={ssdMobilenetv1:new ks,tinyFaceDetector:new go,tinyYolov2:new mo,faceLandmark68Net:new co,faceLandmark68TinyNet:new jp,faceRecognitionNet:new po,faceExpressionNet:new Vp,ageGenderNet:new Gp},Mw=(e,t)=>et.ssdMobilenetv1.locateFaces(e,t),BC=(e,t)=>et.tinyFaceDetector.locateFaces(e,t),WC=(e,t)=>et.tinyYolov2.locateFaces(e,t),Pw=e=>et.faceLandmark68Net.detectLandmarks(e),VC=e=>et.faceLandmark68TinyNet.detectLandmarks(e),UC=e=>et.faceRecognitionNet.computeFaceDescriptor(e),GC=e=>et.faceExpressionNet.predictExpressions(e),HC=e=>et.ageGenderNet.predictAgeAndGender(e),Ow=e=>et.ssdMobilenetv1.load(e),jC=e=>et.tinyFaceDetector.load(e),qC=e=>et.tinyYolov2.load(e),KC=e=>et.faceLandmark68Net.load(e),XC=e=>et.faceLandmark68TinyNet.load(e),YC=e=>et.faceRecognitionNet.load(e),ZC=e=>et.faceExpressionNet.load(e),JC=e=>et.ageGenderNet.load(e),QC=Ow,e_=Mw,t_=Pw;var Lw=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Vu=class extends Lw{async run(){let t=await this.parentTask,n=await fo(t,this.input,async a=>Promise.all(a.map(r=>et.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Up(a,n[r]))}withAgeAndGender(){return new Bu(this,this.input)}},Uu=class extends Lw{async run(){let t=await this.parentTask;if(!t)return;let n=await zu(t,this.input,a=>et.faceExpressionNet.predictExpressions(a),this.extractedFaces);return Up(t,n)}withAgeAndGender(){return new Wu(this,this.input)}},xo=class extends Vu{withAgeAndGender(){return new yo(this,this.input)}withFaceDescriptors(){return new Rr(this,this.input)}},vo=class extends Uu{withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptor(){return new Mr(this,this.input)}};var zw=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Bu=class extends zw{async run(){let t=await this.parentTask,n=await fo(t,this.input,async a=>Promise.all(a.map(r=>et.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Xp(Yp(a,i,o),s)})}withFaceExpressions(){return new Vu(this,this.input)}},Wu=class extends zw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await zu(t,this.input,s=>et.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Xp(Yp(t,a,r),n)}withFaceExpressions(){return new Uu(this,this.input)}},yo=class extends Bu{withFaceExpressions(){return new xo(this,this.input)}withFaceDescriptors(){return new Rr(this,this.input)}},bo=class extends Wu{withFaceExpressions(){return new vo(this,this.input)}withFaceDescriptor(){return new Mr(this,this.input)}};var Jp=class extends aa{constructor(t,n){super();this.parentTask=t;this.input=n}},Rr=class extends Jp{async run(){let t=await this.parentTask;return(await fo(t,this.input,a=>Promise.all(a.map(r=>et.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Kp(t[r],a))}withFaceExpressions(){return new xo(this,this.input)}withAgeAndGender(){return new yo(this,this.input)}},Mr=class extends Jp{async run(){let t=await this.parentTask;if(!t)return;let n=await zu(t,this.input,a=>et.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return Kp(t,n)}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}};var Qp=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?et.faceLandmark68TinyNet:et.faceLandmark68Net}},ed=class extends Qp{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ae?await oo(this.input,n):await io(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ae&&s.dispose()),t.map((s,i)=>uo(s,r[i]))}withFaceExpressions(){return new xo(this,this.input)}withAgeAndGender(){return new yo(this,this.input)}withFaceDescriptors(){return new Rr(this,this.input)}},td=class extends Qp{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ae?await oo(this.input,[n]):await io(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ae&&s.dispose()),uo(t,r)}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptor(){return new Mr(this,this.input)}};var nd=class extends aa{constructor(t,n=new na){super();this.input=t;this.options=n}},Gu=class extends nd{async run(){let{input:t,options:n}=this,a=n instanceof Zp?r=>et.tinyFaceDetector.locateFaces(r,n):n instanceof na?r=>et.ssdMobilenetv1.locateFaces(r,n):n instanceof Ba?r=>et.tinyYolov2.locateFaces(r,n):null;if(!a)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return a(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>gs({},a)))})}withFaceLandmarks(t=!1){return new ed(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Vu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Bu(this.runAndExtendWithFaceDetections(),this.input)}},ad=class extends nd{async run(){let t=await new Gu(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?gs({},n):void 0)})}withFaceLandmarks(t=!1){return new td(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Uu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Wu(this.runAndExtendWithFaceDetection(),this.input)}};function n_(e,t=new na){return new ad(e,t)}function rd(e,t=new na){return new Gu(e,t)}async function Bw(e,t){return rd(e,new na(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function a_(e,t={}){return rd(e,new Ba(t)).withFaceLandmarks().withFaceDescriptors()}var r_=Bw;function qf(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var sd=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof lr)return i;if(i instanceof Float32Array)return new lr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new lr(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>qf(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Au(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distance<a.distance?n:a)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this.distanceThreshold?n:new Au("unknown",n.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>lr.fromJSON(a));return new sd(n,t.distanceThreshold)}};function s_(e){let t=new go;return t.extractWeights(e),t}function Ww(e,t){let{width:n,height:a}=new ln(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>Ww(r,{width:n,height:a}));if(ws(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return uo(gs(e,r),s)}return La(e)?gs(e,e.detection.forSize(n,a)):e instanceof Un||e instanceof yt?e.forSize(n,a):e}var ose=typeof process!="undefined",lse=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",i_={faceapi:iC,node:ose,browser:lse};return ise;})();
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New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var ute={kernelName:Il,backendName:"wasm",kernelFunc:Oa},h2;function cte(e){h2=e.wasm.cwrap(Ms,null,["number","array","number","number","array","number","number","number","number"])}function pte(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=s.shape.length,u=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[c-1]:s.shape[c-2],d=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[c-2]:s.shape[c-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),b=g===y||g===1||y===1;k.assert(l>=2&&c>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. 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Please use 'channelsLast'.`);let W=a.makeOutput(m.outShape,"float32"),G=a.dataIdMap.get(W.dataId).id;return g2(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,b,v,x,B,N,T,C,F,$,P,G),W}var wte={kernelName:Ls,backendName:"wasm",setupFunc:xte,kernelFunc:vte},y2;function kte(e){y2=e.wasm.cwrap(zs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ite(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,inputShape:u}=a,p=1,d=_.convertConv2DDataFormat(l),h=_.computeConv2DInfo(u,s.shape,i,p,o,c,!1,d),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:b,inWidth:v,outChannels:x,outHeight:N,outWidth:T,strideHeight:C,strideWidth:F}=h,$=f-1-h.padInfo.top,P=g-1-h.padInfo.left,B=h.dataFormat==="channelsLast",W=k.computeStrides(h.inShape),G=k.computeStrides(r.shape),[q,j,X]=k.computeStrides(s.shape),K=W[0],ne=B?W[1]:W[2],J=B?W[2]:1,se=B?1:W[1],Q=G[0],oe=B?G[1]:G[2],ae=B?G[2]:1,pe=B?1:G[1],ue=t.makeOutput(h.inShape,"float32"),ge=t.dataIdMap.get(ue.dataId).id,ye=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(s.dataId).id;return y2(ye,ke,m,f,g,b,v,y,N,T,x,C,F,$,P,q,j,X,K,ne,J,se,Q,oe,ae,pe,ge),ue}var Nte={kernelName:zs,backendName:"wasm",setupFunc:kte,kernelFunc:Ite},Tte=Cn(Bs),hw;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(hw||(hw={}));var b2;function Ste(e){b2=e.wasm.cwrap(Xo,null,["number","number","number","number","array","number","number","number","number","number"])}function Cte(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:c}=n,u=l.shape[0],[p,d]=i,h=[u,p,d,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=bf({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(l.dataId).id,b=t.dataIdMap.get(c.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,N=new Uint8Array(new Int32Array(o.shape).buffer);return b2(g,y,b,u,N,p,d,hw[r],s,x),f!=null&&t.disposeData(f.dataId),v}var _te={kernelName:Xo,backendName:"wasm",setupFunc:Ste,kernelFunc:Cte},x2;function Ete(e){x2=e.wasm.cwrap(Ws,null,["number","number","number","number","number","number"])}function Ate(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;k.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=_.getAxesPermutation([s],l),u=r;c!==null&&(u=yf({inputs:{x:r},attrs:{perm:c},backend:n}));let p=_.getInnerMostAxes(1,l)[0];_.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(u.shape,u.dtype),h=u.shape[p],m=n.dataIdMap.get(u.dataId).id,f=n.dataIdMap.get(d.dataId).id;x2(m,i?1:0,o?1:0,h,f,Vn[r.dtype]);let g=d;if(c!==null){let y=_.getUndoAxesPermutation(c);g=yf({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(d.dataId)}return g}var Fte={kernelName:Ws,backendName:"wasm",setupFunc:Ete,kernelFunc:Ate},v2;function $te(e){v2=e.wasm.cwrap(Yo,null,["number","number","number","array","number","array","array","number","number"])}function Dte(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),v=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),x=t.dataIdMap.get(f.dataId).id;return v2(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,v,m.length,x),f}var Rte={kernelName:Yo,backendName:"wasm",setupFunc:$te,kernelFunc:Dte},w2;function Mte(e){w2=e.wasm.cwrap(Vs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Pte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:p}=n,d=c==null?[1,1]:c,h=_.computeConv2DInfo(r.shape,s.shape,l,d,u,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,N=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,F=h.inChannels,$=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let B=a.makeOutput(h.outShape,"float32"),W=a.dataIdMap.get(B.dataId).id;return w2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,v,P,x,N,T,C,F,$,W),B}var Ote={kernelName:Vs,backendName:"wasm",setupFunc:Mte,kernelFunc:Pte},Lte=!1,zte=gn(Qo,Lte,"bool"),Bte=Cn(Gs);function mw(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Oa({inputs:{x:r},backend:a,attrs:{shape:o}})}var Wte={kernelName:el,backendName:"wasm",kernelFunc:mw};function Vte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Ute={kernelName:vc,backendName:"wasm",kernelFunc:Vte},k2;function Gte(e){k2=e.wasm.cwrap(nl,null,["number","number","number","number","number","number"])}function Hte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return k2(s,o,l,c,u,i),r}var jte={kernelName:nl,backendName:"wasm",kernelFunc:Hte,setupFunc:Gte},qte=Cn(Hs),Kte=!1,Xte=gn(js,Kte),I2;function Yte(e){I2=e.wasm.cwrap(qs,null,["number","number","number","number","number","number","number"])}function Zte(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return I2(u,p,d,h,m,r,g),f}var Jte={kernelName:qs,backendName:"wasm",setupFunc:Yte,kernelFunc:Zte},N2;function Qte(e){N2=e.wasm.cwrap(Ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ene(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Dp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return N2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var tne={kernelName:Ti,backendName:"wasm",setupFunc:Qte,kernelFunc:ene},T2;function nne(e){T2=e.wasm.cwrap(Si,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ane(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d,!0),g=Dp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${v})`);x=ae.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,F=f.padInfo.right,$=f.padInfo.bottom,P=f.padInfo.left,B=f.dilationHeight,W=f.dilationWidth,G=f.strideHeight,q=f.strideWidth,j=f.inChannels,X=f.padInfo.type==="SAME"?1:0,K=f.batchSize,ne=f.inHeight,J=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(se.dataId).id,oe=o==null?0:a.dataIdMap.get(o.dataId).id;return T2(y,K,ne,J,b,N,T,x,C,F,$,P,X,B,W,G,q,j,v,g,oe,m||0,Q),se}var rne={kernelName:Si,backendName:"wasm",setupFunc:nne,kernelFunc:ane},S2;function sne(e){S2=e.wasm.cwrap(rl,null,["number","number","number","number","number","number","array","number"])}function ine(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Ry.prepareAndValidate(a,r),c=t.makeOutput(s,a.dtype);if(i===0)return c;let u=r.shape,p=u[u.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(c.dataId).id;return S2(d,Vn[a.dtype],h,i,p,o,m,f),c}var one={kernelName:rl,backendName:"wasm",setupFunc:sne,kernelFunc:ine},C2;function lne(e){C2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function une(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=Oa({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=Oa({inputs:{x:s},attrs:{shape:[c.batchSize,p/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize],m=t.makeOutput(h,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=u.shape.length-1,g=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return C2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),t.disposeData(u.dataId),t.disposeData(d.dataId),m.shape=c.outputShape,m}var cne={kernelName:al,backendName:"wasm",setupFunc:lne,kernelFunc:une},pne=!1,dne=gn(sl,pne,"bool"),hne=!1,mne=gn(Ks,hne,"bool"),_2;function fne(e){_2=e.wasm.cwrap(Ys,null,["number","number","number"])}function gne(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;_2(r,n,i)}return s}var yne={kernelName:Ys,backendName:"wasm",setupFunc:fne,kernelFunc:gne},bne=!1,xne=gn(ul,bne,"bool"),vne=!1,wne=gn(cl,vne,"bool"),kne=Cn(Zs),Ine=!1,Nne=gn(dl,Ine,"bool"),E2;function Tne(e){E2=e.wasm.cwrap(Js,null,["number, number, number"])}function Sne(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:p,inputWasTransposed:d}=Cu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;_.assertAxesAreInnerMostDims("max",u,h);let[m,f]=_.computeOutAndReduceShapes(l.shape,u),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;E2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Cne={kernelName:Js,backendName:"wasm",setupFunc:Tne,kernelFunc:Sne},_ne=!1,Ene=gn(Qs,_ne),A2;function Ane(e){A2=e.wasm.cwrap(ei,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Fne(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=_.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,b=u.dilationWidth,v=u.strideHeight,x=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(u.outShape,"float32"),F=a.dataIdMap.get(C.dataId).id;return A2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,v,x,N,T,F),C}var $ne={kernelName:ei,backendName:"wasm",setupFunc:Ane,kernelFunc:Fne},F2;function Dne(e){F2=e.wasm.cwrap(ti,null,["number, number, number"])}function Rne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=Cu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=bf({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let v=t.makeOutput(f,"float32");if(k.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(v.dataId).id;F2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(v.shape,d);v.shape=x}return c.dtype!=="float32"&&t.disposeData(b.dataId),v}var Mne={kernelName:ti,backendName:"wasm",setupFunc:Dne,kernelFunc:Rne},$2;function Pne(e){$2=e.wasm.cwrap(ni,null,["number, number, number"])}function One(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=Cu(i,r,t);if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v)}let m=c.shape.length;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.computeOutAndReduceShapes(c.shape,p),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;$2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var Lne={kernelName:ni,backendName:"wasm",setupFunc:Pne,kernelFunc:One},zne=!1,Bne=gn(ai,zne),Wne=!0,Vne=gn(ri,Wne),Une=Cn(ml);function fw(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var D2;function Gne(e){D2=e.wasm.cwrap(gl,"number",["number","number","number","number","number"])}function Hne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,c=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(l.dataId).id,p=D2(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=fw(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var jne={kernelName:gl,backendName:"wasm",setupFunc:Gne,kernelFunc:Hne},R2;function qne(e){R2=e.wasm.cwrap(yl,"number",["number","number","number","number","number","bool"])}function Kne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=R2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=fw(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var Xne={kernelName:yl,backendName:"wasm",setupFunc:qne,kernelFunc:Kne},M2;function Yne(e){M2=e.wasm.cwrap(bl,"number",["number","number","number","number","number","number"])}function Zne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=M2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=fw(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var Jne={kernelName:bl,backendName:"wasm",setupFunc:Yne,kernelFunc:Zne},Qne=!1,eae=gn(fl,Qne,"bool"),P2;function tae(e){P2=e.wasm.cwrap(si,null,["number","number","number","number","number"])}function nae(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),c=n.dataIdMap.get(l.dataId).id,u=n.dataIdMap.get(r.dataId).id;return P2(u,s,i,o,c),l}var aae={kernelName:si,backendName:"wasm",setupFunc:tae,kernelFunc:nae};function rae(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var sae={kernelName:xl,backendName:"wasm",kernelFunc:rae};function iae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return mw({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(l=>{k.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=t.map(l=>mw({inputs:{input:l},backend:n,attrs:{dim:r}}));return f2({inputs:o,backend:n,attrs:{axis:r}})}var oae={kernelName:vl,backendName:"wasm",kernelFunc:iae},O2;function lae(e){O2=e.wasm.cwrap(ii,null,["number","array","number","number","array","array","number","number"])}function uae(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,constantValue:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=a.map(m=>m[0]),p=a.map(m=>m[1]),d=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(p).buffer);return O2(i,c,t.shape.length,Vn[t.dtype],d,h,r,l),o}var cae={kernelName:ii,backendName:"wasm",kernelFunc:uae,setupFunc:lae},pae=!1,dae=gn(oi,pae),L2;function hae(e){L2=e.wasm.cwrap(li,null,["number","number","number"])}function mae(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=n.makeOutput(a.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return L2(s,i,l),o}var fae={kernelName:li,backendName:"wasm",setupFunc:hae,kernelFunc:mae},z2;function gae(e){z2=e.wasm.cwrap(wl,null,["number","number","number","number"])}function yae(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=Cu(i,r,t),m=p;if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("prod",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;z2(l,y,Vn[b.dtype],v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var bae={kernelName:wl,backendName:"wasm",setupFunc:gae,kernelFunc:yae},xae=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=zv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},vae={kernelName:Sc,backendName:"wasm",kernelFunc:xae},wae=!0,kae=gn(Us,wae),Iae=Cn(ui),Nae=Cn(pi),B2;function Tae(e){B2=e.wasm.cwrap(ci,null,["number","number","number","number","number","number","number","number","number","number"])}function Sae(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,[u,p,d,h]=r.shape,m=[u,l,c,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=bf({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return b;let v=t.dataIdMap.get(b.dataId).id;return B2(y,u,p,d,h,l,c,s?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),b}var Cae={kernelName:ci,backendName:"wasm",setupFunc:Tae,kernelFunc:Sae},W2;function _ae(e){W2=e.wasm.cwrap(di,null,["number","array","number","array","number","number"])}function Eae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=k.parseAxisParam(s,r.shape);if(r.shape.length===0)return gf({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);W2(l,u,i.length,p,r.shape.length,c);let d=Oa({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var Aae={kernelName:di,backendName:"wasm",kernelFunc:Eae,setupFunc:_ae},V2;function Fae(e){V2=e.wasm.cwrap(Ol,null,["number","number","number","number","number","number","number","number","array","number","number"])}function $ae(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(l.dataId).id,[p,d,h,m]=r.shape,[f,g]=_.getImageCenter(o,d,h),y=i===0,b=255,v=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],x=new Uint8Array(new Int32Array(v).buffer);return V2(c,p,d,h,m,s,f,g,x,v.length,u),l}var Dae={kernelName:Ol,backendName:"wasm",kernelFunc:$ae,setupFunc:Fae},Rae=Cn(hi),Mae=Cn(mi),U2;function Pae(e){U2=e.wasm.cwrap(Nl,null,["number","number","number","number","number","number","array","number","number"])}function Oae(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(k.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:c,sliceSize:u,strides:p,outputSize:d}=My.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(p).buffer),g=t.dataIdMap.get(o.dataId).id;return 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De(this.left,this.bottom)}get bottomRight(){return new De(this.right,this.bottom)}round(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.round(s));return new ot({x:t,y:n,width:a,height:r})}floor(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.floor(s));return new ot({x:t,y:n,width:a,height:r})}toSquare(){let{x:t,y:n,width:a,height:r}=this,s=Math.abs(a-r);return a<r&&(t-=s/2,a+=s),r<a&&(n-=s/2,r+=s),new ot({x:t,y:n,width:a,height:r})}rescale(t){let n=If(t)?t.width:t,a=If(t)?t.height:t;return new ot({x:this.x*n,y:this.y*a,width:this.width*n,height:this.height*a})}pad(t,n){let[a,r,s,i]=[this.x-t/2,this.y-n/2,this.width+t,this.height+n];return new ot({x:a,y:r,width:s,height:i})}clipAtImageBorders(t,n){let{x:a,y:r,right:s,bottom:i}=this,o=Math.max(a,0),l=Math.max(r,0),c=s-o,u=i-l,p=Math.min(c,t-o),d=Math.min(u,n-l);return new ot({x:o,y:l,width:p,height:d}).floor()}shift(t,n){let{width:a,height:r}=this,s=this.x+t,i=this.y+n;return new ot({x:s,y:i,width:a,height:r})}padAtBorders(t,n){let a=this.width+1,r=this.height+1,s=1,i=1,o=a,l=r,c=this.left,u=this.top,p=this.right,d=this.bottom;return p>n&&(o=-p+n+a,p=n),d>t&&(l=-d+t+r,d=t),c<1&&(l=2-c,c=1),u<1&&(l=2-u,u=1),{dy:i,edy:l,dx:s,edx:o,y:u,ey:d,x:c,ex:p,w:a,h:r}}calibrate(t){return new ot({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var ro=class extends ot{constructor(t,n,a,r,s=!1){super({left:t,top:n,right:a,bottom:r},s)}};var Fr=class{constructor(t,n,a,r,s){this._imageDims=new ln(s.width,s.height),this._score=t,this._classScore=n,this._className=a,this._box=new ot(r).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new ot(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new Fr(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var yt=class extends Fr{constructor(t,n,a){super(t,t,"",n,a)}forSize(t,n){let{score:a,relativeBox:r,imageDims:s}=super.forSize(t,n);return new yt(a,r,s)}};function Nf(e,t,n=!0){let a=Math.max(0,Math.min(e.right,t.right)-Math.max(e.left,t.left)),r=Math.max(0,Math.min(e.bottom,t.bottom)-Math.max(e.top,t.top)),s=a*r;return n?s/(e.area+t.area-s):s/Math.min(e.area,t.area)}function Tf(e){let t=e.map(o=>o.x),n=e.map(o=>o.y),a=t.reduce((o,l)=>l<o?l:o,Infinity),r=n.reduce((o,l)=>l<o?l:o,Infinity),s=t.reduce((o,l)=>o<l?l:o,0),i=n.reduce((o,l)=>o<l?l:o,0);return new ro(a,r,s,i)}function Sf(e,t,n,a=!0){let r=t.map((i,o)=>({score:i,boxIndex:o})).sort((i,o)=>i.score-o.score).map(i=>i.boxIndex),s=[];for(;r.length>0;){let i=r.pop();s.push(i);let o=r,l=[];for(let c=0;c<o.length;c++){let 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t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=tse(e,t),r=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Ar(r))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${r}`);let s={mobilenetv1:n(),prediction_layer:a(),output_layer:{extra_dim:r}};return bn(e,t),{params:s,paramMappings:t}}function ka(e,t,n){return D(()=>{let a=At(e,t.filters,n,"same");return a=Z(a,t.batch_norm_offset),qt(a,0,6)})}var nse=.0010000000474974513;function ase(e,t,n){return D(()=>{let a=es(e,t.filters,n,"same");return a=xr(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,nse),qt(a,0,6)})}function rse(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function EC(e,t){return D(()=>{let n,a=ka(e,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((s,i)=>{let o=i+1,l=rse(o);a=ase(a,s.depthwise_conv,l),a=ka(a,s.pointwise_conv,[1,1]),o===11&&(n=a)}),n===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:a,conv11:n}})}function sse(e,t,n){let a=e.arraySync(),r=Math.min(a[t][0],a[t][2]),s=Math.min(a[t][1],a[t][3]),i=Math.max(a[t][0],a[t][2]),o=Math.max(a[t][1],a[t][3]),l=Math.min(a[n][0],a[n][2]),c=Math.min(a[n][1],a[n][3]),u=Math.max(a[n][0],a[n][2]),p=Math.max(a[n][1],a[n][3]),d=(i-r)*(o-s),h=(u-l)*(p-c);if(d<=0||h<=0)return 0;let m=Math.max(r,l),f=Math.max(s,c),g=Math.min(i,u),y=Math.min(o,p),b=Math.max(g-m,0)*Math.max(y-f,0);return b/(d+h-b)}function AC(e,t,n,a,r){let s=e.shape[0],i=Math.min(n,s),o=t.map((u,p)=>({score:u,boxIndex:p})).filter(u=>u.score>r).sort((u,p)=>p.score-u.score),l=u=>u<=a?1:0,c=[];return o.forEach(u=>{if(c.length>=i)return;let p=u.score;for(let d=c.length-1;d>=0;--d){let h=sse(e,u.boxIndex,c[d]);if(h!==0&&(u.score*=l(h),u.score<=r))break}p===u.score&&c.push(u.boxIndex)}),c}function ise(e){let t=dt(Ue(e,[1,0])),n=[fe(t[2],t[0]),fe(t[3],t[1])],a=[Z(t[0],ve(n[0],de(2))),Z(t[1],ve(n[1],de(2)))];return{sizes:n,centers:a}}function ose(e,t){let{sizes:n,centers:a}=ise(e),r=dt(Ue(t,[1,0])),s=ve(L(dn(ve(r[2],de(5))),n[0]),de(2)),i=Z(L(ve(r[0],de(10)),n[0]),a[0]),o=ve(L(dn(ve(r[3],de(5))),n[1]),de(2)),l=Z(L(ve(r[1],de(10)),n[1]),a[1]);return Ue($t([fe(i,s),fe(l,o),Z(i,s),Z(l,o)]),[1,0])}function FC(e,t,n){return D(()=>{let a=e.shape[0],r=ose(H(Xa(n.extra_dim,[a,1,1]),[-1,4]),H(e,[-1,4]));r=H(r,[a,r.shape[0]/a,4]);let s=da(We(t,[0,0,1],[-1,-1,-1])),i=We(s,[0,0,0],[-1,-1,1]);i=H(i,[a,i.shape[1]]);let o=dt(r),l=dt(i);return{boxes:o,scores:l}})}function fo(e,t){return D(()=>{let n=e.shape[0],a=H(co(e,t.box_encoding_predictor),[n,-1,1,4]),r=H(co(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function $C(e,t,n){return D(()=>{let a=ka(e,n.conv_0,[1,1]),r=ka(a,n.conv_1,[2,2]),s=ka(r,n.conv_2,[1,1]),i=ka(s,n.conv_3,[2,2]),o=ka(i,n.conv_4,[1,1]),l=ka(o,n.conv_5,[2,2]),c=ka(l,n.conv_6,[1,1]),u=ka(c,n.conv_7,[2,2]),p=fo(t,n.box_predictor_0),d=fo(e,n.box_predictor_1),h=fo(r,n.box_predictor_2),m=fo(i,n.box_predictor_3),f=fo(l,n.box_predictor_4),g=fo(u,n.box_predictor_5),y=Qe([p.boxPredictionEncoding,d.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),b=Qe([p.classPrediction,d.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:y,classPredictions:b}})}var na=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var ks=class extends Qt{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return D(()=>{let a=ce(t.toBatchTensor(512,!1),"float32"),r=fe(L(a,de(.007843137718737125)),de(1)),s=EC(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=$C(s.out,s.conv11,n.prediction_layer);return FC(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await gt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new na(n),s=await gt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],c=o[0];for(let v=1;v<i.length;v++)i[v].dispose(),o[v].dispose();let u=Array.from(await c.data()),d=AC(l,u,a,.5,r),h=s.getReshapedInputDimensions(0),m=s.inputSize,f=m/h.width,g=m/h.height,y=l.arraySync(),b=d.map(v=>{let[x,N]=[Math.max(0,y[v][0]),Math.min(1,y[v][2])].map(F=>F*g),[T,C]=[Math.max(0,y[v][1]),Math.min(1,y[v][3])].map(F=>F*f);return new yt(u[v],new so(T,x,C-T,N-x),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),c.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return _C(t)}extractParams(t){return CC(t)}};function Lw(e){let t=new ks;return t.extractWeights(e),t}function DC(e){return Lw(e)}var zw=class extends ks{};var RC=.4,MC=[new De(.738768,.874946),new De(2.42204,2.65704),new De(4.30971,7.04493),new De(10.246,4.59428),new De(12.6868,11.8741)],PC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],OC=[117.001,114.697,97.404],LC="tiny_yolov2_model",zC="tiny_yolov2_separable_conv_model";var Kf=e=>typeof e=="number";function Xf(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!Kf(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>Kf(t.x)&&Kf(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(Kf)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Ou(e){return D(()=>{let t=L(e,de(.10000000149011612));return Z(Ye(fe(e,t)),t)})}function Dr(e,t){return D(()=>{let n=Jn(e,[[0,0],[1,1],[1,1],[0,0]]);return n=At(n,t.conv.filters,[1,1],"valid"),n=fe(n,t.bn.sub),n=L(n,t.bn.truediv),n=Z(n,t.conv.bias),Ou(n)})}function Rr(e,t){return D(()=>{let n=Jn(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Oi(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Z(n,t.bias),Ou(n)})}function lse(e,t){let n=Ru(e,t);function a(i,o){let l=nt(e(i)),c=nt(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:c}}function r(i,o,l){let c=n(i,o,3,`${l}/conv`),u=a(o,`${l}/bn`);return{conv:c,bn:u}}let s=Mu(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function BC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=xn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=lse(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,v]=a,x=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),C=c(m,f,"conv3"),F=c(f,g,"conv4"),$=c(g,y,"conv5"),P=b?c(y,b,"conv6"):void 0,B=v?c(b,v,"conv7"):void 0,W=o(v||b||y,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:F,conv5:$,conv6:P,conv7:B,conv8:W}}else{let[p,d,h,m,f,g,y,b,v]=a,x=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),F=l(f,g,"conv4"),$=l(g,y,"conv5"),P=l(y,b,"conv6"),B=l(b,v,"conv7"),W=o(v,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:F,conv5:$,conv6:P,conv7:B,conv8:W}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function use(e,t){let n=Gn(e,t);function a(o){let l=n(`${o}/sub`,1),c=n(`${o}/truediv`,1);return{sub:l,truediv:c}}function r(o){let l=n(`${o}/filters`,4),c=n(`${o}/bias`,1);return{filters:l,bias:c}}function s(o){let l=r(`${o}/conv`),c=a(`${o}/bn`);return{conv:l,bn:c}}let i=Pu(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function WC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=use(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return bn(e,n),{params:i,paramMappings:n}}var Wa=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Bw=class extends Qt{constructor(t){super("TinyYolov2");Xf(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Dr(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Dr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=Dr(a,n.conv6),a=Dr(a,n.conv7),co(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Ou(co(t,n.conv0,"valid",!1)):Rr(t,n.conv0);return a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv1),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv2),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv3),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv4),a=Ft(a,[2,2],[2,2],"same"),a=Rr(a,n.conv5),a=Ft(a,[2,2],[1,1],"same"),a=n.conv6?Rr(a,n.conv6):a,a=n.conv7?Rr(a,n.conv7):a,co(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return D(()=>{let r=ce(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?wa(r,this.config.meanRgb):r,r=r.div(de(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await gt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Wa(n),s=await gt(t),i=await this.forwardInput(s,a),o=D(()=>dt(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},c=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let u=c.map(g=>g.box),p=c.map(g=>g.score),d=c.map(g=>g.classScore),h=c.map(g=>this.config.classes[g.label]);return Sf(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new Fr(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return WC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Bw.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return BC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?Sa(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):de(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;y<c;y++)for(let b=0;b<c;b++)for(let v=0;v<u;v++){let x=Eu(f[y][b][v][0]);if(!a||x>a){let N=(b+Eu(g[y][b][v][0]))/c*o,T=(y+Eu(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,F=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,$=N-C/2,P=T-F/2,B={row:y,col:b,anchor:v},{classScore:W,label:G}=this.withClassScores?await this.extractPredictedClass(h,B):{classScore:1,label:0};m.push({box:new ro($,P,$+C,P+F),score:x,classScore:x*W,label:G,...B})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Lu=Bw;Lu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var go=class extends Lu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:RC,classes:["face"],...t?{anchors:PC,meanRgb:OC}:{anchors:MC,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?zC:LC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function VC(e,t=!0){let n=new go(t);return n.extractWeights(e),n}var Jp=class extends Wa{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var aa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function yo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>ws(l)?r(l):l.detection),i=a||(t instanceof Ae?await uo(t,s):await lo(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ae&&l.dispose()),o}async function zu(e,t,n,a,r){return yo([e],t,async s=>n(s[0]),a,r)}var UC=.4,GC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],HC=[117.001,114.697,97.404];var bo=class extends Lu{constructor(){let t={withSeparableConvs:!0,iouThreshold:UC,classes:["face"],anchors:GC,meanRgb:HC,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var et={ssdMobilenetv1:new ks,tinyFaceDetector:new bo,tinyYolov2:new go,faceLandmark68Net:new ho,faceLandmark68TinyNet:new qp,faceRecognitionNet:new mo,faceExpressionNet:new Up,ageGenderNet:new Hp},Ww=(e,t)=>et.ssdMobilenetv1.locateFaces(e,t),jC=(e,t)=>et.tinyFaceDetector.locateFaces(e,t),qC=(e,t)=>et.tinyYolov2.locateFaces(e,t),Vw=e=>et.faceLandmark68Net.detectLandmarks(e),KC=e=>et.faceLandmark68TinyNet.detectLandmarks(e),XC=e=>et.faceRecognitionNet.computeFaceDescriptor(e),YC=e=>et.faceExpressionNet.predictExpressions(e),ZC=e=>et.ageGenderNet.predictAgeAndGender(e),Uw=e=>et.ssdMobilenetv1.load(e),JC=e=>et.tinyFaceDetector.load(e),QC=e=>et.tinyYolov2.load(e),e_=e=>et.faceLandmark68Net.load(e),t_=e=>et.faceLandmark68TinyNet.load(e),n_=e=>et.faceRecognitionNet.load(e),a_=e=>et.faceExpressionNet.load(e),r_=e=>et.ageGenderNet.load(e),s_=Uw,i_=Ww,o_=Vw;var Gw=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Vu=class extends Gw{async run(){let t=await this.parentTask,n=await yo(t,this.input,async a=>Promise.all(a.map(r=>et.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Gp(a,n[r]))}withAgeAndGender(){return new Bu(this,this.input)}},Uu=class extends Gw{async run(){let t=await this.parentTask;if(!t)return;let n=await zu(t,this.input,a=>et.faceExpressionNet.predictExpressions(a),this.extractedFaces);return Gp(t,n)}withAgeAndGender(){return new Wu(this,this.input)}},wo=class extends Vu{withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},ko=class extends Uu{withAgeAndGender(){return new vo(this,this.input)}withFaceDescriptor(){return new Pr(this,this.input)}};var Hw=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Bu=class extends Hw{async run(){let t=await this.parentTask,n=await yo(t,this.input,async a=>Promise.all(a.map(r=>et.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Yp(Zp(a,i,o),s)})}withFaceExpressions(){return new Vu(this,this.input)}},Wu=class extends Hw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await zu(t,this.input,s=>et.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Yp(Zp(t,a,r),n)}withFaceExpressions(){return new Uu(this,this.input)}},xo=class extends Bu{withFaceExpressions(){return new wo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},vo=class extends Wu{withFaceExpressions(){return new ko(this,this.input)}withFaceDescriptor(){return new Pr(this,this.input)}};var Qp=class extends aa{constructor(t,n){super();this.parentTask=t;this.input=n}},Mr=class extends Qp{async run(){let t=await this.parentTask;return(await yo(t,this.input,a=>Promise.all(a.map(r=>et.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Xp(t[r],a))}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}},Pr=class extends Qp{async run(){let t=await this.parentTask;if(!t)return;let n=await zu(t,this.input,a=>et.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return Xp(t,n)}withFaceExpressions(){return new ko(this,this.input)}withAgeAndGender(){return new vo(this,this.input)}};var ed=class extends aa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?et.faceLandmark68TinyNet:et.faceLandmark68Net}},td=class extends ed{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ae?await uo(this.input,n):await lo(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ae&&s.dispose()),t.map((s,i)=>po(s,r[i]))}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptors(){return new Mr(this,this.input)}},nd=class extends ed{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ae?await uo(this.input,[n]):await lo(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ae&&s.dispose()),po(t,r)}withFaceExpressions(){return new ko(this,this.input)}withAgeAndGender(){return new vo(this,this.input)}withFaceDescriptor(){return new Pr(this,this.input)}};var ad=class extends aa{constructor(t,n=new na){super();this.input=t;this.options=n}},Gu=class extends ad{async run(){let{input:t,options:n}=this,a=n instanceof Jp?r=>et.tinyFaceDetector.locateFaces(r,n):n instanceof na?r=>et.ssdMobilenetv1.locateFaces(r,n):n instanceof Wa?r=>et.tinyYolov2.locateFaces(r,n):null;if(!a)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return a(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>gs({},a)))})}withFaceLandmarks(t=!1){return new td(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Vu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Bu(this.runAndExtendWithFaceDetections(),this.input)}},rd=class extends ad{async run(){let t=await new Gu(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?gs({},n):void 0)})}withFaceLandmarks(t=!1){return new nd(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Uu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Wu(this.runAndExtendWithFaceDetection(),this.input)}};function l_(e,t=new na){return new rd(e,t)}function sd(e,t=new na){return new Gu(e,t)}async function jw(e,t){return sd(e,new na(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function u_(e,t={}){return sd(e,new Wa(t)).withFaceLandmarks().withFaceDescriptors()}var c_=jw;function Yf(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var id=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof ur)return i;if(i instanceof Float32Array)return new ur(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new ur(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>Yf(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Au(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distance<a.distance?n:a)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this.distanceThreshold?n:new Au("unknown",n.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>ur.fromJSON(a));return new id(n,t.distanceThreshold)}};function p_(e){let t=new bo;return t.extractWeights(e),t}function qw(e,t){let{width:n,height:a}=new ln(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>qw(r,{width:n,height:a}));if(ws(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return po(gs(e,r),s)}return za(e)?gs(e,e.detection.forSize(n,a)):e instanceof Un||e instanceof yt?e.forSize(n,a):e}var pse=typeof process!="undefined",dse=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",d_={faceapi:dC,node:pse,browser:dse};return cse;})();
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/face-api.js.map b/dist/face-api.js.map
index 9ce8bd6..7e00328 100644
--- a/dist/face-api.js.map
+++ b/dist/face-api.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
-  "sources": ["../src/env/isNodejs.ts", "../src/index.ts", "../node_modules/node-fetch/browser.js", "../node_modules/seedrandom/lib/alea.js", "../node_modules/seedrandom/lib/xor128.js", "../node_modules/seedrandom/lib/xorwow.js", "../node_modules/seedrandom/lib/xorshift7.js", "../node_modules/seedrandom/lib/xor4096.js", "../node_modules/seedrandom/lib/tychei.js", "(disabled):crypto", "../node_modules/seedrandom/seedrandom.js", "../node_modules/seedrandom/index.js", "../node_modules/string_decoder/lib/string_decoder.js", "(disabled):path", "(disabled):worker_threads", "(disabled):perf_hooks", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js", "../node_modules/@tensorflow/tfjs-core/src/backends/backend.ts", "../node_modules/@tensorflow/tfjs-core/src/util_base.ts", "../node_modules/@tensorflow/tfjs-core/src/environment.ts", "../node_modules/@tensorflow/tfjs-core/src/global_util.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_names.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/util.ts", "../node_modules/@tensorflow/tfjs-core/src/profiler.ts", "../node_modules/@tensorflow/tfjs-core/src/tape.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_format.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util.ts", "../node_modules/@tensorflow/tfjs-core/src/types.ts", "../node_modules/@tensorflow/tfjs-core/src/engine.ts", "../node_modules/@tensorflow/tfjs-core/src/device_util.ts", "../node_modules/@tensorflow/tfjs-core/src/flags.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util_env.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/operation.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/complex.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor_ops_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/io/types.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io_utils.ts", "../node_modules/@tensorflow/tfjs-core/src/io/router_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/io/indexed_db.ts", "../node_modules/@tensorflow/tfjs-core/src/io/local_storage.ts", "../node_modules/@tensorflow/tfjs-core/src/io/model_management.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_browser.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_node.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/buffer.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cast.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/clone.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/print.ts", "../node_modules/@tensorflow/tfjs-core/src/base_side_effects.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io.ts", "../node_modules/@tensorflow/tfjs-core/src/io/browser_files.ts", "../node_modules/@tensorflow/tfjs-core/src/io/progress.ts", "../node_modules/@tensorflow/tfjs-core/src/io/weights_loader.ts", "../node_modules/@tensorflow/tfjs-core/src/io/http.ts", "../node_modules/@tensorflow/tfjs-core/src/io/passthrough.ts", "../node_modules/@tensorflow/tfjs-core/src/math.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mat_mul.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/one_hot.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/confusion_matrix.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/browser.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/gather_nd_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/scatter_nd_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/slice_util.ts", "../node_modules/@tensorflow/tfjs-core/src/serialization.ts", "../node_modules/@tensorflow/tfjs-core/src/test_util.ts", "../node_modules/@tensorflow/tfjs-core/src/version.ts", "../node_modules/@tensorflow/tfjs-core/src/globals.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/add.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/floorDiv.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/div.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mul.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/abs.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/acos.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/acosh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/add_n.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/all.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/any.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/arg_max.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/arg_min.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/asin.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/asinh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atan.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atan2.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atanh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/reshape.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/avg_pool.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/avg_pool_3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/sigmoid.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/slice.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tanh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/basic_lstm_cell.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batch_to_space_nd.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm4d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/bincount.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/broadcast_to.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/ceil.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/clip_by_value.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_1d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_4d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv1d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv2d_backprop_input.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv2d_transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv3d_backprop_input.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv3d_transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cos.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cosh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cumsum.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/dense_bincount.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/depth_to_space.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/depthwise_conv2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/diag.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/dilation2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/broadcast_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/equal.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/where.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/zeros_like.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/div_no_nan.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/dot.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/elu.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/erf.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/exp.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/expand_dims.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/expm1.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tile.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/eye.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/fill.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/floor.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/gather.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/greater.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/greater_equal.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/imag.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/is_finite.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/is_inf.ts", 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"../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeightMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeightMap.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeightMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts"],
-  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\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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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)}var WASM_PAGE_SIZE=65536;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 STACK_BASE=5256480,STACKTOP=STACK_BASE,STACK_MAX=13600,DYNAMIC_BASE=5256480,DYNAMICTOP_PTR=12672;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_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_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"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_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}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){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;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 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 Math_ceil=Math.ceil;var Math_floor=Math.floor;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+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}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(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}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)&&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()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;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\"){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\");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();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13584;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(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>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 __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";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 __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";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={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12832;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13072;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___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+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(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+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>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){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},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\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(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.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}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){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},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){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;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);__ATEXIT__.unshift({func:func,arg: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_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){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{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}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];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}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){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;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(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,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)}},inEventHandler:0,currentEventHandler:null,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_async_queue_on_thread_(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_async_queue_on_thread_(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){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}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}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},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},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],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\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){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 contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};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.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() 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,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_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_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;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;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>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+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}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 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79: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: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();else PThread.initWorker();var GLctx;GL.init();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,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=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 _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"qa\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"ra\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"sa\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"ta\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"ua\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"va\"]).apply(null,arguments)};var _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 _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _Round=Module[\"_Round\"]=function(){return(_Round=Module[\"_Round\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Ma\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Na\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Oa\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Pa\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"Qa\"]).apply(null,arguments)};var _Step=Module[\"_Step\"]=function(){return(_Step=Module[\"_Step\"]=Module[\"asm\"][\"Ra\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"Sa\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Ta\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Ua\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Va\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Wa\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Xa\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Ya\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Za\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"_a\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"$a\"]).apply(null,arguments)};var ___em_js__initPthreadsJS=Module[\"___em_js__initPthreadsJS\"]=function(){return(___em_js__initPthreadsJS=Module[\"___em_js__initPthreadsJS\"]=Module[\"asm\"][\"ab\"]).apply(null,arguments)};var ___errno_location=Module[\"___errno_location\"]=function(){return(___errno_location=Module[\"___errno_location\"]=Module[\"asm\"][\"bb\"]).apply(null,arguments)};var _emscripten_get_global_libc=Module[\"_emscripten_get_global_libc\"]=function(){return(_emscripten_get_global_libc=Module[\"_emscripten_get_global_libc\"]=Module[\"asm\"][\"cb\"]).apply(null,arguments)};var _memalign=Module[\"_memalign\"]=function(){return(_memalign=Module[\"_memalign\"]=Module[\"asm\"][\"db\"]).apply(null,arguments)};var ___pthread_tsd_run_dtors=Module[\"___pthread_tsd_run_dtors\"]=function(){return(___pthread_tsd_run_dtors=Module[\"___pthread_tsd_run_dtors\"]=Module[\"asm\"][\"eb\"]).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\"][\"fb\"]).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\"][\"gb\"]).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\"][\"hb\"]).apply(null,arguments)};var _emscripten_main_browser_thread_id=Module[\"_emscripten_main_browser_thread_id\"]=function(){return(_emscripten_main_browser_thread_id=Module[\"_emscripten_main_browser_thread_id\"]=Module[\"asm\"][\"ib\"]).apply(null,arguments)};var _emscripten_async_run_in_main_thread=Module[\"_emscripten_async_run_in_main_thread\"]=function(){return(_emscripten_async_run_in_main_thread=Module[\"_emscripten_async_run_in_main_thread\"]=Module[\"asm\"][\"jb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread=Module[\"_emscripten_sync_run_in_main_thread\"]=function(){return(_emscripten_sync_run_in_main_thread=Module[\"_emscripten_sync_run_in_main_thread\"]=Module[\"asm\"][\"kb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_0=Module[\"_emscripten_sync_run_in_main_thread_0\"]=function(){return(_emscripten_sync_run_in_main_thread_0=Module[\"_emscripten_sync_run_in_main_thread_0\"]=Module[\"asm\"][\"lb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_1=Module[\"_emscripten_sync_run_in_main_thread_1\"]=function(){return(_emscripten_sync_run_in_main_thread_1=Module[\"_emscripten_sync_run_in_main_thread_1\"]=Module[\"asm\"][\"mb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_2=Module[\"_emscripten_sync_run_in_main_thread_2\"]=function(){return(_emscripten_sync_run_in_main_thread_2=Module[\"_emscripten_sync_run_in_main_thread_2\"]=Module[\"asm\"][\"nb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_xprintf_varargs=Module[\"_emscripten_sync_run_in_main_thread_xprintf_varargs\"]=function(){return(_emscripten_sync_run_in_main_thread_xprintf_varargs=Module[\"_emscripten_sync_run_in_main_thread_xprintf_varargs\"]=Module[\"asm\"][\"ob\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_3=Module[\"_emscripten_sync_run_in_main_thread_3\"]=function(){return(_emscripten_sync_run_in_main_thread_3=Module[\"_emscripten_sync_run_in_main_thread_3\"]=Module[\"asm\"][\"pb\"]).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\"][\"qb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_5=Module[\"_emscripten_sync_run_in_main_thread_5\"]=function(){return(_emscripten_sync_run_in_main_thread_5=Module[\"_emscripten_sync_run_in_main_thread_5\"]=Module[\"asm\"][\"rb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_6=Module[\"_emscripten_sync_run_in_main_thread_6\"]=function(){return(_emscripten_sync_run_in_main_thread_6=Module[\"_emscripten_sync_run_in_main_thread_6\"]=Module[\"asm\"][\"sb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_7=Module[\"_emscripten_sync_run_in_main_thread_7\"]=function(){return(_emscripten_sync_run_in_main_thread_7=Module[\"_emscripten_sync_run_in_main_thread_7\"]=Module[\"asm\"][\"tb\"]).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\"][\"ub\"]).apply(null,arguments)};var _emscripten_async_queue_on_thread_=Module[\"_emscripten_async_queue_on_thread_\"]=function(){return(_emscripten_async_queue_on_thread_=Module[\"_emscripten_async_queue_on_thread_\"]=Module[\"asm\"][\"vb\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"wb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"xb\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"yb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"zb\"]).apply(null,arguments)};var dynCall_vi=Module[\"dynCall_vi\"]=function(){return(dynCall_vi=Module[\"dynCall_vi\"]=Module[\"asm\"][\"Ab\"]).apply(null,arguments)};var dynCall_v=Module[\"dynCall_v\"]=function(){return(dynCall_v=Module[\"dynCall_v\"]=Module[\"asm\"][\"Bb\"]).apply(null,arguments)};var dynCall_ii=Module[\"dynCall_ii\"]=function(){return(dynCall_ii=Module[\"dynCall_ii\"]=Module[\"asm\"][\"Cb\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"_pthread_self\"]=_pthread_self;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};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();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()()}}if(!ENVIRONMENT_IS_PTHREAD)noExitRuntime=true;if(!ENVIRONMENT_IS_PTHREAD)run();\n\n\n  return WasmBackendModuleThreadedSimd\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModuleThreadedSimd;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModuleThreadedSimd; });\n    else 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 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 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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(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 shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function 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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)}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_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;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){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;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 exitRuntime(){runtimeExited=true}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 Math_ceil=Math.ceil;var Math_floor=Math.floor;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+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}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(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}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)&&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()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);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\"){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\");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();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var 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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, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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 threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;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:selfThreadId})}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};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;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)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}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;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;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[\"dynCall_ii\"](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\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){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.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\");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, "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false,\n) {\n  ctx.beginPath();\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx];\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(x, y);\n  });\n\n  if (isClosed) {\n    const from = points[points.length - 1];\n    const to = points[0];\n    if (!from || !to) {\n      return;\n    }\n\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(to.x, to.y);\n  }\n\n  ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = 10 ** prec;\n  return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width);\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n  private _width: number\n\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n    }\n\n    this._width = width;\n    this._height = height;\n  }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height);\n  }\n}\n", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x;\n    this._y = y;\n  }\n\n  get x(): number { return this._x; }\n\n  get y(): number { return this._y; }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y);\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y);\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y);\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y);\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y));\n  }\n\n  public magnitude(): number {\n    return Math.sqrt((this.x ** 2) + (this.y ** 2));\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y));\n  }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n    }\n  }\n\n  private _x: number\n\n  private _y: number\n\n  private _width: number\n\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any;\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n    Box.assertIsValidBox({\n      x, y, width, height,\n    }, 'Box.constructor', allowNegativeDimensions);\n\n    this._x = x;\n    this._y = y;\n    this._width = width;\n    this._height = height;\n  }\n\n  public get x(): number { return this._x; }\n\n  public get y(): number { return this._y; }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public get left(): number { return this.x; }\n\n  public get top(): number { return this.y; }\n\n  public get right(): number { return this.x + this.width; }\n\n  public get bottom(): number { return this.y + this.height; }\n\n  public get area(): number { return this.width * this.height; }\n\n  public get topLeft(): Point { return new Point(this.left, this.top); }\n\n  public get topRight(): Point { return new Point(this.right, this.top); }\n\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n  public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.round(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.floor(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public toSquare(): Box<BoxType> {\n    let {\n      x, y, width, height,\n    } = this;\n    const diff = Math.abs(width - height);\n    if (width < height) {\n      x -= (diff / 2);\n      width += diff;\n    }\n    if (height < width) {\n      y -= (diff / 2);\n      height += diff;\n    }\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY,\n    });\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    const [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY,\n    ];\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const {\n      x, y, right, bottom,\n    } = this;\n    const clippedX = Math.max(x, 0);\n    const clippedY = Math.max(y, 0);\n\n    const newWidth = right - clippedX;\n    const newHeight = bottom - clippedY;\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n    return (new Box({\n      x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n    })).floor();\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this;\n    const x = this.x + sx;\n    const y = this.y + sy;\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1;\n    const h = this.height + 1;\n\n    const dx = 1;\n    const dy = 1;\n    let edx = w;\n    let edy = h;\n\n    let x = this.left;\n    let y = this.top;\n    let ex = this.right;\n    let ey = this.bottom;\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w;\n      ex = imageWidth;\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h;\n      ey = imageHeight;\n    }\n    if (x < 1) {\n      edy = 2 - x;\n      x = 1;\n    }\n    if (y < 1) {\n      edy = 2 - y;\n      y = 1;\n    }\n\n    return {\n      dy, edy, dx, edx, y, ey, x, ex, w, h,\n    };\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height),\n    }).toSquare().round();\n  }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({\n      left, top, right, bottom,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n\n  private _classScore: number\n\n  private _className: string\n\n  private _box: Rect\n\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions,\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n    this._score = score;\n    this._classScore = classScore;\n    this._className = className;\n    this._box = new Box(relativeBox).rescale(this._imageDims);\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n\n  public get className(): string { return this._className; }\n\n  public get box(): Box { return this._box; }\n\n  public get imageDims(): Dimensions { return this._imageDims; }\n\n  public get imageWidth(): number { return this.imageDims.width; }\n\n  public get imageHeight(): number { return this.imageDims.height; }\n\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height },\n    );\n  }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions,\n  ) {\n    super(score, score, '', relativeBox, imageDims);\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height);\n    return new FaceDetection(score, relativeBox, imageDims);\n  }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n  const interSection = width * height;\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map((pt) => pt.x);\n  const ys = pts.map((pt) => pt.y);\n  const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n  const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n  const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n  const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n  return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true,\n): number[] {\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map((c) => c.boxIndex);\n\n  const pick: number[] = [];\n\n  while (indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number;\n    pick.push(curr);\n\n    const indices = indicesSortedByScore;\n\n    const outputs: number[] = [];\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i];\n\n      const currBox = boxes[curr];\n      const idxBox = boxes[idx];\n\n      outputs.push(iou(currBox, idxBox, isIOU));\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold,\n    );\n  }\n\n  return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb;\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n    return tf.sub(x, avg_rgb);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [height, width] = imgTensor.shape.slice(1);\n    if (height === width) {\n      return imgTensor;\n    }\n\n    const dimDiff = Math.abs(height - width);\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n    const paddingAxis = height > width ? 2 : 1;\n\n    const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice();\n      paddingTensorShape[paddingAxis] = paddingAmountLocal;\n      return tf.fill(paddingTensorShape, 0, 'float32');\n    };\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount);\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null;\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend,\n    ]\n      .filter((t) => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n    return tf.concat(tensorsToStack, paddingAxis);\n  });\n}\n", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice();\n  for (let i = array.length - 1; i > 0; i--) {\n    const j = Math.floor(Math.random() * (i + 1));\n    const x = array[i];\n    array[i] = array[j];\n    array[j] = x;\n  }\n  return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({\n      x, y, width, height,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n\n  protected _positions: Point[]\n\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0),\n  ) {\n    const { width, height } = imgDims;\n    this._imgDims = new Dimensions(width, height);\n    this._shift = shift;\n    this._positions = relativeFaceLandmarkPositions.map(\n      (pt) => pt.mul(new Point(width, height)).add(shift),\n    );\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n  public get imageWidth(): number { return this._imgDims.width; }\n\n  public get imageHeight(): number { return this._imgDims.height; }\n\n  public get positions(): Point[] { return this._positions; }\n\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n    );\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height },\n    );\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y),\n    );\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y);\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection);\n\n      return this.shiftBy(box.x, box.y).align(null, options);\n    }\n\n    const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n    if (useDlibAlignment) {\n      return this.alignDlib();\n    }\n\n    return this.alignMinBbox(minBoxPadding);\n  }\n\n  private alignDlib(): Box {\n    const centers = this.getRefPointsForAlignment();\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n    const size = Math.floor(eyeToMouthDist / relScale);\n\n    const refPoint = getCenterPoint(centers);\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions);\n    return box.pad(box.width * padding, box.height * padding);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class');\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions;\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]]),\n    ];\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17);\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22);\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27);\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36);\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42);\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48);\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth(),\n    ].map(getCenterPoint);\n  }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label;\n    this._distance = distance;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get distance(): number { return this._distance; }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n  }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee);\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box);\n    this._label = label;\n  }\n\n  public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n  private _label: string\n\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n    }\n\n    this._label = label;\n    this._descriptors = descriptors;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get descriptors(): Float32Array[] { return this._descriptors; }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d)),\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee);\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n    }\n  }\n\n  private _score: number\n\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label);\n    this._score = score;\n    this._classScore = classScore;\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<\n  TSource\n>(\n  sourceObj: TSource,\n  detection: FaceDetection,\n): WithFaceDetection<TSource> {\n  const extension = { detection };\n  return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n  const fetch = window.fetch;\n  if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n  const readFile = () => {\n    throw new Error('readFile - filesystem not available for browser environment');\n  };\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile,\n  };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n  let requireFsError = '';\n\n  if (!fs) {\n    try {\n      // eslint-disable-next-line global-require\n      fs = require('fs');\n    } catch (err) {\n      requireFsError = err.toString();\n    }\n  }\n\n  const readFile = fs\n    ? (filePath: string) => new Promise<Buffer>((resolve, reject) => {\n      fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n    })\n    : () => {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n    };\n\n  return {\n    readFile,\n  };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n  // eslint-disable-next-line dot-notation\n  const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n  const Image = global.Image || global.HTMLImageElement;\n\n  const createCanvasElement = () => {\n    if (Canvas) return new Canvas();\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n  };\n\n  const createImageElement = () => {\n    if (Image) return new Image();\n    throw new Error('createImageElement - missing Image implementation for nodejs environment');\n  };\n\n  const fetch = global.fetch;\n  // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n  const fileSystem = createFileSystem();\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n    Image: Image || class {},\n    ImageData: global.ImageData || class {},\n    Video: global.HTMLVideoElement || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem,\n  };\n}\n", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n  }\n  return environment;\n}\n\nfunction setEnv(env: Environment) {\n  environment = env;\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) return setEnv(createBrowserEnv());\n  if (isNodejs()) return setEnv(createNodejsEnv());\n  return null;\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize();\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env;\n  environment.Canvas = Canvas;\n  environment.Image = Image;\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n  environment.createImageElement = env.createImageElement || (() => new Image());\n\n  environment.ImageData = env.ImageData || environment.ImageData;\n  environment.Video = env.Video || environment.Video;\n  environment.fetch = env.fetch || environment.fetch;\n  environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg);\n  }\n  return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg;\n  }\n\n  const canvas = resolveInput(canvasArg);\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n  }\n\n  const ctx = canvas.getContext('2d');\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null');\n  }\n\n  return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n  // eslint-disable-next-line no-unused-vars\n  TOP_LEFT = 'TOP_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  TOP_RIGHT = 'TOP_RIGHT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n\n  public backgroundColor: string\n\n  public fontColor: string\n\n  public fontSize: number\n\n  public fontStyle: string\n\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const {\n      anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = options;\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n    this.fontSize = fontSize || 14;\n    this.fontStyle = fontStyle || 'Georgia';\n    this.padding = padding || 4;\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n\n  public anchor : IPoint\n\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {},\n  ) {\n    // eslint-disable-next-line no-nested-ternary\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text);\n    this.anchor = anchor;\n    this.options = new DrawTextFieldOptions(options);\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options;\n    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options;\n    return this.text.length * fontSize + (2 * padding);\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options;\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n    const textFieldWidth = this.measureWidth(ctx);\n    const textFieldHeight = this.measureHeight();\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims;\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n      return { x: newX, y: newY };\n    }\n    return { x, y };\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg);\n    const ctx = getContext2dOrThrow(canvas);\n\n    const {\n      backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = this.options;\n\n    ctx.font = `${fontSize}px ${fontStyle}`;\n    const maxTextWidth = this.measureWidth(ctx);\n    const textHeight = this.measureHeight();\n\n    ctx.fillStyle = backgroundColor;\n    const upperLeft = this.getUpperLeft(ctx, canvas);\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x;\n      const y = padding + upperLeft.y + ((i + 1) * fontSize);\n      ctx.fillText(textLine, x, y);\n    });\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport {\n  AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,\n} from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n\n  public lineWidth: number\n\n  public drawLabelOptions: DrawTextFieldOptions\n\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const {\n      boxColor, lineWidth, label, drawLabelOptions,\n    } = options;\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n    this.lineWidth = lineWidth || 2;\n    this.label = label;\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor,\n    };\n    this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {},\n  ) {\n    this.box = new Box(box);\n    this.options = new DrawBoxOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const { boxColor, lineWidth } = this.options;\n\n    const {\n      x, y, width, height,\n    } = this.box;\n    ctx.strokeStyle = boxColor;\n    ctx.lineWidth = lineWidth;\n    ctx.strokeRect(x, y, width, height);\n\n    const { label } = this.options;\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n    }\n  }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>,\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n  detectionsArray.forEach((det) => {\n    // eslint-disable-next-line no-nested-ternary\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n    // eslint-disable-next-line no-nested-ternary\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n    const label = score ? `${round(score)}` : undefined;\n    new DrawBox(box, { label }).draw(canvasArg);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n  const { Image, Video } = env.getEnv();\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n  // eslint-disable-next-line consistent-return\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null);\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return;\n      // eslint-disable-next-line no-use-before-define\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      reject(e);\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return;\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      resolve(e);\n    }\n\n    media.addEventListener('load', onLoad);\n    media.addEventListener('error', onError);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n    const reader = new FileReader();\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n      const img = env.getEnv().createImageElement();\n      img.onload = () => resolve(img);\n      img.onerror = reject;\n      img.src = reader.result as string;\n    };\n    reader.onerror = reject;\n    reader.readAsDataURL(buf);\n  });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n  const { Image, Video } = env.getEnv();\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight);\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight);\n  }\n  return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n  const { createCanvasElement } = env.getEnv();\n  const canvas = createCanvasElement();\n  canvas.width = width;\n  canvas.height = height;\n  return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n  const { ImageData } = env.getEnv();\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet');\n  }\n\n  const { width, height } = dims || getMediaDimensions(media);\n  const canvas = createCanvas({ width, height });\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n  }\n  return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement,\n): Promise<HTMLCanvasElement> {\n  const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n  await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n  imgTensor3D.dispose();\n\n  return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n  const { Image, Canvas, Video } = env.getEnv();\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n  const { Image, Canvas } = env.getEnv();\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n  }\n\n  const dims = getMediaDimensions(input);\n  const scale = inputSize / Math.max(dims.height, dims.width);\n  const width = scale * dims.width;\n  const height = scale * dims.height;\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n  const offset = Math.abs(width - height) / 2;\n  const dx = centerImage && width < height ? offset : 0;\n  const dy = centerImage && height < width ? offset : 0;\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n  return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport {\n  computeReshapedDimensions, isTensor3D, isTensor4D, range,\n} from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n\n  private _canvases: HTMLCanvasElement[] = []\n\n  private _batchSize: number\n\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false,\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput;\n    this._batchSize = inputs.length;\n\n    inputs.forEach((input, idx) => {\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = input.shape;\n        return;\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0];\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n        }\n\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = (input as any).shape.slice(1);\n        return;\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n      this._canvases[idx] = canvas;\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n    });\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors;\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases;\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput;\n  }\n\n  public get batchSize(): number {\n    return this._batchSize;\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions;\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize;\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n    );\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx];\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0];\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1];\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n    }\n\n    const width = this.getInputWidth(batchIdx);\n    const height = this.getInputHeight(batchIdx);\n    return computeReshapedDimensions({ width, height }, this.inputSize);\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n    this._inputSize = inputSize;\n\n    return tf.tidy(() => {\n      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n        const input = this.getInput(batchIdx);\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>();\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3);\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n      });\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n\n      return batchTensor;\n    });\n  }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs;\n  }\n\n  const inputArgArray = Array.isArray(inputs)\n    ? inputs\n    : [inputs];\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input');\n  }\n\n  const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n\n  const inputArray = inputArgArray.map(resolveInput);\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0];\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n      }\n    }\n  });\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)),\n  );\n\n  return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>,\n): Promise<HTMLCanvasElement[]> {\n  const { Canvas } = env.getEnv();\n\n  let canvas = input as HTMLCanvasElement;\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input);\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported');\n    }\n\n    const tensorOrCanvas = netInput.getInput(0);\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas);\n  }\n\n  const ctx = getContext2dOrThrow(canvas);\n  const boxes = detections.map(\n    (det) => (det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det),\n  )\n    .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n\n  return boxes.map(({\n    x, y, width, height,\n  }) => {\n    const faceImg = createCanvas({ width, height });\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n    return faceImg;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>,\n): Promise<tf.Tensor3D[]> {\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported');\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n    const boxes = detections.map(\n      (det) => (det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det),\n    )\n      .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n    const faceTensors = boxes.map(({\n      x, y, width, height,\n    }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n    return faceTensors;\n  });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n  url: string,\n  // eslint-disable-next-line no-undef\n  init?: RequestInit,\n): Promise<Response> {\n  const { fetch } = env.getEnv();\n  const res = await fetch(url, init);\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n  }\n  return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri);\n  const blob = await (res).blob();\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n  }\n  return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename,\n    };\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`,\n    };\n  }\n  // eslint-disable-next-line no-nested-ternary\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter((s) => s);\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename;\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri);\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference;\n  input.width = width;\n  input.height = height;\n  return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork<TNetParams> {\n  constructor(name: string) {\n    this._name = name;\n  }\n\n  protected _params: TNetParams | undefined = undefined\n\n  protected _paramMappings: ParamMapping[] = []\n\n  public _name: any;\n\n  public get params(): TNetParams | undefined { return this._params; }\n\n  public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n  public get isLoaded(): boolean { return !!this.params; }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    return obj[objProp];\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    obj[objProp].dispose();\n    obj[objProp] = tensor;\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath),\n    }));\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable());\n    });\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync());\n      variable.dispose();\n      this.reassignParamFromPath(path, tensor);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach((param) => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`);\n      }\n      param.tensor.dispose();\n    });\n    this._params = undefined;\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr)),\n    );\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl);\n      return;\n    }\n    await this.loadFromUri(weightsOrUrl);\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`);\n    }\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n    }\n    const { readFile } = env.getEnv();\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)));\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n    const manifest = JSON.parse((await readFile(manifestUri)).toString());\n    const weightMap = await loadWeights(manifest, modelBaseUri);\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const { paramMappings, params } = this.extractParams(weights);\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error('traversePropertyPath - model has no loaded params');\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      // eslint-disable-next-line no-prototype-builtins\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n      }\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n    }, { nextObj: this.params });\n\n    const { obj, objProp } = result;\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n    }\n\n    return { obj, objProp };\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number],\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n    out = tf.add(out, params.bias);\n    return out;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n  });\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias,\n    ) as tf.Tensor4D;\n\n    return withRelu ? tf.relu(out) : out;\n  });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach((path) => {\n    if (!paramMappings.some((pm) => pm.originalPath === path)) {\n      weightMap[path].dispose();\n    }\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams => {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): FCParams => {\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias,\n    };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  // eslint-disable-next-line no-useless-constructor\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    public depthwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public pointwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public bias: tf.Tensor1D,\n  // eslint-disable-next-line no-empty-function\n  ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n\nexport function loadSeparableConvParamsFactory(\n  // eslint-disable-next-line no-unused-vars\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T,\n) {\n  return (prefix: string): SeparableConvParams => {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4);\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  return (originalPath: string, paramRank: number, mappedPath?: string) => {\n    const tensor = weightMap[originalPath];\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath },\n    );\n\n    return tensor;\n  };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights;\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights);\n    remainingWeights = remainingWeights.slice(numWeights);\n    return ret;\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights;\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights,\n  };\n}\n", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock4Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: {\n      dense0, dense1, dense2, dense3,\n    },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return (prefix: string): ConvParams => {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return { filters, bias };\n  };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock4Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n  constructor() {\n    super('FaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock4(normalized, params.dense0, true);\n      out = denseBlock4(out, params.dense1);\n      out = denseBlock4(out, params.dense2);\n      out = denseBlock4(out, params.dense3);\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams,\n): tf.Tensor2D {\n  return tf.tidy(() => tf.add(\n    tf.matMul(x, params.weights),\n    params.bias,\n  ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: extractFcParams('fc'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n  const featureExtractorMap: tf.NamedTensorMap = {};\n  const classifierMap: tf.NamedTensorMap = {};\n\n  Object.keys(weightMap).forEach((key) => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n    map[key] = weightMap[key];\n  });\n\n  return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name);\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor;\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  protected abstract getClassifierChannelsIn(): number\n\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeightMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const cIn = this.getClassifierChannelsIn();\n    const cOut = this.getClassifierChannelsOut();\n    const classifierWeightSize = (cOut * cIn) + cOut;\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n  public neutral: number\n\n  public happy: number\n\n  public sad: number\n\n  public angry: number\n\n  public fearful: number\n\n  public disgusted: number\n\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx];\n    });\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map((expression) => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor);\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)));\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n      const data = await t.data();\n      t.dispose();\n      return data;\n    }));\n    out.dispose();\n\n    const predictionsByBatch = probabilitesByBatch\n      .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7;\n  }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n>(\n  sourceObj: TSource,\n  expressions: FaceExpressions,\n): WithFaceExpressions<TSource> {\n  const extension = { expressions };\n  return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint,\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n  faceExpressionsArray.forEach((e) => {\n    // eslint-disable-next-line no-nested-ternary\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined);\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n    }\n\n    const sorted = expr.asSortedArray();\n    const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0));\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n      anchor,\n    );\n    drawTextField.draw(canvasArg);\n  });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n    landmarks: TFaceLandmarks\n    unshiftedLandmarks: TFaceLandmarks\n    alignedRect: FaceDetection\n  }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    // eslint-disable-next-line dot-notation\n    && obj['landmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['alignedRect'] instanceof FaceDetection;\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks<TSource, TFaceLandmarks> {\n  const { box: shift } = sourceObj.detection;\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y);\n\n  const rect = landmarks.align();\n  const { imageDims } = sourceObj.detection;\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect,\n  };\n\n  return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n\n  public drawPoints: boolean\n\n  public lineWidth: number\n\n  public pointSize: number\n\n  public lineColor: string\n\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const {\n      drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n    } = options;\n    this.drawLines = drawLines;\n    this.drawPoints = drawPoints;\n    this.lineWidth = lineWidth || 1;\n    this.pointSize = pointSize || 2;\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {},\n  ) {\n    this.faceLandmarks = faceLandmarks;\n    this.options = new DrawFaceLandmarksOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const {\n      drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n    } = this.options;\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor;\n      ctx.lineWidth = lineWidth;\n      drawContour(ctx, this.faceLandmarks.getJawOutline());\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getNose());\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n      drawContour(ctx, this.faceLandmarks.getMouth(), true);\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor;\n      ctx.fillStyle = pointColor;\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath();\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n        ctx.fill();\n      };\n      this.faceLandmarks.positions.forEach(drawPoint);\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>,\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n  faceLandmarksArray.forEach((f) => {\n    // eslint-disable-next-line no-nested-ternary\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg);\n  });\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x;\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n  out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n  return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n  out = tf.add(out, x);\n  return out;\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception');\n    this._numMainBlocks = numMainBlocks;\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n    if (!params) {\n      throw new Error('TinyXception - load model before inference');\n    }\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n      out = reductionBlock(out, params.entry_flow.reduction_block_1);\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n      });\n      out = reductionBlock(out, params.exit_flow.reduction_block);\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks);\n  }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const age = extractFCParams(512, 1, 'fc/age');\n  const gender = extractFCParams(512, 2, 'fc/gender');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender'),\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n  // eslint-disable-next-line no-unused-vars\n  FEMALE = 'female',\n  // eslint-disable-next-line no-unused-vars\n  MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport {\n  AgeAndGenderPrediction, Gender, NetOutput, NetParams,\n} from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet');\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor;\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n      const gender = fullyConnectedLayer(pooled, params.fc.gender);\n      return { age, gender };\n    });\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input);\n      return { age, gender: tf.softmax(gender) };\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n\n    const ages = tf.unstack(out.age);\n    const genders = tf.unstack(out.gender);\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i],\n    }));\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0];\n        const probMale = (await genderTensor.data())[0];\n        const isMale = probMale > 0.5;\n        const gender = isMale ? Gender.MALE : Gender.FEMALE;\n        const genderProbability = isMale ? probMale : (1 - probMale);\n\n        ageTensor.dispose();\n        genderTensor.dispose();\n        return { age, gender, genderProbability };\n      }),\n    );\n    out.age.dispose();\n    out.gender.dispose();\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model';\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeightMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width);\n      return {\n        width: width * scale,\n        height: height * scale,\n      };\n    });\n\n    const batchSize = inputDimensions.length;\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n      // eslint-disable-next-line no-unused-vars\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx];\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n      };\n\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          getPaddingX(batchIdx),\n          getPaddingY(batchIdx),\n        ))))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          inputDimensions[batchIdx].width,\n          inputDimensions[batchIdx].height,\n        ))));\n\n      return landmarkTensors as tf.Tensor2D;\n    });\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input);\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width })),\n      );\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input);\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data());\n        const xCoords = landmarksArray.filter((_, i) => isEven(i));\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width: netInput.getInputWidth(batchIdx),\n          },\n        );\n      },\n    ));\n\n    landmarkTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136;\n  }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n  weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock3Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock3Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n  constructor() {\n    super('TinyFaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock3(normalized, params.dense0, true);\n      out = denseBlock3(out, params.dense1);\n      out = denseBlock3(out, params.dense2);\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMapTiny(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights);\n  }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128;\n  }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n  const { filters, bias } = params.conv;\n\n  let out = tf.conv2d(x, filters, strides, padding);\n  out = tf.add(out, bias);\n  out = scale(out, params.scale);\n  return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,\n} from '../common/index';\nimport { isFloat } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues);\n    const depth = weights.length / (numFilters * filterSize * filterSize);\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0],\n      ),\n    );\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams {\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n    const bias = tf.tensor1d(extractWeights(numFilters));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n    const weights = tf.tensor1d(extractWeights(numWeights));\n    const biases = tf.tensor1d(extractWeights(numWeights));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` },\n    );\n\n    return {\n      weights,\n      biases,\n    };\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvLayerParams {\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n    return { conv, scale };\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false,\n  ): ResidualLayerParams {\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n    return { conv1, conv2 };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n  );\n  paramMappings.push({ paramPath: 'fc' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n    return { weights, biases };\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n    const scale = extractScaleLayerParams(prefix);\n\n    return { conv: { filters, bias }, scale };\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`),\n    };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const conv32_down = extractConvLayerParams('conv32_down');\n  const conv32_1 = extractResidualLayerParams('conv32_1');\n  const conv32_2 = extractResidualLayerParams('conv32_2');\n  const conv32_3 = extractResidualLayerParams('conv32_3');\n\n  const conv64_down = extractResidualLayerParams('conv64_down');\n  const conv64_1 = extractResidualLayerParams('conv64_1');\n  const conv64_2 = extractResidualLayerParams('conv64_2');\n  const conv64_3 = extractResidualLayerParams('conv64_3');\n\n  const conv128_down = extractResidualLayerParams('conv128_down');\n  const conv128_1 = extractResidualLayerParams('conv128_1');\n  const conv128_2 = extractResidualLayerParams('conv128_2');\n\n  const conv256_down = extractResidualLayerParams('conv256_down');\n  const conv256_1 = extractResidualLayerParams('conv256_1');\n  const conv256_2 = extractResidualLayerParams('conv256_2');\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n  const { fc } = weightMap;\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n  out = tf.add(out, x);\n  out = tf.relu(out);\n  return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape);\n  const isPad = pooled.shape[3] !== out.shape[3];\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number];\n    padShapeX[1] = 1;\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX);\n    out = tf.concat([out, zerosW], 1);\n\n    const padShapeY = [...out.shape] as [number, number, number, number];\n    padShapeY[2] = 1;\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY);\n    out = tf.concat([out, zerosH], 2);\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n  out = tf.add(pooled, out) as tf.Tensor4D;\n\n  out = tf.relu(out);\n  return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('FaceRecognitionNet');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = convDown(normalized, params.conv32_down);\n      out = tf.maxPool(out, 3, 2, 'valid');\n\n      out = residual(out, params.conv32_1);\n      out = residual(out, params.conv32_2);\n      out = residual(out, params.conv32_3);\n\n      out = residualDown(out, params.conv64_down);\n      out = residual(out, params.conv64_1);\n      out = residual(out, params.conv64_2);\n      out = residual(out, params.conv64_3);\n\n      out = residualDown(out, params.conv128_down);\n      out = residual(out, params.conv128_1);\n      out = residual(out, params.conv128_2);\n\n      out = residualDown(out, params.conv256_down);\n      out = residual(out, params.conv256_1);\n      out = residual(out, params.conv256_2);\n      out = residualDown(out, params.conv256_down_out);\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n      const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n      return fullyConnected;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n    const netInput = await toNetInput(input);\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n    faceDescriptorTensors.forEach((t) => t.dispose());\n    return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet();\n  net.extractWeights(weights);\n  return net;\n}\n", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n>(\n  sourceObj: TSource,\n  descriptor: Float32Array,\n): WithFaceDescriptor<TSource> {\n  const extension = { descriptor };\n  return { ...sourceObj, ...extension };\n}\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n  TSource\n>(\n  sourceObj: TSource,\n  age: number,\n): WithAge<TSource> {\n  const extension = { age };\n  return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n    && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n  TSource\n>(\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number,\n): WithGender<TSource> {\n  const extension = { gender, genderProbability };\n  return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,\n} from '../common/index';\nimport {\n  MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` },\n    );\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance,\n    };\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean,\n  ): ConvParams {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): PointwiseConvParams {\n    const {\n      filters,\n      bias,\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n    return {\n      filters,\n      batch_norm_offset: bias,\n    };\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): MobileNetV1.ConvPairParams {\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n    return { depthwise_conv, pointwise_conv };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13,\n    };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0,\n    };\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1,\n    };\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2,\n    };\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3,\n    };\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4,\n    };\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5,\n    };\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5,\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n  const mobilenetv1 = extractMobilenetV1Params();\n  const prediction_layer = extractPredictionLayerParams();\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4],\n  );\n  const output_layer = {\n    extra_dim,\n  };\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer,\n    },\n    paramMappings,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,\n} from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport {\n  BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n    return { filters, batch_norm_offset };\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n    const mappedPrefix = `mobilenetv1/conv_${idx}`;\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance,\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n    };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13),\n    };\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n    return { filters, bias };\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n    );\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`,\n    );\n    return { box_encoding_predictor, class_predictor };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5),\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n  const extra_dim = weightMap['Output/extra_dim'];\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim,\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.conv2d(x, params.filters, strides, 'same');\n    out = tf.add(out, params.batch_norm_offset);\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon,\n    );\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13,\n    ];\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1;\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n      if (layerIdx === 11) {\n        conv11 = out;\n      }\n    });\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null');\n    }\n\n    return {\n      out,\n      conv11: conv11 as any,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync();\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)\n      * Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n): number[] {\n  const numBoxes = boxes.shape[0];\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes,\n  );\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter((c) => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score);\n\n  const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n  const selected: number[] = [];\n\n  candidates.forEach((c) => {\n    if (selected.length >= outputSize) return;\n    const originalScore = c.score;\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j]);\n      if (iou === 0.0) continue;\n      c.score *= suppressFunc(iou);\n      if (c.score <= scoreThreshold) break;\n    }\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex);\n    }\n  });\n  return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1]),\n  ];\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),\n  ];\n  return {\n    sizes,\n    centers,\n  };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers,\n  } = getCenterCoordinatesAndSizesLayer(x0);\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out),\n    ]),\n    [1, 0],\n  );\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = boxPredictions.shape[0];\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n    );\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4],\n    );\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number],\n    );\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = x.shape[0];\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4],\n    );\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3],\n    );\n    return { boxPredictionEncoding, classPrediction };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams,\n) {\n  return tf.tidy(() => {\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding,\n    ], 1) as tf.Tensor4D;\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction,\n    ], 1) as tf.Tensor4D;\n\n    return {\n      boxPredictions,\n      classPredictions,\n    };\n  });\n}\n", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5;\n    this._maxResults = maxResults || 100;\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`);\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence; }\n\n  get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('SsdMobilenetv1');\n  }\n\n  public forwardInput(input: NetInput) {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;\n      const features = mobileNetV1(x, params.mobilenetv1);\n\n      const {\n        boxPredictions,\n        classPredictions,\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer);\n    });\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {},\n  ): Promise<FaceDetection[]> {\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n\n    const netInput = await toNetInput(input);\n\n    const {\n      boxes: _boxes,\n      scores: _scores,\n    } = this.forwardInput(netInput);\n\n    const boxes = _boxes[0];\n    const scores = _scores[0];\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose();\n      _scores[i].dispose();\n    }\n\n    const scoresData = Array.from(await scores.data());\n    const iouThreshold = 0.5;\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence,\n    );\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0);\n    const inputSize = netInput.inputSize as number;\n    const padX = inputSize / reshapedDims.width;\n    const padY = inputSize / reshapedDims.height;\n\n    const boxesData = boxes.arraySync();\n    const results = indices\n      .map((idx) => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2]),\n        ].map((val) => val * padY);\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3]),\n        ].map((val) => val * padX);\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top,\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0),\n          },\n        );\n      });\n\n    boxes.dispose();\n    scores.dispose();\n    return results;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1();\n  net.extractWeights(weights);\n  return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`);\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612));\n    return tf.add(tf.relu(tf.sub(x, min)), min);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n    out = tf.sub(out, params.bn.sub);\n    out = tf.mul(out, params.bn.truediv);\n    out = tf.add(out, params.conv.bias);\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n    out = tf.add(out, params.bias);\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n    const sub = tf.tensor1d(extractWeights(size));\n    const truediv = tf.tensor1d(extractWeights(size));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` },\n    );\n    return { sub, truediv };\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n    return { conv, bn };\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0');\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  }\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry(`${prefix}/sub`, 1);\n    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n    return { sub, truediv };\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { filters, bias };\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`);\n    const bn = extractBatchNormParams(`${prefix}/bn`);\n    return { conv, bn };\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    // eslint-disable-next-line no-mixed-operators\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8'),\n    };\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8'),\n    };\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n  return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416;\n    this._scoreThreshold = scoreThreshold || 0.5;\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n    }\n  }\n\n  get inputSize(): number { return this._inputSize; }\n\n  get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n  public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2');\n    validateConfig(config);\n    this._config = config;\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config;\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1;\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0);\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n    let out = convWithBatchNorm(x, params.conv0);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = convWithBatchNorm(out, params.conv6);\n    out = convWithBatchNorm(out, params.conv7);\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor;\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D;\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n    });\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input), inputSize);\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput, inputSize);\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0),\n    };\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n    out.dispose();\n    out0.dispose();\n\n    const boxes = results.map((res) => res.box);\n    const scores = results.map((res) => res.score);\n    const classScores = results.map((res) => res.classScore);\n    const classNames = results.map((res) => this.config.classes[res.label]);\n\n    const indices = nonMaxSuppression(\n      boxes.map((box) => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true,\n    );\n\n    const detections = indices.map((idx) => new ObjectDetection(\n      scores[idx],\n      classScores[idx],\n      classNames[idx],\n      boxes[idx],\n      inputDimensions,\n    ));\n    return detections;\n  }\n\n  protected getDefaultModelName(): string {\n    return '';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap, this.config);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n    const numFilters = filterSizes ? filterSizes.length : undefined;\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number,\n  ) {\n    const { width, height } = inputBlobDimensions;\n    const inputSize = Math.max(width, height);\n    const correctionFactorX = inputSize / width;\n    const correctionFactorY = inputSize / height;\n\n    const numCells = outputTensor.shape[1];\n    const numBoxes = this.config.anchors.length;\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0);\n      return [boxes, scores, classScores];\n    });\n\n    const results = [] as any;\n    const scoresData = await scoresTensor.array();\n    const boxesData = await boxesTensor.array();\n    for (let row = 0; row < numCells; row++) {\n      for (let col = 0; col < numCells; col++) {\n        for (let anchor = 0; anchor < numBoxes; anchor++) {\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n            const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n            const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n            const x = (ctX - (widthLocal / 2));\n            const y = (ctY - (heightLocal / 2));\n            const pos = { row, col, anchor };\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 };\n            results.push({\n              box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n              score,\n              classScore: score * classScore,\n              label,\n              ...pos,\n            });\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose();\n    scoresTensor.dispose();\n    classScoresTensor.dispose();\n    return results;\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n    const { row, col, anchor } = pos;\n    const classesData = await classesTensor.array();\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label,\n      }))\n      .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n  constructor(withSeparableConvs: boolean = true) {\n    const config = {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      ...(withSeparableConvs\n        ? {\n          anchors: BOX_ANCHORS_SEPARABLE,\n          meanRgb: MEAN_RGB_SEPARABLE,\n        }\n        : {\n          anchors: BOX_ANCHORS,\n          withClassScores: true,\n        }),\n    };\n\n    super(config);\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs;\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeightMap(weightMap);\n  }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs);\n  net.extractWeights(weights);\n  return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask<T> {\n  public async then(\n    // eslint-disable-next-line no-unused-vars\n    onfulfilled: (value: T) => T | PromiseLike<T>,\n  ): Promise<T> {\n    return onfulfilled(await this.run());\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented');\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n  const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n    ? getRectForAlignment(parentResult)\n    : parentResult.detection));\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  );\n\n  const results = await computeResults(faces);\n\n  faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n  return results;\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment?: (parentResultLocal: WithFaceLandmarks<TSource, any>) => FaceDetection,\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async (faces) => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment,\n  );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512],\n    };\n\n    super(config);\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeightMap(weightMap);\n  }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]> => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i]),\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces,\n    );\n\n    return extendWithFaceExpressions(parentResult, faceExpressions);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n    const parentResults = await this.parentTask;\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i];\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n    });\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces,\n    );\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n  ) {\n    super();\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>)),\n      null,\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      // eslint-disable-next-line no-shadow\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return extendWithFaceDescriptor(parentResult, descriptor);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected useTinyLandmarkNet: boolean,\n  ) {\n    super();\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net;\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const detections = parentResults.map((res) => res.detection);\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections);\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      (face) => this.landmarkNet.detectLandmarks(face),\n    )) as FaceLandmarks68[];\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return parentResults.map((parentResult, i) => extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i]));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { detection } = parentResult;\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection]);\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n  ) {\n    super();\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n  public async run(): Promise<FaceDetection[]> {\n    const { input, options } = this;\n\n    // eslint-disable-next-line no-nested-ternary\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      // eslint-disable-next-line no-shadow\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        // eslint-disable-next-line no-nested-ternary\n        options instanceof SsdMobilenetv1Options\n          // eslint-disable-next-line no-shadow\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              // eslint-disable-next-line no-shadow\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      );\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');\n    }\n\n    return faceDetectionFunction(input);\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}>[]>(async (resolve) => {\n      const detections = await this.run();\n      resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach((faceDetection) => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}> | undefined>(async (resolve) => {\n      const detection = await this.run();\n      resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number,\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {},\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n  const desc1 = Array.from(arr1);\n  const desc2 = Array.from(arr2);\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + (diff ** 2), 0),\n  );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6,\n  ) {\n    this._distanceThreshold = distanceThreshold;\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n    if (!inputArray.length) {\n      throw new Error('FaceRecognizer.constructor - expected atleast one input');\n    }\n\n    let count = 1;\n    const createUniqueLabel = () => `person ${count++}`;\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc;\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n      }\n\n      throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>');\n    });\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n  public get distanceThreshold(): number { return this._distanceThreshold; }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map((d) => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1);\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n        label,\n        this.computeMeanDistance(queryDescriptor, descriptors),\n      ))\n      .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor);\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance);\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector();\n  net.extractWeights(weights);\n  return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height);\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height));\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height);\n  }\n\n  return results;\n}\n"],
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"../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Exp.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/ExpandDims.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Fill.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/FlipLeftRight.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Floor.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/FloorDiv.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/FusedBatchNorm.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/FusedConv2D.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/FusedDepthwiseConv2D.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/GatherNd.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/GatherV2.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Greater.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/GreaterEqual.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/LeakyRelu.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Less.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/LessEqual.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Log.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/LogicalAnd.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Max.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Maximum.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/MaxPool.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Mean.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Min.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Minimum.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Multiply.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Neg.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/NonMaxSuppression_util.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/NonMaxSuppressionV3.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/NonMaxSuppressionV4.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/NonMaxSuppressionV5.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/NotEqual.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/OneHot.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/OnesLike.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Pack.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/PadV2.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Pow.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Prelu.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Prod.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Range.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/RealDiv.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Relu.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Relu6.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/ResizeBilinear.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Reverse.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/RotateWithOffset.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Round.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Rsqrt.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/ScatterNd.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Select.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Sigmoid.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Sin.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Slice.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Softmax.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/SplitV.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Sqrt.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Square.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/SquaredDifference.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Step.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/StridedSlice.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Sub.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Sum.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Tanh.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Tile.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/TopK.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/Unpack.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/kernels/ZerosLike.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/register_all_kernels.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/flags_wasm.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/backend_wasm.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js", "../node_modules/@tensorflow/tfjs-backend-wasm/src/version.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/base.ts", "../node_modules/@tensorflow/tfjs-backend-wasm/src/index.ts", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/NeuralNetwork.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeightMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeightMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeightMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeightMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeightMap.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeightMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts"],
+  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\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 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\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}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}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(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 shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){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\"){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;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":171,\"maximum\":171+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;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)}var WASM_PAGE_SIZE=65536;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 STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_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_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"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_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}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){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;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 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 Math_ceil=Math.ceil;var Math_floor=Math.floor;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+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}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(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}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)&&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()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;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\"){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\");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();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13568;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(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>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 __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";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 __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";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={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12816;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13056;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___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+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(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+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>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){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},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\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(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.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}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){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},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){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;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);__ATEXIT__.unshift({func:func,arg: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_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){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{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}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];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}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){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;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(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,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)}},inEventHandler:0,currentEventHandler:null,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_async_queue_on_thread_(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_async_queue_on_thread_(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){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}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}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},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},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],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\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){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 contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};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.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() 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,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_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_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;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;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>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+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}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 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79: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: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();else PThread.initWorker();var GLctx;GL.init();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,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var 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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)}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_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;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){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;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 exitRuntime(){runtimeExited=true}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 Math_ceil=Math.ceil;var Math_floor=Math.floor;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+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}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(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}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)&&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()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);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\"){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\");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();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};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}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"Ceil\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Floor\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"LeakyRelu\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"MaxPool\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Maximum\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"Mean\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"Min\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Minimum\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Multiply\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"Neg\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"NotEqual\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"OneHot\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"PadV2\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"Pow\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Prelu\"]).apply(null,arguments)};var _Prod=Module[\"_Prod\"]=function(){return(_Prod=Module[\"_Prod\"]=Module[\"asm\"][\"Prod\"]).apply(null,arguments)};var _RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"RealDiv\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Relu\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Relu6\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"ResizeBilinear\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Reverse\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"RotateWithOffset\"]).apply(null,arguments)};var _Round=Module[\"_Round\"]=function(){return(_Round=Module[\"_Round\"]=Module[\"asm\"][\"Round\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Rsqrt\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"ScatterNd\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"SelectV2\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"Sigmoid\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Sin\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Softmax\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Sqrt\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Square\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"SquaredDifference\"]).apply(null,arguments)};var _Step=Module[\"_Step\"]=function(){return(_Step=Module[\"_Step\"]=Module[\"asm\"][\"Step\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"StridedSlice\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sub\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Sum\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Tanh\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Tile\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"TopK\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Transpose\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"_FusedMatMul\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"malloc\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"free\"]).apply(null,arguments)};var __start=Module[\"__start\"]=function(){return(__start=Module[\"__start\"]=Module[\"asm\"][\"_start\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"stackSave\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"stackAlloc\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"stackRestore\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}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();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);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(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}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()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n  return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModule;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModule; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModule\"] = WasmBackendModule;\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, 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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, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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 threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;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:selfThreadId})}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};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;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)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}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;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;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[\"dynCall_ii\"](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\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){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.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\");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, "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false,\n) {\n  ctx.beginPath();\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx];\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(x, y);\n  });\n\n  if (isClosed) {\n    const from = points[points.length - 1];\n    const to = points[0];\n    if (!from || !to) {\n      return;\n    }\n\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(to.x, to.y);\n  }\n\n  ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = 10 ** prec;\n  return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width);\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n  private _width: number\n\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n    }\n\n    this._width = width;\n    this._height = height;\n  }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height);\n  }\n}\n", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x;\n    this._y = y;\n  }\n\n  get x(): number { return this._x; }\n\n  get y(): number { return this._y; }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y);\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y);\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y);\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y);\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y));\n  }\n\n  public magnitude(): number {\n    return Math.sqrt((this.x ** 2) + (this.y ** 2));\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y));\n  }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n    }\n  }\n\n  private _x: number\n\n  private _y: number\n\n  private _width: number\n\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any;\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n    Box.assertIsValidBox({\n      x, y, width, height,\n    }, 'Box.constructor', allowNegativeDimensions);\n\n    this._x = x;\n    this._y = y;\n    this._width = width;\n    this._height = height;\n  }\n\n  public get x(): number { return this._x; }\n\n  public get y(): number { return this._y; }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public get left(): number { return this.x; }\n\n  public get top(): number { return this.y; }\n\n  public get right(): number { return this.x + this.width; }\n\n  public get bottom(): number { return this.y + this.height; }\n\n  public get area(): number { return this.width * this.height; }\n\n  public get topLeft(): Point { return new Point(this.left, this.top); }\n\n  public get topRight(): Point { return new Point(this.right, this.top); }\n\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n  public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.round(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.floor(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public toSquare(): Box<BoxType> {\n    let {\n      x, y, width, height,\n    } = this;\n    const diff = Math.abs(width - height);\n    if (width < height) {\n      x -= (diff / 2);\n      width += diff;\n    }\n    if (height < width) {\n      y -= (diff / 2);\n      height += diff;\n    }\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY,\n    });\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    const [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY,\n    ];\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const {\n      x, y, right, bottom,\n    } = this;\n    const clippedX = Math.max(x, 0);\n    const clippedY = Math.max(y, 0);\n\n    const newWidth = right - clippedX;\n    const newHeight = bottom - clippedY;\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n    return (new Box({\n      x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n    })).floor();\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this;\n    const x = this.x + sx;\n    const y = this.y + sy;\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1;\n    const h = this.height + 1;\n\n    const dx = 1;\n    const dy = 1;\n    let edx = w;\n    let edy = h;\n\n    let x = this.left;\n    let y = this.top;\n    let ex = this.right;\n    let ey = this.bottom;\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w;\n      ex = imageWidth;\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h;\n      ey = imageHeight;\n    }\n    if (x < 1) {\n      edy = 2 - x;\n      x = 1;\n    }\n    if (y < 1) {\n      edy = 2 - y;\n      y = 1;\n    }\n\n    return {\n      dy, edy, dx, edx, y, ey, x, ex, w, h,\n    };\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height),\n    }).toSquare().round();\n  }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({\n      left, top, right, bottom,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n\n  private _classScore: number\n\n  private _className: string\n\n  private _box: Rect\n\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions,\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n    this._score = score;\n    this._classScore = classScore;\n    this._className = className;\n    this._box = new Box(relativeBox).rescale(this._imageDims);\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n\n  public get className(): string { return this._className; }\n\n  public get box(): Box { return this._box; }\n\n  public get imageDims(): Dimensions { return this._imageDims; }\n\n  public get imageWidth(): number { return this.imageDims.width; }\n\n  public get imageHeight(): number { return this.imageDims.height; }\n\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height },\n    );\n  }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions,\n  ) {\n    super(score, score, '', relativeBox, imageDims);\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height);\n    return new FaceDetection(score, relativeBox, imageDims);\n  }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n  const interSection = width * height;\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map((pt) => pt.x);\n  const ys = pts.map((pt) => pt.y);\n  const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n  const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n  const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n  const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n  return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true,\n): number[] {\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map((c) => c.boxIndex);\n\n  const pick: number[] = [];\n\n  while (indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number;\n    pick.push(curr);\n\n    const indices = indicesSortedByScore;\n\n    const outputs: number[] = [];\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i];\n\n      const currBox = boxes[curr];\n      const idxBox = boxes[idx];\n\n      outputs.push(iou(currBox, idxBox, isIOU));\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold,\n    );\n  }\n\n  return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb;\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n    return tf.sub(x, avg_rgb);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [height, width] = imgTensor.shape.slice(1);\n    if (height === width) {\n      return imgTensor;\n    }\n\n    const dimDiff = Math.abs(height - width);\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n    const paddingAxis = height > width ? 2 : 1;\n\n    const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice();\n      paddingTensorShape[paddingAxis] = paddingAmountLocal;\n      return tf.fill(paddingTensorShape, 0, 'float32');\n    };\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount);\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null;\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend,\n    ]\n      .filter((t) => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n    return tf.concat(tensorsToStack, paddingAxis);\n  });\n}\n", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice();\n  for (let i = array.length - 1; i > 0; i--) {\n    const j = Math.floor(Math.random() * (i + 1));\n    const x = array[i];\n    array[i] = array[j];\n    array[j] = x;\n  }\n  return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({\n      x, y, width, height,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n\n  protected _positions: Point[]\n\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0),\n  ) {\n    const { width, height } = imgDims;\n    this._imgDims = new Dimensions(width, height);\n    this._shift = shift;\n    this._positions = relativeFaceLandmarkPositions.map(\n      (pt) => pt.mul(new Point(width, height)).add(shift),\n    );\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n  public get imageWidth(): number { return this._imgDims.width; }\n\n  public get imageHeight(): number { return this._imgDims.height; }\n\n  public get positions(): Point[] { return this._positions; }\n\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n    );\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height },\n    );\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y),\n    );\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y);\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection);\n\n      return this.shiftBy(box.x, box.y).align(null, options);\n    }\n\n    const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n    if (useDlibAlignment) {\n      return this.alignDlib();\n    }\n\n    return this.alignMinBbox(minBoxPadding);\n  }\n\n  private alignDlib(): Box {\n    const centers = this.getRefPointsForAlignment();\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n    const size = Math.floor(eyeToMouthDist / relScale);\n\n    const refPoint = getCenterPoint(centers);\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions);\n    return box.pad(box.width * padding, box.height * padding);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class');\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions;\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]]),\n    ];\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17);\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22);\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27);\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36);\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42);\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48);\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth(),\n    ].map(getCenterPoint);\n  }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label;\n    this._distance = distance;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get distance(): number { return this._distance; }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n  }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee);\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box);\n    this._label = label;\n  }\n\n  public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n  private _label: string\n\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n    }\n\n    this._label = label;\n    this._descriptors = descriptors;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get descriptors(): Float32Array[] { return this._descriptors; }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d)),\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee);\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n    }\n  }\n\n  private _score: number\n\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label);\n    this._score = score;\n    this._classScore = classScore;\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<\n  TSource\n>(\n  sourceObj: TSource,\n  detection: FaceDetection,\n): WithFaceDetection<TSource> {\n  const extension = { detection };\n  return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n  const fetch = window.fetch;\n  if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n  const readFile = () => {\n    throw new Error('readFile - filesystem not available for browser environment');\n  };\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile,\n  };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n  let requireFsError = '';\n\n  if (!fs) {\n    try {\n      // eslint-disable-next-line global-require\n      fs = require('fs');\n    } catch (err) {\n      requireFsError = err.toString();\n    }\n  }\n\n  const readFile = fs\n    ? (filePath: string) => new Promise<Buffer>((resolve, reject) => {\n      fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n    })\n    : () => {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n    };\n\n  return {\n    readFile,\n  };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n  // eslint-disable-next-line dot-notation\n  const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n  const Image = global.Image || global.HTMLImageElement;\n\n  const createCanvasElement = () => {\n    if (Canvas) return new Canvas();\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n  };\n\n  const createImageElement = () => {\n    if (Image) return new Image();\n    throw new Error('createImageElement - missing Image implementation for nodejs environment');\n  };\n\n  const fetch = global.fetch;\n  // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n  const fileSystem = createFileSystem();\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n    Image: Image || class {},\n    ImageData: global.ImageData || class {},\n    Video: global.HTMLVideoElement || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem,\n  };\n}\n", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n  }\n  return environment;\n}\n\nfunction setEnv(env: Environment) {\n  environment = env;\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) return setEnv(createBrowserEnv());\n  if (isNodejs()) return setEnv(createNodejsEnv());\n  return null;\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize();\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env;\n  environment.Canvas = Canvas;\n  environment.Image = Image;\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n  environment.createImageElement = env.createImageElement || (() => new Image());\n\n  environment.ImageData = env.ImageData || environment.ImageData;\n  environment.Video = env.Video || environment.Video;\n  environment.fetch = env.fetch || environment.fetch;\n  environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg);\n  }\n  return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg;\n  }\n\n  const canvas = resolveInput(canvasArg);\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n  }\n\n  const ctx = canvas.getContext('2d');\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null');\n  }\n\n  return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n  // eslint-disable-next-line no-unused-vars\n  TOP_LEFT = 'TOP_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  TOP_RIGHT = 'TOP_RIGHT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n\n  public backgroundColor: string\n\n  public fontColor: string\n\n  public fontSize: number\n\n  public fontStyle: string\n\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const {\n      anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = options;\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n    this.fontSize = fontSize || 14;\n    this.fontStyle = fontStyle || 'Georgia';\n    this.padding = padding || 4;\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n\n  public anchor : IPoint\n\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {},\n  ) {\n    // eslint-disable-next-line no-nested-ternary\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text);\n    this.anchor = anchor;\n    this.options = new DrawTextFieldOptions(options);\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options;\n    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options;\n    return this.text.length * fontSize + (2 * padding);\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options;\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n    const textFieldWidth = this.measureWidth(ctx);\n    const textFieldHeight = this.measureHeight();\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims;\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n      return { x: newX, y: newY };\n    }\n    return { x, y };\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg);\n    const ctx = getContext2dOrThrow(canvas);\n\n    const {\n      backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = this.options;\n\n    ctx.font = `${fontSize}px ${fontStyle}`;\n    const maxTextWidth = this.measureWidth(ctx);\n    const textHeight = this.measureHeight();\n\n    ctx.fillStyle = backgroundColor;\n    const upperLeft = this.getUpperLeft(ctx, canvas);\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x;\n      const y = padding + upperLeft.y + ((i + 1) * fontSize);\n      ctx.fillText(textLine, x, y);\n    });\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport {\n  AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions,\n} from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n\n  public lineWidth: number\n\n  public drawLabelOptions: DrawTextFieldOptions\n\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const {\n      boxColor, lineWidth, label, drawLabelOptions,\n    } = options;\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n    this.lineWidth = lineWidth || 2;\n    this.label = label;\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor,\n    };\n    this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {},\n  ) {\n    this.box = new Box(box);\n    this.options = new DrawBoxOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const { boxColor, lineWidth } = this.options;\n\n    const {\n      x, y, width, height,\n    } = this.box;\n    ctx.strokeStyle = boxColor;\n    ctx.lineWidth = lineWidth;\n    ctx.strokeRect(x, y, width, height);\n\n    const { label } = this.options;\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n    }\n  }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>,\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n  detectionsArray.forEach((det) => {\n    // eslint-disable-next-line no-nested-ternary\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n    // eslint-disable-next-line no-nested-ternary\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n    const label = score ? `${round(score)}` : undefined;\n    new DrawBox(box, { label }).draw(canvasArg);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n  const { Image, Video } = env.getEnv();\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n  // eslint-disable-next-line consistent-return\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null);\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return;\n      // eslint-disable-next-line no-use-before-define\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      reject(e);\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return;\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      resolve(e);\n    }\n\n    media.addEventListener('load', onLoad);\n    media.addEventListener('error', onError);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n    const reader = new FileReader();\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n      const img = env.getEnv().createImageElement();\n      img.onload = () => resolve(img);\n      img.onerror = reject;\n      img.src = reader.result as string;\n    };\n    reader.onerror = reject;\n    reader.readAsDataURL(buf);\n  });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n  const { Image, Video } = env.getEnv();\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight);\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight);\n  }\n  return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n  const { createCanvasElement } = env.getEnv();\n  const canvas = createCanvasElement();\n  canvas.width = width;\n  canvas.height = height;\n  return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n  const { ImageData } = env.getEnv();\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet');\n  }\n\n  const { width, height } = dims || getMediaDimensions(media);\n  const canvas = createCanvas({ width, height });\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n  }\n  return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement,\n): Promise<HTMLCanvasElement> {\n  const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n  await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n  imgTensor3D.dispose();\n\n  return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n  const { Image, Canvas, Video } = env.getEnv();\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n  const { Image, Canvas } = env.getEnv();\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n  }\n\n  const dims = getMediaDimensions(input);\n  const scale = inputSize / Math.max(dims.height, dims.width);\n  const width = scale * dims.width;\n  const height = scale * dims.height;\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n  const offset = Math.abs(width - height) / 2;\n  const dx = centerImage && width < height ? offset : 0;\n  const dy = centerImage && height < width ? offset : 0;\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n  return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport {\n  computeReshapedDimensions, isTensor3D, isTensor4D, range,\n} from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n\n  private _canvases: HTMLCanvasElement[] = []\n\n  private _batchSize: number\n\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false,\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput;\n    this._batchSize = inputs.length;\n\n    inputs.forEach((input, idx) => {\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = input.shape;\n        return;\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0];\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n        }\n\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = (input as any).shape.slice(1);\n        return;\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n      this._canvases[idx] = canvas;\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n    });\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors;\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases;\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput;\n  }\n\n  public get batchSize(): number {\n    return this._batchSize;\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions;\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize;\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n    );\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx];\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0];\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1];\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n    }\n\n    const width = this.getInputWidth(batchIdx);\n    const height = this.getInputHeight(batchIdx);\n    return computeReshapedDimensions({ width, height }, this.inputSize);\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n    this._inputSize = inputSize;\n\n    return tf.tidy(() => {\n      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n        const input = this.getInput(batchIdx);\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>();\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3);\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n      });\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n\n      return batchTensor;\n    });\n  }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs;\n  }\n\n  const inputArgArray = Array.isArray(inputs)\n    ? inputs\n    : [inputs];\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input');\n  }\n\n  const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n\n  const inputArray = inputArgArray.map(resolveInput);\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0];\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n      }\n    }\n  });\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)),\n  );\n\n  return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>,\n): Promise<HTMLCanvasElement[]> {\n  const { Canvas } = env.getEnv();\n\n  let canvas = input as HTMLCanvasElement;\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input);\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported');\n    }\n\n    const tensorOrCanvas = netInput.getInput(0);\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas);\n  }\n\n  const ctx = getContext2dOrThrow(canvas);\n  const boxes = detections.map(\n    (det) => (det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det),\n  )\n    .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n\n  return boxes.map(({\n    x, y, width, height,\n  }) => {\n    const faceImg = createCanvas({ width, height });\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n    return faceImg;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>,\n): Promise<tf.Tensor3D[]> {\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported');\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n    const boxes = detections.map(\n      (det) => (det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det),\n    )\n      .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n    const faceTensors = boxes.map(({\n      x, y, width, height,\n    }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n    return faceTensors;\n  });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n  url: string,\n  // eslint-disable-next-line no-undef\n  init?: RequestInit,\n): Promise<Response> {\n  const { fetch } = env.getEnv();\n  const res = await fetch(url, init);\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n  }\n  return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri);\n  const blob = await (res).blob();\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n  }\n  return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename,\n    };\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`,\n    };\n  }\n  // eslint-disable-next-line no-nested-ternary\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter((s) => s);\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename;\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri);\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference;\n  input.width = width;\n  input.height = height;\n  return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork<TNetParams> {\n  constructor(name: string) {\n    this._name = name;\n  }\n\n  protected _params: TNetParams | undefined = undefined\n\n  protected _paramMappings: ParamMapping[] = []\n\n  public _name: any;\n\n  public get params(): TNetParams | undefined { return this._params; }\n\n  public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n  public get isLoaded(): boolean { return !!this.params; }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    return obj[objProp];\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    obj[objProp].dispose();\n    obj[objProp] = tensor;\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath),\n    }));\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable());\n    });\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync());\n      variable.dispose();\n      this.reassignParamFromPath(path, tensor);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach((param) => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`);\n      }\n      param.tensor.dispose();\n    });\n    this._params = undefined;\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr)),\n    );\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl);\n      return;\n    }\n    await this.loadFromUri(weightsOrUrl);\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`);\n    }\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n    }\n    const { readFile } = env.getEnv();\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)));\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n    const manifest = JSON.parse((await readFile(manifestUri)).toString());\n    const weightMap = await loadWeights(manifest, modelBaseUri);\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const { paramMappings, params } = this.extractParams(weights);\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error('traversePropertyPath - model has no loaded params');\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      // eslint-disable-next-line no-prototype-builtins\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n      }\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n    }, { nextObj: this.params });\n\n    const { obj, objProp } = result;\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n    }\n\n    return { obj, objProp };\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number],\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n    out = tf.add(out, params.bias);\n    return out;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n  });\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias,\n    ) as tf.Tensor4D;\n\n    return withRelu ? tf.relu(out) : out;\n  });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach((path) => {\n    if (!paramMappings.some((pm) => pm.originalPath === path)) {\n      weightMap[path].dispose();\n    }\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams => {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): FCParams => {\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias,\n    };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  // eslint-disable-next-line no-useless-constructor\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    public depthwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public pointwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public bias: tf.Tensor1D,\n  // eslint-disable-next-line no-empty-function\n  ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n\nexport function loadSeparableConvParamsFactory(\n  // eslint-disable-next-line no-unused-vars\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T,\n) {\n  return (prefix: string): SeparableConvParams => {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4);\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  return (originalPath: string, paramRank: number, mappedPath?: string) => {\n    const tensor = weightMap[originalPath];\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath },\n    );\n\n    return tensor;\n  };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights;\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights);\n    remainingWeights = remainingWeights.slice(numWeights);\n    return ret;\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights;\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights,\n  };\n}\n", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock4Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: {\n      dense0, dense1, dense2, dense3,\n    },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return (prefix: string): ConvParams => {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return { filters, bias };\n  };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock4Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n  constructor() {\n    super('FaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock4(normalized, params.dense0, true);\n      out = denseBlock4(out, params.dense1);\n      out = denseBlock4(out, params.dense2);\n      out = denseBlock4(out, params.dense3);\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams,\n): tf.Tensor2D {\n  return tf.tidy(() => tf.add(\n    tf.matMul(x, params.weights),\n    params.bias,\n  ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: extractFcParams('fc'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n  const featureExtractorMap: tf.NamedTensorMap = {};\n  const classifierMap: tf.NamedTensorMap = {};\n\n  Object.keys(weightMap).forEach((key) => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n    map[key] = weightMap[key];\n  });\n\n  return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name);\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor;\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  protected abstract getClassifierChannelsIn(): number\n\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeightMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const cIn = this.getClassifierChannelsIn();\n    const cOut = this.getClassifierChannelsOut();\n    const classifierWeightSize = (cOut * cIn) + cOut;\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n  public neutral: number\n\n  public happy: number\n\n  public sad: number\n\n  public angry: number\n\n  public fearful: number\n\n  public disgusted: number\n\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx];\n    });\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map((expression) => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor);\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)));\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n      const data = await t.data();\n      t.dispose();\n      return data;\n    }));\n    out.dispose();\n\n    const predictionsByBatch = probabilitesByBatch\n      .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7;\n  }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n>(\n  sourceObj: TSource,\n  expressions: FaceExpressions,\n): WithFaceExpressions<TSource> {\n  const extension = { expressions };\n  return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint,\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n  faceExpressionsArray.forEach((e) => {\n    // eslint-disable-next-line no-nested-ternary\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined);\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n    }\n\n    const sorted = expr.asSortedArray();\n    const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0));\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n      anchor,\n    );\n    drawTextField.draw(canvasArg);\n  });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n    landmarks: TFaceLandmarks\n    unshiftedLandmarks: TFaceLandmarks\n    alignedRect: FaceDetection\n  }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    // eslint-disable-next-line dot-notation\n    && obj['landmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['alignedRect'] instanceof FaceDetection;\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks<TSource, TFaceLandmarks> {\n  const { box: shift } = sourceObj.detection;\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y);\n\n  const rect = landmarks.align();\n  const { imageDims } = sourceObj.detection;\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect,\n  };\n\n  return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n\n  public drawPoints: boolean\n\n  public lineWidth: number\n\n  public pointSize: number\n\n  public lineColor: string\n\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const {\n      drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n    } = options;\n    this.drawLines = drawLines;\n    this.drawPoints = drawPoints;\n    this.lineWidth = lineWidth || 1;\n    this.pointSize = pointSize || 2;\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {},\n  ) {\n    this.faceLandmarks = faceLandmarks;\n    this.options = new DrawFaceLandmarksOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const {\n      drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n    } = this.options;\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor;\n      ctx.lineWidth = lineWidth;\n      drawContour(ctx, this.faceLandmarks.getJawOutline());\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getNose());\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n      drawContour(ctx, this.faceLandmarks.getMouth(), true);\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor;\n      ctx.fillStyle = pointColor;\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath();\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n        ctx.fill();\n      };\n      this.faceLandmarks.positions.forEach(drawPoint);\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>,\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n  faceLandmarksArray.forEach((f) => {\n    // eslint-disable-next-line no-nested-ternary\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg);\n  });\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x;\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n  out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n  return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n  out = tf.add(out, x);\n  return out;\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception');\n    this._numMainBlocks = numMainBlocks;\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n    if (!params) {\n      throw new Error('TinyXception - load model before inference');\n    }\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n      out = reductionBlock(out, params.entry_flow.reduction_block_1);\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n      });\n      out = reductionBlock(out, params.exit_flow.reduction_block);\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks);\n  }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const age = extractFCParams(512, 1, 'fc/age');\n  const gender = extractFCParams(512, 2, 'fc/gender');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping,\n} from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender'),\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n  // eslint-disable-next-line no-unused-vars\n  FEMALE = 'female',\n  // eslint-disable-next-line no-unused-vars\n  MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport {\n  AgeAndGenderPrediction, Gender, NetOutput, NetParams,\n} from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet');\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor;\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n      const gender = fullyConnectedLayer(pooled, params.fc.gender);\n      return { age, gender };\n    });\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input);\n      return { age, gender: tf.softmax(gender) };\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n\n    const ages = tf.unstack(out.age);\n    const genders = tf.unstack(out.gender);\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i],\n    }));\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0];\n        const probMale = (await genderTensor.data())[0];\n        const isMale = probMale > 0.5;\n        const gender = isMale ? Gender.MALE : Gender.FEMALE;\n        const genderProbability = isMale ? probMale : (1 - probMale);\n\n        ageTensor.dispose();\n        genderTensor.dispose();\n        return { age, gender, genderProbability };\n      }),\n    );\n    out.age.dispose();\n    out.gender.dispose();\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model';\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeightMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width);\n      return {\n        width: width * scale,\n        height: height * scale,\n      };\n    });\n\n    const batchSize = inputDimensions.length;\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n      // eslint-disable-next-line no-unused-vars\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx];\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n      };\n\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          getPaddingX(batchIdx),\n          getPaddingY(batchIdx),\n        ))))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          inputDimensions[batchIdx].width,\n          inputDimensions[batchIdx].height,\n        ))));\n\n      return landmarkTensors as tf.Tensor2D;\n    });\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input);\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width })),\n      );\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input);\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data());\n        const xCoords = landmarksArray.filter((_, i) => isEven(i));\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width: netInput.getInputWidth(batchIdx),\n          },\n        );\n      },\n    ));\n\n    landmarkTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136;\n  }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n  weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock3Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock3Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n  constructor() {\n    super('TinyFaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D;\n\n      let out = denseBlock3(normalized, params.dense0, true);\n      out = denseBlock3(out, params.dense1);\n      out = denseBlock3(out, params.dense2);\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMapTiny(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights);\n  }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128;\n  }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n  const { filters, bias } = params.conv;\n\n  let out = tf.conv2d(x, filters, strides, padding);\n  out = tf.add(out, bias);\n  out = scale(out, params.scale);\n  return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping,\n} from '../common/index';\nimport { isFloat } from '../utils/index';\nimport {\n  ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues);\n    const depth = weights.length / (numFilters * filterSize * filterSize);\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0],\n      ),\n    );\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams {\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n    const bias = tf.tensor1d(extractWeights(numFilters));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n    const weights = tf.tensor1d(extractWeights(numWeights));\n    const biases = tf.tensor1d(extractWeights(numWeights));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` },\n    );\n\n    return {\n      weights,\n      biases,\n    };\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvLayerParams {\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n    return { conv, scale };\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false,\n  ): ResidualLayerParams {\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n    return { conv1, conv2 };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n  );\n  paramMappings.push({ paramPath: 'fc' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n    return { weights, biases };\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n    const scale = extractScaleLayerParams(prefix);\n\n    return { conv: { filters, bias }, scale };\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`),\n    };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const conv32_down = extractConvLayerParams('conv32_down');\n  const conv32_1 = extractResidualLayerParams('conv32_1');\n  const conv32_2 = extractResidualLayerParams('conv32_2');\n  const conv32_3 = extractResidualLayerParams('conv32_3');\n\n  const conv64_down = extractResidualLayerParams('conv64_down');\n  const conv64_1 = extractResidualLayerParams('conv64_1');\n  const conv64_2 = extractResidualLayerParams('conv64_2');\n  const conv64_3 = extractResidualLayerParams('conv64_3');\n\n  const conv128_down = extractResidualLayerParams('conv128_down');\n  const conv128_1 = extractResidualLayerParams('conv128_1');\n  const conv128_2 = extractResidualLayerParams('conv128_2');\n\n  const conv256_down = extractResidualLayerParams('conv256_down');\n  const conv256_1 = extractResidualLayerParams('conv256_1');\n  const conv256_2 = extractResidualLayerParams('conv256_2');\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n  const { fc } = weightMap;\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n  out = tf.add(out, x);\n  out = tf.relu(out);\n  return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape);\n  const isPad = pooled.shape[3] !== out.shape[3];\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number];\n    padShapeX[1] = 1;\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX);\n    out = tf.concat([out, zerosW], 1);\n\n    const padShapeY = [...out.shape] as [number, number, number, number];\n    padShapeY[2] = 1;\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY);\n    out = tf.concat([out, zerosH], 2);\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n  out = tf.add(pooled, out) as tf.Tensor4D;\n\n  out = tf.relu(out);\n  return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('FaceRecognitionNet');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D;\n\n      let out = convDown(normalized, params.conv32_down);\n      out = tf.maxPool(out, 3, 2, 'valid');\n\n      out = residual(out, params.conv32_1);\n      out = residual(out, params.conv32_2);\n      out = residual(out, params.conv32_3);\n\n      out = residualDown(out, params.conv64_down);\n      out = residual(out, params.conv64_1);\n      out = residual(out, params.conv64_2);\n      out = residual(out, params.conv64_3);\n\n      out = residualDown(out, params.conv128_down);\n      out = residual(out, params.conv128_1);\n      out = residual(out, params.conv128_2);\n\n      out = residualDown(out, params.conv256_down);\n      out = residual(out, params.conv256_1);\n      out = residual(out, params.conv256_2);\n      out = residualDown(out, params.conv256_down_out);\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n      const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n      return fullyConnected;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n    const netInput = await toNetInput(input);\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n    faceDescriptorTensors.forEach((t) => t.dispose());\n    return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet();\n  net.extractWeights(weights);\n  return net;\n}\n", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n>(\n  sourceObj: TSource,\n  descriptor: Float32Array,\n): WithFaceDescriptor<TSource> {\n  const extension = { descriptor };\n  return { ...sourceObj, ...extension };\n}\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n  TSource\n>(\n  sourceObj: TSource,\n  age: number,\n): WithAge<TSource> {\n  const extension = { age };\n  return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n    && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n  TSource\n>(\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number,\n): WithGender<TSource> {\n  const extension = { gender, genderProbability };\n  return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory,\n} from '../common/index';\nimport {\n  MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` },\n    );\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance,\n    };\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean,\n  ): ConvParams {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): PointwiseConvParams {\n    const {\n      filters,\n      bias,\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n    return {\n      filters,\n      batch_norm_offset: bias,\n    };\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): MobileNetV1.ConvPairParams {\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n    return { depthwise_conv, pointwise_conv };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13,\n    };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0,\n    };\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1,\n    };\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2,\n    };\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3,\n    };\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4,\n    };\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5,\n    };\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5,\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n  const mobilenetv1 = extractMobilenetV1Params();\n  const prediction_layer = extractPredictionLayerParams();\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4],\n  );\n  const output_layer = {\n    extra_dim,\n  };\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer,\n    },\n    paramMappings,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport {\n  ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping,\n} from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport {\n  BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams,\n} from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n    return { filters, batch_norm_offset };\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n    const mappedPrefix = `mobilenetv1/conv_${idx}`;\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance,\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n    };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13),\n    };\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n    return { filters, bias };\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n    );\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`,\n    );\n    return { box_encoding_predictor, class_predictor };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5),\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n  const extra_dim = weightMap['Output/extra_dim'];\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim,\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.conv2d(x, params.filters, strides, 'same');\n    out = tf.add(out, params.batch_norm_offset);\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number],\n) {\n  return tf.tidy(() => {\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon,\n    );\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13,\n    ];\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1;\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n      if (layerIdx === 11) {\n        conv11 = out;\n      }\n    });\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null');\n    }\n\n    return {\n      out,\n      conv11: conv11 as any,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync();\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0)\n      * Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n): number[] {\n  const numBoxes = boxes.shape[0];\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes,\n  );\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter((c) => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score);\n\n  const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n  const selected: number[] = [];\n\n  candidates.forEach((c) => {\n    if (selected.length >= outputSize) return;\n    const originalScore = c.score;\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j]);\n      if (iou === 0.0) continue;\n      c.score *= suppressFunc(iou);\n      if (c.score <= scoreThreshold) break;\n    }\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex);\n    }\n  });\n  return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1]),\n  ];\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2))),\n  ];\n  return {\n    sizes,\n    centers,\n  };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers,\n  } = getCenterCoordinatesAndSizesLayer(x0);\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out),\n    ]),\n    [1, 0],\n  );\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = boxPredictions.shape[0];\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n    );\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4],\n    );\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number],\n    );\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = x.shape[0];\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4],\n    );\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3],\n    );\n    return { boxPredictionEncoding, classPrediction };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams,\n) {\n  return tf.tidy(() => {\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding,\n    ], 1) as tf.Tensor4D;\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction,\n    ], 1) as tf.Tensor4D;\n\n    return {\n      boxPredictions,\n      classPredictions,\n    };\n  });\n}\n", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5;\n    this._maxResults = maxResults || 100;\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`);\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence; }\n\n  get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('SsdMobilenetv1');\n  }\n\n  public forwardInput(input: NetInput) {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D;\n      const features = mobileNetV1(x, params.mobilenetv1);\n\n      const {\n        boxPredictions,\n        classPredictions,\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer);\n    });\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {},\n  ): Promise<FaceDetection[]> {\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n\n    const netInput = await toNetInput(input);\n\n    const {\n      boxes: _boxes,\n      scores: _scores,\n    } = this.forwardInput(netInput);\n\n    const boxes = _boxes[0];\n    const scores = _scores[0];\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose();\n      _scores[i].dispose();\n    }\n\n    const scoresData = Array.from(await scores.data());\n    const iouThreshold = 0.5;\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence,\n    );\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0);\n    const inputSize = netInput.inputSize as number;\n    const padX = inputSize / reshapedDims.width;\n    const padY = inputSize / reshapedDims.height;\n\n    const boxesData = boxes.arraySync();\n    const results = indices\n      .map((idx) => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2]),\n        ].map((val) => val * padY);\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3]),\n        ].map((val) => val * padX);\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top,\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0),\n          },\n        );\n      });\n\n    boxes.dispose();\n    scores.dispose();\n    return results;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1();\n  net.extractWeights(weights);\n  return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`);\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612));\n    return tf.add(tf.relu(tf.sub(x, min)), min);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n    out = tf.sub(out, params.bn.sub);\n    out = tf.mul(out, params.bn.truediv);\n    out = tf.add(out, params.conv.bias);\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n    out = tf.add(out, params.bias);\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n    const sub = tf.tensor1d(extractWeights(size));\n    const truediv = tf.tensor1d(extractWeights(size));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` },\n    );\n    return { sub, truediv };\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n    return { conv, bn };\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0');\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  }\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry(`${prefix}/sub`, 1);\n    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n    return { sub, truediv };\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { filters, bias };\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`);\n    const bn = extractBatchNormParams(`${prefix}/bn`);\n    return { conv, bn };\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    // eslint-disable-next-line no-mixed-operators\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8'),\n    };\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8'),\n    };\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n  return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416;\n    this._scoreThreshold = scoreThreshold || 0.5;\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n    }\n  }\n\n  get inputSize(): number { return this._inputSize; }\n\n  get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n  public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2');\n    validateConfig(config);\n    this._config = config;\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config;\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1;\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0);\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n    let out = convWithBatchNorm(x, params.conv0);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = convWithBatchNorm(out, params.conv6);\n    out = convWithBatchNorm(out, params.conv7);\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor;\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D;\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n    });\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input), inputSize);\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput, inputSize);\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0),\n    };\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n    out.dispose();\n    out0.dispose();\n\n    const boxes = results.map((res) => res.box);\n    const scores = results.map((res) => res.score);\n    const classScores = results.map((res) => res.classScore);\n    const classNames = results.map((res) => this.config.classes[res.label]);\n\n    const indices = nonMaxSuppression(\n      boxes.map((box) => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true,\n    );\n\n    const detections = indices.map((idx) => new ObjectDetection(\n      scores[idx],\n      classScores[idx],\n      classNames[idx],\n      boxes[idx],\n      inputDimensions,\n    ));\n    return detections;\n  }\n\n  protected getDefaultModelName(): string {\n    return '';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap, this.config);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n    const numFilters = filterSizes ? filterSizes.length : undefined;\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number,\n  ) {\n    const { width, height } = inputBlobDimensions;\n    const inputSize = Math.max(width, height);\n    const correctionFactorX = inputSize / width;\n    const correctionFactorY = inputSize / height;\n\n    const numCells = outputTensor.shape[1];\n    const numBoxes = this.config.anchors.length;\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0);\n      return [boxes, scores, classScores];\n    });\n\n    const results = [] as any;\n    const scoresData = await scoresTensor.array();\n    const boxesData = await boxesTensor.array();\n    for (let row = 0; row < numCells; row++) {\n      for (let col = 0; col < numCells; col++) {\n        for (let anchor = 0; anchor < numBoxes; anchor++) {\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n            const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n            const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n            const x = (ctX - (widthLocal / 2));\n            const y = (ctY - (heightLocal / 2));\n            const pos = { row, col, anchor };\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 };\n            results.push({\n              box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n              score,\n              classScore: score * classScore,\n              label,\n              ...pos,\n            });\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose();\n    scoresTensor.dispose();\n    classScoresTensor.dispose();\n    return results;\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n    const { row, col, anchor } = pos;\n    const classesData = await classesTensor.array();\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label,\n      }))\n      .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n  constructor(withSeparableConvs: boolean = true) {\n    const config = {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      ...(withSeparableConvs\n        ? {\n          anchors: BOX_ANCHORS_SEPARABLE,\n          meanRgb: MEAN_RGB_SEPARABLE,\n        }\n        : {\n          anchors: BOX_ANCHORS,\n          withClassScores: true,\n        }),\n    };\n\n    super(config);\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs;\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeightMap(weightMap);\n  }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs);\n  net.extractWeights(weights);\n  return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask<T> {\n  public async then(\n    // eslint-disable-next-line no-unused-vars\n    onfulfilled: (value: T) => T | PromiseLike<T>,\n  ): Promise<T> {\n    return onfulfilled(await this.run());\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented');\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n  const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n    ? getRectForAlignment(parentResult)\n    : parentResult.detection));\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  );\n\n  const results = await computeResults(faces);\n\n  faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n  return results;\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment?: (parentResultLocal: WithFaceLandmarks<TSource, any>) => FaceDetection,\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async (faces) => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment,\n  );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512],\n    };\n\n    super(config);\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeightMap(weightMap);\n  }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]> => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i]),\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces,\n    );\n\n    return extendWithFaceExpressions(parentResult, faceExpressions);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n    const parentResults = await this.parentTask;\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map(\n        (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      )),\n      this.extractedFaces,\n    );\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i];\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n    });\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces,\n    );\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n  ) {\n    super();\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>)),\n      null,\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      // eslint-disable-next-line no-shadow\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return extendWithFaceDescriptor(parentResult, descriptor);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected useTinyLandmarkNet: boolean,\n  ) {\n    super();\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net;\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const detections = parentResults.map((res) => res.detection);\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections);\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      (face) => this.landmarkNet.detectLandmarks(face),\n    )) as FaceLandmarks68[];\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return parentResults.map((parentResult, i) => extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i]));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const { detection } = parentResult;\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection]);\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n  ) {\n    super();\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n  public async run(): Promise<FaceDetection[]> {\n    const { input, options } = this;\n\n    // eslint-disable-next-line no-nested-ternary\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      // eslint-disable-next-line no-shadow\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        // eslint-disable-next-line no-nested-ternary\n        options instanceof SsdMobilenetv1Options\n          // eslint-disable-next-line no-shadow\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              // eslint-disable-next-line no-shadow\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      );\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');\n    }\n\n    return faceDetectionFunction(input);\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}>[]>(async (resolve) => {\n      const detections = await this.run();\n      resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach((faceDetection) => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}> | undefined>(async (resolve) => {\n      const detection = await this.run();\n      resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options(),\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number,\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {},\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n  const desc1 = Array.from(arr1);\n  const desc2 = Array.from(arr2);\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + (diff ** 2), 0),\n  );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6,\n  ) {\n    this._distanceThreshold = distanceThreshold;\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n    if (!inputArray.length) {\n      throw new Error('FaceRecognizer.constructor - expected atleast one input');\n    }\n\n    let count = 1;\n    const createUniqueLabel = () => `person ${count++}`;\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc;\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n      }\n\n      throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>');\n    });\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n  public get distanceThreshold(): number { return this._distanceThreshold; }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map((d) => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1);\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n        label,\n        this.computeMeanDistance(queryDescriptor, descriptors),\n      ))\n      .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor);\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance);\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector();\n  net.extractWeights(weights);\n  return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height);\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height));\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height);\n  }\n\n  return results;\n}\n"],
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diff --git a/dist/face-api.json b/dist/face-api.json
index ed77009..78f7be2 100644
--- a/dist/face-api.json
+++ b/dist/face-api.json
@@ -1,7 +1,7 @@
 {
   "inputs": {
     "dist/tfjs.esm.js": {
-      "bytes": 1053904,
+      "bytes": 1055784,
       "imports": []
     },
     "src/draw/drawContour.ts": {
@@ -2591,7 +2591,7 @@
       "imports": [],
       "exports": [],
       "inputs": {},
-      "bytes": 1445466
+      "bytes": 1447740
     },
     "dist/face-api.js": {
       "imports": [],
@@ -2604,7 +2604,7 @@
           "bytesInOutput": 3011
         },
         "dist/tfjs.esm.js": {
-          "bytesInOutput": 1045393
+          "bytesInOutput": 1047274
         },
         "src/draw/index.ts": {
           "bytesInOutput": 280
@@ -3066,7 +3066,7 @@
           "bytesInOutput": 446
         }
       },
-      "bytes": 1132908
+      "bytes": 1134789
     }
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 }
diff --git a/dist/face-api.node-gpu.js b/dist/face-api.node-gpu.js
index 541d403..4881a26 100644
--- a/dist/face-api.node-gpu.js
+++ b/dist/face-api.node-gpu.js
@@ -5,5 +5,5 @@
   author: <https://github.com/vladmandic>'
   */
 
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rr(o,t){return(e,r,n)=>{let a=er.tensor2d(o(e*r),[e,r]),s=er.tensor1d(o(r));return t.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:a,bias:s}}}var ke=b(g());var or=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function me(o,t){return(e,r,n)=>{let a=ke.tensor4d(o(3*3*e),[3,3,e,1]),s=ke.tensor4d(o(e*r),[1,1,e,r]),i=ke.tensor1d(o(r));return t.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new or(a,s,i)}}function pe(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),n=o(`${t}/bias`,1);return new or(e,r,n)}}function j(o,t){return(e,r,n)=>{let a=o[e];if(!Bt(a,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have ${a}`);return t.push({originalPath:e,paramPath:n||e}),a}}function B(o){let t=o;function e(n){let a=t.slice(0,n);return t=t.slice(n),a}function r(){return t}return{extractWeights:e,getRemainingWeights:r}}function nr(o,t){let e=ce(o,t),r=me(o,t);function n(s,i,c,m=!1){let p=m?e(s,i,3,`${c}/conv0`):r(s,i,`${c}/conv0`),d=r(i,i,`${c}/conv1`),u=r(i,i,`${c}/conv2`);return{conv0:p,conv1:d,conv2:u}}function a(s,i,c,m=!1){let{conv0:p,conv1:d,conv2:u}=n(s,i,c,m),l=r(i,i,`${c}/conv3`);return{conv0:p,conv1:d,conv2:u,conv3:l}}return{extractDenseBlock3Params:n,extractDenseBlock4Params:a}}function wo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),{extractDenseBlock4Params:n}=nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2"),c=n(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i,dense3:c}}}function ar(o){return t=>{let e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function sr(o,t){let e=j(o,t),r=ar(e),n=pe(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function 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wo(t)}};var Co=b(g());var de=b(g());function Ae(o,t){return de.tidy(()=>de.add(de.matMul(o,t.weights),t.bias))}function Eo(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=B(o),i=rr(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function Mo(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return W(o,t),{params:n,paramMappings:t}}function ir(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var We=class extends S{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return Co.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t;return Ae(r.as2D(r.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Eo(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Mo(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),n=r*e+r,a=t.slice(0,t.length-n),s=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(s)}};var Zr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],It=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Zr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return 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t.push({paramPath:`${m}/filters`},{paramPath:`${m}/batch_norm_scale`},{paramPath:`${m}/batch_norm_offset`},{paramPath:`${m}/batch_norm_mean`},{paramPath:`${m}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:u,batch_norm_mean:l,batch_norm_variance:v}}function r(c,m,p,d,u){let l=at.tensor4d(o(c*m*p*p),[p,p,c,m]),v=at.tensor1d(o(m));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${u?"batch_norm_offset":"bias"}`}),{filters:l,bias:v}}function n(c,m,p,d){let{filters:u,bias:l}=r(c,m,p,d,!0);return{filters:u,batch_norm_offset:l}}function a(c,m,p){let d=e(c,`${p}/depthwise_conv`),u=n(c,m,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:u}}function s(){let 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Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var co=b(g());function aa(o,t){let e=ce(o,t);function r(s,i){let c=co.tensor1d(o(s)),m=co.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=me(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function 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c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=sa(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var mo=class extends S{constructor(t){super("TinyYolov2");io(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=Ft(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),Gt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(Gt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(r,e.conv7):r,Gt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?ot(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new lt(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Wr(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return nn(t,this.config)}extractParams(t){let e=this.config.filterSizes||mo.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return on(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],_=await u.array(),h=await d.array();for(let y=0;y<m;y++)for(let T=0;T<m;T++)for(let F=0;F<p;F++){let L=De(_[y][T][F][0]);if(!r||L>r){let G=(T+De(h[y][T][F][0]))/m*i,et=(y+De(h[y][T][F][1]))/m*c,it=Math.exp(h[y][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[y][T][F][3])*this.config.anchors[F].y/m*c,Pt=G-it/2,_t=et-X/2,wt={row:y,col:T,anchor:F},{classScore:Qt,label:ho}=this.withClassScores?await this.extractPredictedClass(l,wt):{classScore:1,label:0};v.push({box:new ee(Pt,_t,Pt+it,_t+X),score:L,classScore:L*Qt,label:ho,...wt})}}return d.dispose(),u.dispose(),l.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ge=mo;ge.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ve=class extends ge{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ia(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var gr=class extends lt{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var tt=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b(g());var po=b(g());async function Xt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>zt(c)?n(c):c.detection),s=r||(t instanceof po.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof po.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Xt([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],cn=[117.001,114.697,97.404];var Fe=class extends ge{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new Ut,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new dr,faceRecognitionNet:new xe,faceExpressionNet:new cr,ageGenderNet:new pr},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),pa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),da=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ua=o=>P.faceExpressionNet.predictExpressions(o),la=o=>P.ageGenderNet.predictAgeAndGender(o),dn=o=>P.ssdMobilenetv1.load(o),fa=o=>P.tinyFaceDetector.load(o),ha=o=>P.tinyYolov2.load(o),xa=o=>P.faceLandmark68Net.load(o),ba=o=>P.faceLandmark68TinyNet.load(o),ga=o=>P.faceRecognitionNet.load(o),va=o=>P.faceExpressionNet.load(o),ya=o=>P.ageGenderNet.load(o),Fa=dn,Ta=mn,Pa=pn;var uo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},_e=class extends uo{async run(){let t=await this.parentTask,e=await Xt(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>mr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},we=class extends uo{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new Pe(this,this.input)}},Zt=class extends _e{withAgeAndGender(){return new Jt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var lo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends lo{async run(){let t=await this.parentTask,e=await Xt(t,this.input,async r=>Promise.all(r.map(n=>P.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return hr(xr(r,s,i),a)})}withFaceExpressions(){return new _e(this,this.input)}},Pe=class extends lo{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await ye(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return hr(xr(t,r,n),e)}withFaceExpressions(){return new we(this,this.input)}},Jt=class extends Te{withFaceExpressions(){return new Zt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},qt=class extends Pe{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends vr{async run(){let t=await this.parentTask;return(await Xt(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>fr(t[n],r))}withFaceExpressions(){return new Zt(this,this.input)}withAgeAndGender(){return new Jt(this,this.input)}},Wt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await 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distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>fo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Ee(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this.distanceThreshold?e:new Ee("unknown",e.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>xt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function Ea(o){let t=new Fe;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(zt(o)){let 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rr(o,t){return(e,r,n)=>{let a=er.tensor2d(o(e*r),[e,r]),s=er.tensor1d(o(r));return t.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:a,bias:s}}}var ke=b(g());var or=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function me(o,t){return(e,r,n)=>{let a=ke.tensor4d(o(3*3*e),[3,3,e,1]),s=ke.tensor4d(o(e*r),[1,1,e,r]),i=ke.tensor1d(o(r));return t.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new or(a,s,i)}}function pe(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),n=o(`${t}/bias`,1);return new or(e,r,n)}}function j(o,t){return(e,r,n)=>{let a=o[e];if(!Bt(a,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have ${a}`);return t.push({originalPath:e,paramPath:n||e}),a}}function B(o){let t=o;function e(n){let a=t.slice(0,n);return t=t.slice(n),a}function r(){return t}return{extractWeights:e,getRemainingWeights:r}}function nr(o,t){let e=ce(o,t),r=me(o,t);function n(s,i,c,m=!1){let p=m?e(s,i,3,`${c}/conv0`):r(s,i,`${c}/conv0`),d=r(i,i,`${c}/conv1`),u=r(i,i,`${c}/conv2`);return{conv0:p,conv1:d,conv2:u}}function a(s,i,c,m=!1){let{conv0:p,conv1:d,conv2:u}=n(s,i,c,m),l=r(i,i,`${c}/conv3`);return{conv0:p,conv1:d,conv2:u,conv3:l}}return{extractDenseBlock3Params:n,extractDenseBlock4Params:a}}function wo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),{extractDenseBlock4Params:n}=nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2"),c=n(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i,dense3:c}}}function ar(o){return t=>{let e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function sr(o,t){let e=j(o,t),r=ar(e),n=pe(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function 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wo(t)}};var Co=b(g());var de=b(g());function Ae(o,t){return de.tidy(()=>de.add(de.matMul(o,t.weights),t.bias))}function Eo(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=B(o),i=rr(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function Mo(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return W(o,t),{params:n,paramMappings:t}}function ir(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var We=class extends S{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return Co.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t;return Ae(r.as2D(r.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Eo(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Mo(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),n=r*e+r,a=t.slice(0,t.length-n),s=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(s)}};var Zr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],It=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Zr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return 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t.push({paramPath:`${m}/filters`},{paramPath:`${m}/batch_norm_scale`},{paramPath:`${m}/batch_norm_offset`},{paramPath:`${m}/batch_norm_mean`},{paramPath:`${m}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:u,batch_norm_mean:l,batch_norm_variance:v}}function r(c,m,p,d,u){let l=at.tensor4d(o(c*m*p*p),[p,p,c,m]),v=at.tensor1d(o(m));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${u?"batch_norm_offset":"bias"}`}),{filters:l,bias:v}}function n(c,m,p,d){let{filters:u,bias:l}=r(c,m,p,d,!0);return{filters:u,batch_norm_offset:l}}function a(c,m,p){let d=e(c,`${p}/depthwise_conv`),u=n(c,m,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:u}}function s(){let 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Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var co=b(g());function aa(o,t){let e=ce(o,t);function r(s,i){let c=co.tensor1d(o(s)),m=co.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=me(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function 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c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=sa(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var mo=class extends S{constructor(t){super("TinyYolov2");io(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=Ft(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),Gt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(Gt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(r,e.conv7):r,Gt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?ot(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new lt(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Wr(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return nn(t,this.config)}extractParams(t){let e=this.config.filterSizes||mo.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return on(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],_=await u.array(),h=await d.array();for(let y=0;y<m;y++)for(let T=0;T<m;T++)for(let F=0;F<p;F++){let L=De(_[y][T][F][0]);if(!r||L>r){let G=(T+De(h[y][T][F][0]))/m*i,et=(y+De(h[y][T][F][1]))/m*c,it=Math.exp(h[y][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[y][T][F][3])*this.config.anchors[F].y/m*c,Pt=G-it/2,_t=et-X/2,wt={row:y,col:T,anchor:F},{classScore:Qt,label:ho}=this.withClassScores?await this.extractPredictedClass(l,wt):{classScore:1,label:0};v.push({box:new ee(Pt,_t,Pt+it,_t+X),score:L,classScore:L*Qt,label:ho,...wt})}}return d.dispose(),u.dispose(),l.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ge=mo;ge.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ve=class extends ge{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ia(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var gr=class extends lt{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var tt=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b(g());var po=b(g());async function Xt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>zt(c)?n(c):c.detection),s=r||(t instanceof po.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof po.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Xt([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],cn=[117.001,114.697,97.404];var Fe=class extends ge{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new Ut,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new dr,faceRecognitionNet:new xe,faceExpressionNet:new cr,ageGenderNet:new pr},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),pa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),da=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ua=o=>P.faceExpressionNet.predictExpressions(o),la=o=>P.ageGenderNet.predictAgeAndGender(o),dn=o=>P.ssdMobilenetv1.load(o),fa=o=>P.tinyFaceDetector.load(o),ha=o=>P.tinyYolov2.load(o),xa=o=>P.faceLandmark68Net.load(o),ba=o=>P.faceLandmark68TinyNet.load(o),ga=o=>P.faceRecognitionNet.load(o),va=o=>P.faceExpressionNet.load(o),ya=o=>P.ageGenderNet.load(o),Fa=dn,Ta=mn,Pa=pn;var uo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},_e=class extends uo{async run(){let t=await this.parentTask,e=await Xt(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>mr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},we=class extends uo{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new Pe(this,this.input)}},Zt=class extends _e{withAgeAndGender(){return new Jt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var lo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends lo{async run(){let t=await this.parentTask,e=await Xt(t,this.input,async r=>Promise.all(r.map(n=>P.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return hr(xr(r,s,i),a)})}withFaceExpressions(){return new _e(this,this.input)}},Pe=class extends lo{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await ye(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return hr(xr(t,r,n),e)}withFaceExpressions(){return new we(this,this.input)}},Jt=class extends Te{withFaceExpressions(){return new Zt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},qt=class extends Pe{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends vr{async run(){let t=await this.parentTask;return(await Xt(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>fr(t[n],r))}withFaceExpressions(){return new Zt(this,this.input)}withAgeAndGender(){return new Jt(this,this.input)}},Wt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return fr(t,e)}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}};var yr=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},Fr=class extends yr{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof je.Tensor?await se(this.input,e):await ae(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),t.map((a,s)=>le(a,n[s]))}withFaceExpressions(){return new Zt(this,this.input)}withAgeAndGender(){return new Jt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Tr=class extends yr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof je.Tensor?await se(this.input,[e]):await ae(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var Pr=class extends tt{constructor(t,e=new Z){super();this.input=t;this.options=e}},He=class extends Pr{async run(){let{input:t,options:e}=this,r=e instanceof gr?n=>P.tinyFaceDetector.locateFaces(n,e):e instanceof Z?n=>P.ssdMobilenetv1.locateFaces(n,e):e instanceof lt?n=>P.tinyYolov2.locateFaces(n,e):null;if(!r)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return r(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>$t({},r)))})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new _e(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Te(this.runAndExtendWithFaceDetections(),this.input)}},_r=class extends Pr{async run(){let t=await new He(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?$t({},e):void 0)})}withFaceLandmarks(t=!1){return new Tr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Pe(this.runAndExtendWithFaceDetection(),this.input)}};function _a(o,t=new Z){return new _r(o,t)}function wr(o,t=new Z){return new He(o,t)}async function un(o,t){return wr(o,new Z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function wa(o,t={}){return wr(o,new lt(t)).withFaceLandmarks().withFaceDescriptors()}var Da=un;function fo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a**2,0))}var Dr=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof xt)return s;if(s instanceof Float32Array)return new xt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new xt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>fo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Ee(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this.distanceThreshold?e:new Ee("unknown",e.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>xt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function Ea(o){let t=new Fe;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(zt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le($t(o,n),a)}return pt(o)?$t(o,o.detection.forSize(e,r)):o instanceof V||o instanceof M?o.forSize(e,r):o}var Ca=typeof process!="undefined",Na=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Ia={faceapi:No,node:Ca,browser:Na};
 //# sourceMappingURL=face-api.node-gpu.js.map
diff --git a/dist/face-api.node.js b/dist/face-api.node.js
index 5a638d1..49c3982 100644
--- a/dist/face-api.node.js
+++ b/dist/face-api.node.js
@@ -5,5 +5,5 @@
   author: <https://github.com/vladmandic>'
   */
 
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rr(o,t){return(e,r,n)=>{let a=er.tensor2d(o(e*r),[e,r]),s=er.tensor1d(o(r));return t.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:a,bias:s}}}var ke=b(g());var or=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function me(o,t){return(e,r,n)=>{let a=ke.tensor4d(o(3*3*e),[3,3,e,1]),s=ke.tensor4d(o(e*r),[1,1,e,r]),i=ke.tensor1d(o(r));return t.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new or(a,s,i)}}function pe(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),n=o(`${t}/bias`,1);return new or(e,r,n)}}function j(o,t){return(e,r,n)=>{let a=o[e];if(!Bt(a,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have ${a}`);return t.push({originalPath:e,paramPath:n||e}),a}}function B(o){let t=o;function e(n){let a=t.slice(0,n);return t=t.slice(n),a}function r(){return t}return{extractWeights:e,getRemainingWeights:r}}function nr(o,t){let e=ce(o,t),r=me(o,t);function n(s,i,c,m=!1){let p=m?e(s,i,3,`${c}/conv0`):r(s,i,`${c}/conv0`),d=r(i,i,`${c}/conv1`),u=r(i,i,`${c}/conv2`);return{conv0:p,conv1:d,conv2:u}}function a(s,i,c,m=!1){let{conv0:p,conv1:d,conv2:u}=n(s,i,c,m),l=r(i,i,`${c}/conv3`);return{conv0:p,conv1:d,conv2:u,conv3:l}}return{extractDenseBlock3Params:n,extractDenseBlock4Params:a}}function wo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),{extractDenseBlock4Params:n}=nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2"),c=n(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i,dense3:c}}}function ar(o){return t=>{let e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function sr(o,t){let e=j(o,t),r=ar(e),n=pe(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function 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wo(t)}};var Co=b(g());var de=b(g());function Ae(o,t){return de.tidy(()=>de.add(de.matMul(o,t.weights),t.bias))}function Eo(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=B(o),i=rr(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function Mo(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return W(o,t),{params:n,paramMappings:t}}function ir(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var We=class extends S{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return Co.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t;return Ae(r.as2D(r.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Eo(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Mo(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),n=r*e+r,a=t.slice(0,t.length-n),s=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(s)}};var Zr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],It=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Zr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return 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t.push({paramPath:`${m}/filters`},{paramPath:`${m}/batch_norm_scale`},{paramPath:`${m}/batch_norm_offset`},{paramPath:`${m}/batch_norm_mean`},{paramPath:`${m}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:u,batch_norm_mean:l,batch_norm_variance:v}}function r(c,m,p,d,u){let l=at.tensor4d(o(c*m*p*p),[p,p,c,m]),v=at.tensor1d(o(m));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${u?"batch_norm_offset":"bias"}`}),{filters:l,bias:v}}function n(c,m,p,d){let{filters:u,bias:l}=r(c,m,p,d,!0);return{filters:u,batch_norm_offset:l}}function a(c,m,p){let d=e(c,`${p}/depthwise_conv`),u=n(c,m,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:u}}function s(){let 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Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var co=b(g());function aa(o,t){let e=ce(o,t);function r(s,i){let c=co.tensor1d(o(s)),m=co.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=me(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function 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c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=sa(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var mo=class extends S{constructor(t){super("TinyYolov2");io(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=Ft(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Ft(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=Ft(r,e.conv6),r=Ft(r,e.conv7),Gt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?be(Gt(t,e.conv0,"valid",!1)):Tt(t,e.conv0);return r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv1),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv2),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv3),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv4),r=N.maxPool(r,[2,2],[2,2],"same"),r=Tt(r,e.conv5),r=N.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?Tt(r,e.conv6):r,r=e.conv7?Tt(r,e.conv7):r,Gt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let n=N.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?ot(n,this.config.meanRgb):n,n=n.div(N.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await E(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new lt(e),a=await E(t),s=await this.forwardInput(a,r),i=N.tidy(()=>N.unstack(s)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},m=await this.extractBoxes(i,a.getReshapedInputDimensions(0),n);s.dispose(),i.dispose();let p=m.map(h=>h.box),d=m.map(h=>h.score),u=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Wr(p.map(h=>h.rescale(r)),d,this.config.iouThreshold,!0).map(h=>new Dt(d[h],u[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return nn(t,this.config)}extractParams(t){let e=this.config.filterSizes||mo.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return on(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],_=await u.array(),h=await d.array();for(let y=0;y<m;y++)for(let T=0;T<m;T++)for(let F=0;F<p;F++){let L=De(_[y][T][F][0]);if(!r||L>r){let G=(T+De(h[y][T][F][0]))/m*i,et=(y+De(h[y][T][F][1]))/m*c,it=Math.exp(h[y][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[y][T][F][3])*this.config.anchors[F].y/m*c,Pt=G-it/2,_t=et-X/2,wt={row:y,col:T,anchor:F},{classScore:Qt,label:ho}=this.withClassScores?await this.extractPredictedClass(l,wt):{classScore:1,label:0};v.push({box:new ee(Pt,_t,Pt+it,_t+X),score:L,classScore:L*Qt,label:ho,...wt})}}return d.dispose(),u.dispose(),l.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ge=mo;ge.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ve=class extends ge{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ia(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var gr=class extends lt{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var tt=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b(g());var po=b(g());async function Xt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>zt(c)?n(c):c.detection),s=r||(t instanceof po.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof po.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Xt([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],cn=[117.001,114.697,97.404];var Fe=class extends ge{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new Ut,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new dr,faceRecognitionNet:new xe,faceExpressionNet:new cr,ageGenderNet:new pr},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),pa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),da=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ua=o=>P.faceExpressionNet.predictExpressions(o),la=o=>P.ageGenderNet.predictAgeAndGender(o),dn=o=>P.ssdMobilenetv1.load(o),fa=o=>P.tinyFaceDetector.load(o),ha=o=>P.tinyYolov2.load(o),xa=o=>P.faceLandmark68Net.load(o),ba=o=>P.faceLandmark68TinyNet.load(o),ga=o=>P.faceRecognitionNet.load(o),va=o=>P.faceExpressionNet.load(o),ya=o=>P.ageGenderNet.load(o),Fa=dn,Ta=mn,Pa=pn;var uo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},_e=class extends uo{async run(){let t=await this.parentTask,e=await Xt(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>mr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},we=class extends uo{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new Pe(this,this.input)}},Zt=class extends _e{withAgeAndGender(){return new Jt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var lo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends lo{async run(){let t=await this.parentTask,e=await Xt(t,this.input,async r=>Promise.all(r.map(n=>P.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return hr(xr(r,s,i),a)})}withFaceExpressions(){return new _e(this,this.input)}},Pe=class extends lo{async run(){let t=await 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distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>fo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Ee(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this.distanceThreshold?e:new Ee("unknown",e.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>xt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function Ea(o){let t=new Fe;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(zt(o)){let 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rr(o,t){return(e,r,n)=>{let a=er.tensor2d(o(e*r),[e,r]),s=er.tensor1d(o(r));return t.push({paramPath:`${n}/weights`},{paramPath:`${n}/bias`}),{weights:a,bias:s}}}var ke=b(g());var or=class{constructor(t,e,r){this.depthwise_filter=t;this.pointwise_filter=e;this.bias=r}};function me(o,t){return(e,r,n)=>{let a=ke.tensor4d(o(3*3*e),[3,3,e,1]),s=ke.tensor4d(o(e*r),[1,1,e,r]),i=ke.tensor1d(o(r));return t.push({paramPath:`${n}/depthwise_filter`},{paramPath:`${n}/pointwise_filter`},{paramPath:`${n}/bias`}),new or(a,s,i)}}function pe(o){return t=>{let e=o(`${t}/depthwise_filter`,4),r=o(`${t}/pointwise_filter`,4),n=o(`${t}/bias`,1);return new or(e,r,n)}}function j(o,t){return(e,r,n)=>{let a=o[e];if(!Bt(a,r))throw new Error(`expected weightMap[${e}] to be a Tensor${r}D, instead have ${a}`);return t.push({originalPath:e,paramPath:n||e}),a}}function B(o){let t=o;function e(n){let a=t.slice(0,n);return t=t.slice(n),a}function r(){return t}return{extractWeights:e,getRemainingWeights:r}}function nr(o,t){let e=ce(o,t),r=me(o,t);function n(s,i,c,m=!1){let p=m?e(s,i,3,`${c}/conv0`):r(s,i,`${c}/conv0`),d=r(i,i,`${c}/conv1`),u=r(i,i,`${c}/conv2`);return{conv0:p,conv1:d,conv2:u}}function a(s,i,c,m=!1){let{conv0:p,conv1:d,conv2:u}=n(s,i,c,m),l=r(i,i,`${c}/conv3`);return{conv0:p,conv1:d,conv2:u,conv3:l}}return{extractDenseBlock3Params:n,extractDenseBlock4Params:a}}function wo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=B(o),{extractDenseBlock4Params:n}=nr(e,t),a=n(3,32,"dense0",!0),s=n(32,64,"dense1"),i=n(64,128,"dense2"),c=n(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:s,dense2:i,dense3:c}}}function ar(o){return t=>{let e=o(`${t}/filters`,4),r=o(`${t}/bias`,1);return{filters:e,bias:r}}}function sr(o,t){let e=j(o,t),r=ar(e),n=pe(e);function a(i,c=!1){let m=c?r(`${i}/conv0`):n(`${i}/conv0`),p=n(`${i}/conv1`),d=n(`${i}/conv2`);return{conv0:m,conv1:p,conv2:d}}function 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wo(t)}};var Co=b(g());var de=b(g());function Ae(o,t){return de.tidy(()=>de.add(de.matMul(o,t.weights),t.bias))}function Eo(o,t,e){let r=[],{extractWeights:n,getRemainingWeights:a}=B(o),i=rr(n,r)(t,e,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function Mo(o){let t=[],e=j(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:r("fc")};return W(o,t),{params:n,paramMappings:t}}function ir(o){let t={},e={};return Object.keys(o).forEach(r=>{let n=r.startsWith("fc")?e:t;n[r]=o[r]}),{featureExtractorMap:t,classifierMap:e}}var We=class extends S{constructor(t,e){super(t);this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return Co.tidy(()=>{let r=t instanceof bt?this.faceFeatureExtractor.forwardInput(t):t;return Ae(r.as2D(r.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Eo(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=ir(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Mo(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),n=r*e+r,a=t.slice(0,t.length-n),s=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(s)}};var Zr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],It=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Zr.forEach((e,r)=>{this[e]=t[r]})}asSortedArray(){return 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t.push({paramPath:`${m}/filters`},{paramPath:`${m}/batch_norm_scale`},{paramPath:`${m}/batch_norm_offset`},{paramPath:`${m}/batch_norm_mean`},{paramPath:`${m}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:u,batch_norm_mean:l,batch_norm_variance:v}}function r(c,m,p,d,u){let l=at.tensor4d(o(c*m*p*p),[p,p,c,m]),v=at.tensor1d(o(m));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${u?"batch_norm_offset":"bias"}`}),{filters:l,bias:v}}function n(c,m,p,d){let{filters:u,bias:l}=r(c,m,p,d,!0);return{filters:u,batch_norm_offset:l}}function a(c,m,p){let d=e(c,`${p}/depthwise_conv`),u=n(c,m,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:u}}function s(){let 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Q=b(g());var K=b(g());function be(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function Ft(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),be(e)})}var St=b(g());function Tt(o,t){return St.tidy(()=>{let e=St.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=St.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=St.add(e,t.bias),be(e)})}var co=b(g());function aa(o,t){let e=ce(o,t);function r(s,i){let c=co.tensor1d(o(s)),m=co.tensor1d(o(s));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(s,i,c){let m=e(s,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let a=me(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}}function 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c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=pe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=sa(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:s,paramMappings:e}}var lt=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var mo=class extends S{constructor(t){super("TinyYolov2");io(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=Ft(t,e.conv0);return 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${r} filterSizes in config`);return on(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:a}=e,s=Math.max(n,a),i=s/n,c=s/a,m=t.shape[1],p=this.config.anchors.length,[d,u,l]=N.tidy(()=>{let y=t.reshape([m,m,p,this.boxEncodingSize]),T=y.slice([0,0,0,0],[m,m,p,4]),F=y.slice([0,0,0,4],[m,m,p,1]),L=this.withClassScores?N.softmax(y.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):N.scalar(0);return[T,F,L]}),v=[],_=await u.array(),h=await d.array();for(let y=0;y<m;y++)for(let T=0;T<m;T++)for(let F=0;F<p;F++){let L=De(_[y][T][F][0]);if(!r||L>r){let G=(T+De(h[y][T][F][0]))/m*i,et=(y+De(h[y][T][F][1]))/m*c,it=Math.exp(h[y][T][F][2])*this.config.anchors[F].x/m*i,X=Math.exp(h[y][T][F][3])*this.config.anchors[F].y/m*c,Pt=G-it/2,_t=et-X/2,wt={row:y,col:T,anchor:F},{classScore:Qt,label:ho}=this.withClassScores?await this.extractPredictedClass(l,wt):{classScore:1,label:0};v.push({box:new ee(Pt,_t,Pt+it,_t+X),score:L,classScore:L*Qt,label:ho,...wt})}}return d.dispose(),u.dispose(),l.dispose(),v}async extractPredictedClass(t,e){let{row:r,col:n,anchor:a}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>s[r][n][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},ge=mo;ge.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ve=class extends ge{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ia(o,t=!0){let e=new ve(t);return e.extractWeights(o),e}var gr=class extends lt{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var tt=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var je=b(g());var po=b(g());async function Xt(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>zt(c)?n(c):c.detection),s=r||(t instanceof po.Tensor?await se(t,a):await ae(t,a)),i=await e(s);return s.forEach(c=>c instanceof po.Tensor&&c.dispose()),i}async function ye(o,t,e,r,n){return Xt([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],cn=[117.001,114.697,97.404];var Fe=class extends ge{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new Ut,tinyFaceDetector:new Fe,tinyYolov2:new ve,faceLandmark68Net:new fe,faceLandmark68TinyNet:new dr,faceRecognitionNet:new xe,faceExpressionNet:new cr,ageGenderNet:new pr},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ca=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ma=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),pa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),da=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ua=o=>P.faceExpressionNet.predictExpressions(o),la=o=>P.ageGenderNet.predictAgeAndGender(o),dn=o=>P.ssdMobilenetv1.load(o),fa=o=>P.tinyFaceDetector.load(o),ha=o=>P.tinyYolov2.load(o),xa=o=>P.faceLandmark68Net.load(o),ba=o=>P.faceLandmark68TinyNet.load(o),ga=o=>P.faceRecognitionNet.load(o),va=o=>P.faceExpressionNet.load(o),ya=o=>P.ageGenderNet.load(o),Fa=dn,Ta=mn,Pa=pn;var uo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},_e=class extends uo{async run(){let t=await this.parentTask,e=await Xt(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>mr(r,e[n]))}withAgeAndGender(){return new Te(this,this.input)}},we=class extends uo{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return mr(t,e)}withAgeAndGender(){return new Pe(this,this.input)}},Zt=class extends _e{withAgeAndGender(){return new Jt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Kt=class extends we{withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var lo=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},Te=class extends lo{async run(){let t=await this.parentTask,e=await Xt(t,this.input,async r=>Promise.all(r.map(n=>P.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return hr(xr(r,s,i),a)})}withFaceExpressions(){return new _e(this,this.input)}},Pe=class extends lo{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await ye(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return hr(xr(t,r,n),e)}withFaceExpressions(){return new we(this,this.input)}},Jt=class extends Te{withFaceExpressions(){return new Zt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},qt=class extends Pe{withFaceExpressions(){return new Kt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var vr=class extends tt{constructor(t,e){super();this.parentTask=t;this.input=e}},At=class extends vr{async run(){let t=await this.parentTask;return(await Xt(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>fr(t[n],r))}withFaceExpressions(){return new Zt(this,this.input)}withAgeAndGender(){return new Jt(this,this.input)}},Wt=class extends vr{async run(){let t=await this.parentTask;if(!t)return;let e=await ye(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return fr(t,e)}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}};var yr=class extends tt{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},Fr=class extends yr{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof je.Tensor?await se(this.input,e):await ae(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),t.map((a,s)=>le(a,n[s]))}withFaceExpressions(){return new Zt(this,this.input)}withAgeAndGender(){return new Jt(this,this.input)}withFaceDescriptors(){return new At(this,this.input)}},Tr=class extends yr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof je.Tensor?await se(this.input,[e]):await ae(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof je.Tensor&&a.dispose()),le(t,n)}withFaceExpressions(){return new Kt(this,this.input)}withAgeAndGender(){return new qt(this,this.input)}withFaceDescriptor(){return new Wt(this,this.input)}};var Pr=class extends tt{constructor(t,e=new Z){super();this.input=t;this.options=e}},He=class extends Pr{async run(){let{input:t,options:e}=this,r=e instanceof gr?n=>P.tinyFaceDetector.locateFaces(n,e):e instanceof Z?n=>P.ssdMobilenetv1.locateFaces(n,e):e instanceof lt?n=>P.tinyYolov2.locateFaces(n,e):null;if(!r)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return r(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>$t({},r)))})}withFaceLandmarks(t=!1){return new Fr(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new _e(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Te(this.runAndExtendWithFaceDetections(),this.input)}},_r=class extends Pr{async run(){let t=await new He(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?$t({},e):void 0)})}withFaceLandmarks(t=!1){return new Tr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new we(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Pe(this.runAndExtendWithFaceDetection(),this.input)}};function _a(o,t=new Z){return new _r(o,t)}function wr(o,t=new Z){return new He(o,t)}async function un(o,t){return wr(o,new Z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function wa(o,t={}){return wr(o,new lt(t)).withFaceLandmarks().withFaceDescriptors()}var Da=un;function fo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a**2,0))}var Dr=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof xt)return s;if(s instanceof Float32Array)return new xt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new xt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>fo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Ee(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distance<r.distance?e:r)}findBestMatch(t){let e=this.matchDescriptor(t);return e.distance<this.distanceThreshold?e:new Ee("unknown",e.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>xt.fromJSON(r));return new Dr(e,t.distanceThreshold)}};function Ea(o){let t=new Fe;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(zt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return le($t(o,n),a)}return pt(o)?$t(o,o.detection.forSize(e,r)):o instanceof V||o instanceof M?o.forSize(e,r):o}var Ca=typeof process!="undefined",Na=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Ia={faceapi:No,node:Ca,browser:Na};
 //# sourceMappingURL=face-api.node.js.map
diff --git a/dist/tfjs.esm.js b/dist/tfjs.esm.js
index 8ec5407..c05a31c 100644
--- a/dist/tfjs.esm.js
+++ b/dist/tfjs.esm.js
@@ -5,43 +5,43 @@
   author: <https://github.com/vladmandic>'
   */
 
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a=k(r,"x","batchNorm"),i=k(e,"mean","batchNorm"),l=k(t,"variance","batchNorm"),u;n!=null&&(u=k(n,"scale","batchNorm"));let c;return o!=null&&(c=k(o,"offset","batchNorm")),T(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),T(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),T(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&T(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&T(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),zn(a,i,l,c,u,s)}var m_=S({batchNorm4d_:bG});function _G(r,e,t){let o=k(r,"x","bincount"),n=k(e,"weights","bincount");T(o.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${o.dtype}`),T(t>=0,()=>`size must be non-negative, but got 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TU({x:r,filter:e,strides:t,pad:o,dataFormat:n="NHWC",dilations:s=[1,1],dimRoundingMode:a,bias:i,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(l=l||"linear",Hu(E.state.gradientDepth,l)===!1){let v=jr(r,e,t,o,n,s,a);return i!=null&&(v=Q(v,i)),ju(v,l,u,c)}let p=k(r,"x","conv2d"),m=k(e,"filter","conv2d"),f=p,d=!1;p.rank===3&&(d=!0,f=L(p,[1,p.shape[0],p.shape[1],p.shape[2]])),T(f.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`),T(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),a!=null&&T(st(o),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${o}.`),T(f.shape[3]===m.shape[2],()=>`Error in conv2d: depth of input (${f.shape[3]}) must match input depth for filter ${m.shape[2]}.`),T(yr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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s=n,a=!1;n.rank===3&&(a=!0,s=L(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},l={alignCorners:t,halfPixelCenters:o,size:e},u=E.runKernel(da,i,l);return a?L(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var gg=S({resizeNearestNeighbor_:tj});function rj(r,e,t){T(e%1==0,()=>`bandPart(): numLower must be an integer, got ${e}.`),T(t%1==0,()=>`bandPart(): numUpper must be an integer, got ${t}.`);let o=k(r,"a","bandPart");T(o.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${o.rank}.`);let n=o.shape,[s,a]=o.shape.slice(-2);if(!(e<=s))throw new Error(`bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`);if(!(t<=a))throw new Error(`bandPart(): numUpper (${t}) must not be greater than the number of columns (${a}).`);e<0&&(e=s),t<0&&(t=a);let i=L(Zc(0,s,1,"int32"),[-1,1]),l=Zc(0,a,1,"int32"),u=ue(i,l),c=fr(Oo(u,le(+e,"int32")),to(u,le(-t,"int32"))),p=gt([s,a],o.dtype);return L(Bt(ur(L(o,[-1,s,a])).map(m=>Dt(c,m,p))),n)}var c0=S({bandPart_:rj});function oj(r){let e;if(Array.isArray(r)){e=!1,T(r!=null&&r.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let n=r[0].shape[0];for(let s=1;s<r.length;++s)T(r[s].shape[0]===n,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${n})`)}else e=!0,r=lr(r,r.shape[0],0).map(n=>wo(n,[0]));T(r.length<=r[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);let t=[],o=r;for(let n=0;n<r.length;++n)t.push(E.tidy(()=>{let s=o[n];if(n>0)for(let a=0;a<n;++a){let i=O(ye(O(t[a],s)),t[a]);s=ue(s,i)}return de(s,Gu(s,"euclidean"))}));return e?Bt(t,0):t}var p0=S({gramSchmidt_:oj});function nj(r,e=!1){if(T(r.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`),r.rank===2)return m0(r,e);{let 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gj={fft:Ra,ifft:zi,rfft:Fa,irfft:zu},xj={hammingWindow:ZN,hannWindow:fg,frame:dg,stft:JN},Ds={flipLeftRight:e0,resizeNearestNeighbor:gg,resizeBilinear:hg,rotateWithOffset:t0,cropAndResize:QN,nonMaxSuppression:r0,nonMaxSuppressionAsync:s0,nonMaxSuppressionWithScore:i0,nonMaxSuppressionWithScoreAsync:a0,nonMaxSuppressionPadded:l0,nonMaxSuppressionPaddedAsync:u0},H_={bandPart:c0,gramSchmidt:p0,qr:f0},yj={absoluteDifference:d0,computeWeightedLoss:Nr,cosineDistance:h0,hingeLoss:g0,huberLoss:x0,logLoss:y0,meanSquaredError:b0,sigmoidCrossEntropy:_0,softmaxCrossEntropy:w0};var Fr=class extends tg{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Te(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return ig(e,t)}dispose(){this.iterations_!=null&&Te(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:le(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Fr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var tp=class extends Fr{constructor(e,t,o=null){super();this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=E.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let 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setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};tp.className="Adadelta";eo(tp);var rp=class extends Fr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=E.registeredVariables[o];if(this.accumulatedGrads[n]==null){let l=!1;this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:V(()=>Na(s.shape,this.initialAccumulatorValue).variable(l))}}let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;V(()=>{let 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Fr{constructor(e,t,o,n=null){super();this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],V(()=>{this.accBeta1=le(t).variable(),this.accBeta2=le(o).variable()}),n==null&&(this.epsilon=E.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);V(()=>{let o=ue(1,this.accBeta1),n=ue(1,this.accBeta2);t.forEach((s,a)=>{let i=E.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:V(()=>Ie(i).variable(l))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:V(()=>Ie(i).variable(l))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedSecondMoment[a].variable,m=Q(O(c,this.beta1),O(u,1-this.beta1)),f=Q(O(p,this.beta2),O(Pe(u),1-this.beta2)),d=de(m,o),h=de(f,n);c.assign(m),p.assign(f);let g=Q(O(de(d,Q(xt(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(O(this.accBeta1,this.beta1)),this.accBeta2.assign(O(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Te(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),V(()=>{this.accBeta1.assign(Rr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Rr(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};op.className="Adam";eo(op);var np=class extends Fr{constructor(e,t,o,n=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],V(()=>{this.iteration=le(0).variable(),this.accBeta1=le(t).variable()}),n==null&&(this.epsilon=E.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);V(()=>{let o=ue(1,this.accBeta1),n=de(-this.learningRate,Q(O(this.iteration,this.decay),1));t.forEach((s,a)=>{let 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n=k(r,"labels","absoluteDifference"),s=k(e,"predictions","absoluteDifference"),a=null;t!=null&&(a=k(t,"weights","absoluteDifference")),Nt(n.shape,s.shape,"Error in absoluteDifference: ");let i=Tt(ue(n,s));return Nr(i,a,o)}var x0=S({absoluteDifference_:pj});function mj(r,e,t,o,n=Gt.SUM_BY_NONZERO_WEIGHTS){let s=k(r,"labels","cosineDistance"),a=k(e,"predictions","cosineDistance"),i=null;o!=null&&(i=k(o,"weights","cosineDistance")),Nt(s.shape,a.shape,"Error in cosineDistance: ");let l=le(1),u=ue(l,ye(O(s,a),t,!0));return Nr(u,i,n)}var y0=S({cosineDistance_:mj});function fj(r,e,t,o=Gt.SUM_BY_NONZERO_WEIGHTS){let n=k(r,"labels","hingeLoss"),s=k(e,"predictions","hingeLoss"),a=null;t!=null&&(a=k(t,"weights","hingeLoss")),Nt(n.shape,s.shape,"Error in hingeLoss: ");let i=le(1);n=ue(O(le(2),n),i);let l=Ir(ue(i,O(n,s)));return Nr(l,a,o)}var b0=S({hingeLoss_:fj});function dj(r,e,t,o=1,n=Gt.SUM_BY_NONZERO_WEIGHTS){let s=k(r,"labels","huberLoss"),a=k(e,"predictions","huberLoss"),i=null;t!=null&&(i=k(t,"weights","huberLoss")),Nt(s.shape,a.shape,"Error in huberLoss: ");let l=le(o),u=Tt(ue(a,s)),c=As(u,l),p=ue(u,c),m=Q(O(le(.5),Pe(c)),O(l,p));return Nr(m,i,n)}var _0=S({huberLoss_:dj});function hj(r,e,t,o=1e-7,n=Gt.SUM_BY_NONZERO_WEIGHTS){let s=k(r,"labels","logLoss"),a=k(e,"predictions","logLoss"),i=null;t!=null&&(i=k(t,"weights","logLoss")),Nt(s.shape,a.shape,"Error in logLoss: ");let l=le(1),u=le(o),c=je(O(s,ir(Q(a,u)))),p=O(ue(l,s),ir(Q(ue(l,a),u))),m=ue(c,p);return Nr(m,i,n)}var w0=S({logLoss_:hj});function gj(r,e,t,o=Gt.SUM_BY_NONZERO_WEIGHTS){let n=k(r,"labels","meanSquaredError"),s=k(e,"predictions","meanSquaredError"),a=null;t!=null&&(a=k(t,"weights","meanSquaredError")),Nt(n.shape,s.shape,"Error in meanSquaredError: ");let i=Vu(n,s);return Nr(i,a,o)}var k0=S({meanSquaredError_:gj});function xj(r,e){let t=k(r,"labels","sigmoidCrossEntropyWithLogits"),o=k(e,"logits","sigmoidCrossEntropyWithLogits");Nt(t.shape,o.shape,"Error in sigmoidCrossEntropyWithLogits: ");let n=Ir(o),s=O(o,t),a=Au(Xt(je(Tt(o))));return Q(ue(n,s),a)}function yj(r,e,t,o=0,n=Gt.SUM_BY_NONZERO_WEIGHTS){let s=k(r,"multiClassLabels","sigmoidCrossEntropy"),a=k(e,"logits","sigmoidCrossEntropy"),i=null;if(t!=null&&(i=k(t,"weights","sigmoidCrossEntropy")),Nt(s.shape,a.shape,"Error in sigmoidCrossEntropy: "),o>0){let u=le(o),c=le(1),p=le(.5);s=Q(O(s,ue(c,u)),O(p,u))}let l=xj(s,a);return Nr(l,i,n)}var v0=S({sigmoidCrossEntropy_:yj});function bj(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t}`);return Hr((n,s,a)=>{let l=Um(s,[t],!0),u=ue(oe(s,"float32"),l);a([n,u]);let c=je(O(u,n));return{value:ye(c,[t]),gradFunc:(f,d)=>{let[h,g]=d,x=Vn(f.shape,[t]);return[O(L(f,x),ue(oe(h,"float32"),Xt(g))),O(L(f,x),ue(Xt(g),oe(h,"float32")))]}}})(r,e)}function _j(r,e,t,o=0,n=Gt.SUM_BY_NONZERO_WEIGHTS){let s=k(r,"onehotLabels","softmaxCrossEntropy"),a=k(e,"logits","softmaxCrossEntropy"),i=null;if(t!=null&&(i=k(t,"weights","softmaxCrossEntropy")),Nt(s.shape,a.shape,"Error in softmaxCrossEntropy: "),o>0){let u=le(o),c=le(1),p=le(s.shape[1]);s=Q(O(s,ue(c,u)),de(u,p))}let l=bj(s,a);return Nr(l,i,n)}var C0=S({softmaxCrossEntropy_:_j});var wj={fft:Ra,ifft:zi,rfft:Fa,irfft:Bu},kj={hammingWindow:e0,hannWindow:yg,frame:bg,stft:t0},Rs={flipLeftRight:o0,resizeNearestNeighbor:wg,resizeBilinear:_g,rotateWithOffset:n0,cropAndResize:r0,nonMaxSuppression:s0,nonMaxSuppressionAsync:l0,nonMaxSuppressionWithScore:u0,nonMaxSuppressionWithScoreAsync:c0,nonMaxSuppressionPadded:p0,nonMaxSuppressionPaddedAsync:m0},Z_={bandPart:f0,gramSchmidt:d0,qr:g0},vj={absoluteDifference:x0,computeWeightedLoss:Nr,cosineDistance:y0,hingeLoss:b0,huberLoss:_0,logLoss:w0,meanSquaredError:k0,sigmoidCrossEntropy:v0,softmaxCrossEntropy:C0};var Fr=class extends ig{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Te(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return pg(e,t)}dispose(){this.iterations_!=null&&Te(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:le(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Fr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var rp=class extends Fr{constructor(e,t,o=null){super();this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=E.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=E.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:B(()=>Ie(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:B(()=>Ie(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let l=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;B(()=>{let c=Q(O(l,this.rho),O(Pe(i),1-this.rho)),p=O(de(xt(Q(u,this.epsilon)),xt(Q(l,this.epsilon))),i),m=Q(O(u,this.rho),O(Pe(p),1-this.rho));l.assign(c),u.assign(m);let f=Q(O(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Te(this.accumulatedGrads.map(e=>e.variable)),Te(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};rp.className="Adadelta";eo(rp);var op=class extends Fr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=E.registeredVariables[o];if(this.accumulatedGrads[n]==null){let l=!1;this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:B(()=>Na(s.shape,this.initialAccumulatorValue).variable(l))}}let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;B(()=>{let l=Q(i,Pe(a));i.assign(l);let u=Q(O(de(a,xt(Q(l,E.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Te(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};op.className="Adagrad";eo(op);var np=class extends Fr{constructor(e,t,o,n=null){super();this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=le(t).variable(),this.accBeta2=le(o).variable()}),n==null&&(this.epsilon=E.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);B(()=>{let o=ue(1,this.accBeta1),n=ue(1,this.accBeta2);t.forEach((s,a)=>{let i=E.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:B(()=>Ie(i).variable(l))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:B(()=>Ie(i).variable(l))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedSecondMoment[a].variable,m=Q(O(c,this.beta1),O(u,1-this.beta1)),f=Q(O(p,this.beta2),O(Pe(u),1-this.beta2)),d=de(m,o),h=de(f,n);c.assign(m),p.assign(f);let g=Q(O(de(d,Q(xt(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(O(this.accBeta1,this.beta1)),this.accBeta2.assign(O(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Te(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),B(()=>{this.accBeta1.assign(Rr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Rr(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};np.className="Adam";eo(np);var sp=class extends Fr{constructor(e,t,o,n=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=le(0).variable(),this.accBeta1=le(t).variable()}),n==null&&(this.epsilon=E.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);B(()=>{let o=ue(1,this.accBeta1),n=de(-this.learningRate,Q(O(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=E.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ie(i).variable(l)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ie(i).variable(l)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedWeightedInfNorm[a].variable,m=Q(O(c,this.beta1),O(u,1-this.beta1)),f=O(p,this.beta2),d=Tt(u),h=qr(f,d);c.assign(m),p.assign(h);let g=Q(O(de(n,o),de(m,Q(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Q(this.iteration,1)),this.accBeta1.assign(O(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Te(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};sp.className="Adamax";eo(sp);var ll=class extends Fr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=E.registeredVariables[o];B(()=>{let i=Q(O(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Dt(le(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};ll.className="SGD";eo(ll);var ip=class extends ll{constructor(e,t,o=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=le(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=E.registeredVariables[o];if(this.accumulations[n]==null){let l=!1;this.accumulations[n]={originalName:`${o}/momentum`,variable:B(()=>Ie(s).variable(l))}}let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&B(()=>{let l,u=Q(O(this.m,a),i);this.useNesterov?l=Q(O(this.c,Q(i,O(u,this.m))),s):l=Q(O(this.c,u),s),a.assign(u),s.assign(l)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Te(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};ip.className="Momentum";eo(ip);var ap=class extends Fr{constructor(e,t=.9,o=0,n=null,s=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=E.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let 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Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let _=b.sourceLayer,w=b.nodeIndex,v=b.tensorIndex;this.outputLayers.push(_),this.outputLayersNodeIndices.push(w),this.outputLayersTensorIndices.push(v)}for(let b of this.inputs){let _=b.sourceLayer,w=b.nodeIndex,v=b.tensorIndex;Po(w===0,"input layer has >1 nodes"),Po(v===0,"input layer has >1 tensors"),this.inputLayers.push(_),this.inputLayersNodeIndices.push(w),this.inputLayersTensorIndices.push(v)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let _=this.inputLayers[b];if(!(_ instanceof Wi))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${_.getClassName()}.`);this.inputNames.push(_.name),this.feedInputShapes.push(_.batchInputShape),this.feedInputNames.push(_.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},o={},n={},s={},a={},i=[],l=(b,_,w,v,$,A)=>{(v==null||$==null||A==null)&&(v=b.sourceLayer,$=b.nodeIndex,A=b.tensorIndex);let R=v.inboundNodes[$];if(w.indexOf(R)!==-1)throw new Or(`The tensor ${b.name} at layer "${v.name}" is part of a cycle.`);if(_.indexOf(R)!==-1)return;this.containerNodes.add(Mo.nodeKey(v,$)),v.id in a||(a[v.id]=Object.keys(a).length),w.indexOf(R)===-1&&w.push(R);let M=R.inboundLayers.length;for(let z=0;z<M;z++){let W=R.inputTensors[z],U=R.inboundLayers[z],q=R.nodeIndices[z],Z=R.tensorIndices[z];l(W,_,w,U,q,Z)}for(_.push(R);w.indexOf(R)>=0;)w.splice(w.indexOf(R),1);i.push(R)},u=[],c=[];for(let b of this.outputs)l(b,u,c);let p=i.slice().reverse();for(let b of p){o[b.id]=b,b.id in t||(t[b.id]=0);let _=t[b.id],w=n[b.outboundLayer.id]==null?0:n[b.outboundLayer.id];_=Math.max(_,w),n[b.outboundLayer.id]=_,s[b.outboundLayer.id]=b.outboundLayer,t[b.id]=_;for(let v=0;v<b.inboundLayers.length;v++){let $=b.inboundLayers[v],A=b.nodeIndices[v],R=$.inboundNodes[A],M=t[R.id]==null?0:t[R.id];t[R.id]=Math.max(_+1,M),o[R.id]=R}}let m={};for(let b in t){let _=t[b];_ in m||(m[_]=[]),m[_].push(o[b])}let f={};for(let b in n){let _=n[b];_ in f||(f[_]=[]),f[_].push(s[b])}let d=Object.keys(f).map(b=>parseInt(b,10)).sort(lf);this.layers=[];for(let b of d){let _=f[b];_.sort((w,v)=>{let $=a[w.id],A=a[v.id];return $<A?-1:$>A?1:0});for(let w of _)w instanceof Mo&&this.internalContainerRefs.push(w),this.layers.push(w)}this.layersByDepth=f,d=Object.keys(m).map(b=>parseInt(b,10)).sort(lf);let h=this.inputs.slice(),g=[];for(let b of d)for(let _ of m[b]){let w=_.outboundLayer;if(w!=null){for(let v of _.inputTensors)if(h.indexOf(v)===-1)throw new Or(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${w.name}". The following previous layers were accessed without issue: ${g}`);for(let v of _.outputTensors)h.push(v);g.push(w.name)}}this.nodesByDepth=m;let x=this.layers.map(b=>b.name);for(let b of x){let _=x.filter(w=>w===b).length;if(_!==1)throw new Or(`The name "${b}" is used ${_} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(x))}this.outboundNodes=[],this.inboundNodes=[],new ml({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(o=>o.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new B("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let o of this.layers)t.push(...o.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let o={},n=0;for(let a of this.layers)for(let i of a.weights){if(o[i.originalName]!=null)throw new B(`Duplicate weight name: ${i.originalName}`);o[i.originalName]=i,n++}let s=[];for(let a in e){let i=a;if(o[a]==null){let l=a.split("/");i=l.slice(0,-2).concat([l[l.length-1]]).join("/")}if(o[i]!=null)s.push([o[i],e[a]]);else if(t)throw new B(`Provided weight data has no target variable: ${a}`);delete o[i]}if(t){let a=[];for(let i in o)a.push(i);if(a.length>0)throw new B(`${a.length} of ${n} weights are not set: ${a}`)}_p(s)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${hl}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let o=Gg(this.updatedConfig());return t?JSON.stringify(o):o}call(e,t){return V(()=>{e=yt(e);let o=new Fs;for(let n=0;n<this.inputs.length;++n)o.add(this.inputs[n],e[n]);return Ju(this.outputs,o,t)})}computeMask(e,t){return V(()=>{e=yt(e);let o;return t==null?o=Vn(null,e.length):o=yt(t),this.runInternalGraph(e,o)[1]})}computeOutputShape(e){let t=yp(e);if(t.length!==this.inputLayers.length)throw new B(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let o={};for(let i=0;i<t.length;i++){let l=this.inputLayers[i],u=t[i],c=l.name+"_0_0";o[c]=u}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(lf);if(n.length>1)for(let i of n){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer;if(this.inputLayers.map(h=>h.id).indexOf(c.id)!==-1)continue;let p=[];for(let h=0;h<u.inboundLayers.length;h++){let g=u.inboundLayers[h],x=u.nodeIndices[h],b=u.tensorIndices[h],_=`${g.name}_${x}_${b}`,w=o[_];p.push(w)}let m=c.computeOutputShape(dr(p)),f=yp(m),d=c.inboundNodes.indexOf(u);for(let h=0;h<f.length;h++){let g=`${c.name}_${d}_${h}`;o[g]=f[h]}}}let s=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let l=this.outputLayers[i],u=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],p=`${l.name}_${u}_${c}`;a.push(p)}for(let i=0;i<a.length;i++){let l=a[i];Po(l in o),s.push(o[l])}return dr(s)}runInternalGraph(e,t){t==null&&(t=Vn(null,e.length));let o={};for(let l=0;l<this.inputs.length;++l){let u=this.inputs[l],c=e[l],p=t[l];o[u.id]=[c,p]}let n=Object.keys(this.nodesByDepth).map(l=>parseInt(l,10)).sort(lf);for(let l of n){let u=this.nodesByDepth[l];for(let c of u){let p=c.outboundLayer,m=c.inputTensors,f=c.outputTensors,d=new Array;for(let h of m)h.id in o&&d.push(o[h.id]);if(d.length===m.length){let h={},g,x,b,_;if(c.callArgs!=null&&(h=c.callArgs),d.length===1){let[w,v]=d[0];h.mask==null&&(h.mask=v),b=yt(p.call(w,h)),_=yt(p.computeMask(w,v)),g=[w],x=[v]}else g=d.map(w=>w[0]),x=d.map(w=>w[1]),h.mask==null&&(h.mask=x),b=yt(p.call(g,h)),_=yt(p.computeMask(g,x));if(p.activityRegularizer)throw new Ne("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let w=0;w<f.length;++w){let v=f[w],$=b[w],A=_[w];o[v.id]=[$,A]}}}}let s=[],a=[],i=[];for(let l of this.outputs){Po(l.id in o,`Could not compute output ${l.name} : ${l.id}`);let[u,c]=o[l.id];i.push(u.shape),s.push(u),a.push(c)}return[s,a,i]}buildNodeConversionMap(e){let t={},o;for(let n of this.layers){o=n instanceof Mo?1:0;for(let s=0;s<n.inboundNodes.length;s++){let a=Mo.nodeKey(n,s);this.containerNodes.has(a)&&(t[a]=o,o+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new B(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new B("Provide either a layer name or layer index");for(let o of this.layers)if(o.name===e)return o;throw new B(`No such layer: ${e}`)}calculateLosses(){return V(()=>{let e=[];for(let t of this.layers)for(let o=0;o<t.inboundNodes.length;++o){let n=Mo.nodeKey(t,o);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),o=[];for(let a of this.layers){let i=a.getClassName(),l=a.getConfig(),u=[];for(let p=0;p<a.inboundNodes.length;p++){let m=a.inboundNodes[p],f=Mo.nodeKey(a,p),d={};if(this.containerNodes.has(f)){if(m.callArgs)try{JSON.stringify(m.callArgs),d=m.callArgs}catch(h){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${m.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),d={}}if(m.inboundLayers.length>0){let h=[];for(let g=0;g<m.inboundLayers.length;g++){let x=m.inboundLayers[g],b=m.nodeIndices[g],_=m.tensorIndices[g],w=Mo.nodeKey(x,b),v=t[w];v==null&&(v=0),h.push([x.name,v,_,d])}u.push(h)}}}let c={};c.name=a.name,c.className=i,c.config=l,c.inboundNodes=u,o.push(c)}e.layers=o;let n=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],l=this.inputLayersNodeIndices[a],u=Mo.nodeKey(i,l);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let p=this.inputLayersTensorIndices[a];n.push([i.name,c,p])}e.inputLayers=n;let s=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],l=this.outputLayersNodeIndices[a],u=Mo.nodeKey(i,l);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let p=this.outputLayersTensorIndices[a];s.push([i.name,c,p])}return e.outputLayers=s,e}static fromConfig(e,t,o={},n=!1){let s={},a={};function i(g,x){g.name in a?a[g.name].push(x):a[g.name]=[x]}function l(g,x){let b=[],_;for(let w of x){let v=w[0],$=w[1],A=w[2];if(_=w[3]==null?{}:w[3],!(v in s)){i(g,x);return}let R=s[v];if(R.inboundNodes.length<=$){i(g,x);return}let M=R.inboundNodes[$];b.push(M.outputTensors[A])}b.length>0&&g.apply(dr(b),_)}function u(g){let x=g.name,b=Yr(g,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(n),s[x]=b,g.inboundNodes.forEach(w=>{if(!(w instanceof Array))throw new B(`Corrupted configuration, expected array for nodeData: ${w}`);i(b,w)})}let c=t.name,p=t.layers;for(let g of p)u(g);for(;!yT(a);)for(let g of p){let x=s[g.name];if(x.name in a){let b=a[x.name];delete a[x.name];for(let _ of b)l(x,_)}}let m=[],f=[],d=t.inputLayers;for(let g of d){let x=g[0],b=g[1],_=g[2];Po(x in s);let v=s[x].inboundNodes[b].outputTensors;m.push(v[_])}let h=t.outputLayers;for(let g of h){let x=g[0],b=g[1],_=g[2];Po(x in s);let v=s[x].inboundNodes[b].outputTensors;f.push(v[_])}return new e({inputs:m,outputs:f,name:c})}get stateful(){if(this._stateful)throw new B("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){V(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function lq(r,e,t){let o=e.length;if(r==null||Array.isArray(r)&&r.length===0)return e.map(n=>null);if(o===1)return Array.isArray(r)&&r.length===1?r:typeof r=="object"&&e[0]in r?[r[e[0]]]:[r];if(Array.isArray(r)){if(r.length!==o)throw new Error(`Provided ${t} is an array of ${r.length} element(s), but the model has ${o} outputs. Make sure a set of weights is provided for each model output.`);return r}else if(typeof r=="object"&&Object.keys(r).length>0&&typeof r[Object.keys(r)[0]]=="object"){let n=[];return e.forEach(s=>{s in r?n.push(r[s]):n.push(null)}),n}else throw new Error(`The model has multiple (${o}) outputs, so ${t} must be either an array with ${o} elements or an object with ${e} keys. Provided ${t} not understood: ${JSON.stringify(r)}`)}function Wg(r,e){return lq(r,e,"classWeight")}async function Ug(r,e,t,o){if(e!=null||o!=null)throw new Error("Support sampleWeight is not implemented yet");if(t!=null){let n=V(()=>{if(r.shape.length===1)return r.clone();if(r.shape.length===2)if(r.shape[1]>1){let i=1;return r.argMax(i)}else{if(r.shape[1]===1)return r.reshape([r.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${r.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${r.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await n.data());Te(n);let a=[];return s.forEach(i=>{if(t[i]==null)throw new Error(`classWeight must contain all classes in the training data. 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(Expected output keys: ${JSON.stringify(r.outputNames)})`);for(let l=0;l<s.length;l++)y.assert(s[l].shape[0]===i,()=>`Batch size mismatch: input ${r.inputNames[l]} has ${s[l].shape[0]}; expected  ${i} based on input ${r.inputNames[0]}.`);for(let l=0;l<a.length;l++)y.assert(a[l].shape[0]===i,()=>`Batch size mismatch: output ${r.outputNames[l]} has ${a[l].shape[0]}; expected  ${i} based on input ${r.inputNames[0]}.`);return{xs:s,ys:a}}function r1(r,e,t){if(t instanceof Ve)return[t];if(Array.isArray(t))return y.assert(t.length===e.length,()=>`Received an array of ${t.length} Tensors, but expected ${e.length} to match the ${r} keys ${e}.`),t;{let o=[];for(let n of e){if(t[n]==null)throw new B(`The feature data generated by the dataset lacks the required ${r} key '${n}'.`);o.push(t[n])}return o}}function cq(r){if(r.length===3)throw new Ne("Validation with sample weights is not implemented yet.");return{xs:r[0],ys:r[1]}}async function s1(r,e,t){let o=t.batchesPerEpoch!=null;if(y.assert(r.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),y.assert(t!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),y.assert(t.epochs!=null&&t.epochs>0&&Number.isInteger(t.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${t.epochs}`),y.assert(!o||t.batchesPerEpoch>0&&Number.isInteger(t.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${t.batchesPerEpoch}`),y.assert(t.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),r.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");r.isTraining=!0;try{let n=t.validationData!=null,s,a;if(n)if(n1(t.validationData))y.assert(t.validationBatches==null||t.validationBatches>0&&Number.isInteger(t.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${t.validationBatches}`);else{let g=cq(t.validationData);s=g.xs,a=g.ys}let i=r.makeTrainFunction(),l=r.getDedupedMetricsNames(),u;n?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let c=Og(t.callbacks,t.yieldEvery),p=t.verbose==null?1:t.verbose,{callbackList:m,history:f}=Pg(c,p,t.epochs,null,null,pq(e,t),null,n,u);m.setModel(r),r.history=f,await m.onTrainBegin(),r.stopTraining_=!1;let d=t.initialEpoch==null?0:t.initialEpoch,h=await e.iterator();for(;d<t.epochs;){let g={};await m.onEpochBegin(d);let x=0,b=0;for(o||(h=await e.iterator());o?x<t.batchesPerEpoch:!0;){let _=await h.next();if(o&&_.done){console.warn(`You provided \`batchesPerEpoch\` as ${t.batchesPerEpoch}, but your dataset iterator ran out of data after ${x} batches; interrupting training. 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e.metrics)s[a]=Pa(e.metrics[a])}this.compile({loss:n,metrics:s,optimizer:o})}async save(e,t){if(typeof e=="string"){let u=vr.getSaveHandlers(e);if(u.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new B(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let o=await vr.encodeWeights(this.getNamedWeights(t)),n=!1,s=null,i={modelTopology:this.toJSON(s,n),format:yq,generatedBy:`TensorFlow.js tfjs-layers v${hl}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u="optimizer",{data:c,specs:p}=await vr.encodeWeights(await this.optimizer.getWeights(),u);o.specs.push(...p),o.data=vr.concatenateArrayBuffers([o.data,c])}if(this.userDefinedMetadata!=null){let u=!0;dw(this.userDefinedMetadata,this.name,u),i.userDefinedMetadata=this.userDefinedMetadata}return i.weightData=o.data,i.weightSpecs=o.specs,e.save(i)}setUserDefinedMetadata(e){dw(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};vo.className="Model";J.registerClass(vo);var yw=class extends vo{};yw.className="Functional";J.registerClass(yw);async function p1(r,e){"modelTopology"in r||(r={modelTopology:r}),r=r;let t=r.modelTopology;t.model_config!=null&&(t=t.model_config);let o=Zu(t),n=Yr(o,e);if(r.weightsManifest!=null){let s=await vr.loadWeights(r.weightsManifest,r.pathPrefix,n.weights.map(i=>i.originalName)),a={};for(let i of n.weights)a[i.originalName]=s[i.originalName];n.loadWeights(a),Te(s)}return n}async function m1(r,e){if(e==null&&(e={}),typeof r=="string"){let t=vr.getLoadHandlers(r,e);if(t.length===0)t.push(vr.browserHTTPRequest(r,e));else if(t.length>1)throw new B(`Found more than one (${t.length}) load handlers for URL 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this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,o={},n=!1){let s,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new B("Legacy serialization format not supported yet.");s=t}else y.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."),s=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Hi))throw new Ne(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let l of s){let c=Yr(l,void 0,n);n&&c.setFastWeightInitDuringBuild(!0),i.add(c)}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 this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let o={};o.className=t.getClassName(),o.config=t.getConfig(),e.push(o)}return{name:this.name,layers:e}}};Hi.className="Sequential";J.registerClass(Hi);function f1(r){return new vo(r)}function d1(r){return new Hi(r)}function h1(r,e){return e==null&&(e={}),m1(r,e)}function Kg(r){return Ag(r)}function g1(r,e){no.registerCallbackConstructor(r,e)}var so=class extends J.Serializable{getConfig(){return{}}},bw=class extends so{apply(e,t=1){return PT(e,t)}};bw.className="elu";J.registerClass(bw);var _w=class extends so{apply(e){return Pu(e)}};_w.className="selu";J.registerClass(_w);var ww=class extends so{apply(e){return Ir(e)}};ww.className="relu";J.registerClass(ww);var kw=class extends so{apply(e){return V(()=>Ts(6,Ir(e)))}};kw.className="relu6";J.registerClass(kw);var vw=class extends so{apply(e){return e}};vw.className="linear";J.registerClass(vw);var Cw=class extends so{apply(e){return 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bt(r){if(r==null)return null;if(typeof r=="string"){let t={className:r in b1?b1[r]:r,config:{}};return _1(t)}else return r instanceof Rw?r:_1(r)}var $f=class extends Me{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let o=Ir(e);return this.maxValue!=null&&(o=nr(o,0,this.maxValue)),o}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};$f.className="ReLU";J.registerClass($f);var Rf=class extends Me{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let o=Oe(e);return Sa(o,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Rf.className="LeakyReLU";J.registerClass(Rf);var Ff=class extends 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Received: ${r.length} elements.`);for(let o=0;o<e;++o){let n=r[o];if(!DT(n))throw new B(`The ${t} argument must be an integer or tuple of ${e} integers. 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instead`);if(s==="channelsFirst"&&(r=Ue(r,[0,2,1])),n==="causal")throw new Ne("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=vu(r,e,o,n==="same"?"same":"valid","NWC",a);return t!=null&&(i=ro(i,t)),i})}function w1(r,e,t,o=[1,1],n="valid",s,a,i=null){return V(()=>{if(s==null&&(s=Kr()),$t(s),r.rank!==3&&r.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let l=zf(r,s);if(n==="causal")throw new Ne("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=zn.conv2d({x:l,filter:e,strides:o,pad:n==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:t,activation:i}),s==="channelsFirst"&&(l=Ue(l,[0,3,1,2])),l})}function kq(r,e,t,o=[1,1,1],n="valid",s,a){return V(()=>{if(s==null&&(s=Kr()),$t(s),r.rank!==4&&r.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let i=Fw(r,s);if(n==="causal")throw new Ne("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Rm(i,e,o,n==="same"?"same":"valid","NDHWC",a),t!=null&&(i=ro(i,t)),s==="channelsFirst"&&(i=Ue(i,[0,4,1,2,3])),i})}var Ip=class extends Me{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Ip.verifyArgs(t),this.rank=e,Wt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ne(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=xl(t.kernelSize,e,"kernelSize"),this.strides=xl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Xr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,$t(this.dataFormat),this.activation=Ps(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=dt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=bt(t.biasRegularizer),this.activityRegularizer=bt(t.activityRegularizer),this.dilationRate=xl(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(Po("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!_g(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:Os(this.activation),useBias:this.useBias,biasInitializer:It(this.biasInitializer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},ec=class extends Ip{constructor(e,t){super(e,t);this.kernel=null,ec.verifyArgs(t),this.filters=t.filters,Wt(this.filters,"filters"),this.kernelInitializer=dt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=bt(t.kernelRegularizer)}build(e){e=Qe(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 o=e[t],n=this.kernelSize.concat([o,this.filters]);this.kernel=this.addWeight("kernel",n,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]:o}}],this.built=!0}call(e,t){return V(()=>{e=Oe(e);let o,n=this.bias==null?null:this.bias.read(),s=wg(this.activation.getClassName());if(s!=null&&this.rank===2)o=w1(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)o=wq(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)o=w1(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)o=kq(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ne("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(o=this.activation.apply(o))}return o})}computeOutputShape(e){e=Qe(e);let t=[],o=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<o.length;++s){let a=io(o[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:It(this.kernelInitializer),kernelRegularizer:lt(this.kernelRegularizer),kernelConstraint:Ot(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)}`)}},yl=class extends ec{constructor(e){super(2,e);yl.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!_g(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)}.`)}};yl.className="Conv2D";J.registerClass(yl);var tc=class extends ec{constructor(e){super(3,e);tc.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)}.`)}};tc.className="Conv3D";J.registerClass(tc);var Bf=class extends yl{constructor(e){super(e);if(this.inputSpec=[new Et({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Qe(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let o=e[t],n=this.kernelSize.concat([this.filters,o]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Et({ndim:4,axes:{[t]:o}})],this.built=!0}call(e,t){return V(()=>{let o=Oe(e);if(o.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${o.shape.length}`);let n=o.shape,s=n[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let l=n[a],u=n[i],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=Lf(l,m,c,this.padding),h=Lf(u,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(o=Ue(o,[0,2,3,1]));let x=Cu(o,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Ue(x,[0,3,1,2])),this.bias!=null&&(x=ro(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(e){e=Qe(e);let t=e.slice(),o,n,s;this.dataFormat==="channelsFirst"?(o=1,n=2,s=3):(o=3,n=1,s=2);let a=this.kernelSize[0],i=this.kernelSize[1],l=this.strides[0],u=this.strides[1];return t[o]=this.filters,t[n]=Lf(t[n],l,a,this.padding),t[s]=Lf(t[s],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Bf.className="Conv2DTranspose";J.registerClass(Bf);var Ow=class extends ec{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 ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=dt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=bt(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=dt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=bt(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=Qe(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${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 o=e[t],n=this.kernelSize.concat([o,this.depthMultiplier]),s=[];for(let i=0;i<this.rank;++i)s.push(1);s.push(o*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Et({ndim:this.rank+2,axes:{[t]:o}})],this.built=!0}call(e,t){return V(()=>{e=Oe(e);let o;if(this.rank===1)throw new Ne("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ue(e,[0,2,3,1])),o=Km(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(o=ro(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),this.dataFormat==="channelsFirst"&&(o=Ue(o,[0,3,1,2])),o})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=It(this.depthwiseInitializer),e.pointwiseInitializer=It(this.pointwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.pointwiseRegularizer=lt(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};Ow.className="SeparableConv";var Vf=class extends Ow{constructor(e){super(2,e)}};Vf.className="SeparableConv2D";J.registerClass(Vf);var rc=class extends ec{constructor(e){super(1,e);rc.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"&&!_g(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};rc.className="Conv1D";J.registerClass(rc);var Gf=class extends Me{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return V(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let o=df(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return df(o,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let o=df(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return df(o,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Gf.className="Cropping2D";J.registerClass(Gf);var Wf=class extends Me{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,TT(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],o=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,o]}else{let t=e[1]==null?null:this.size[0]*e[1],o=e[2]==null?null:this.size[1]*e[2];return[e[0],t,o,e[3]]}}call(e,t){return V(()=>{let o=Oe(e),n=o.shape;if(this.dataFormat==="channelsFirst"){o=Ue(o,[0,2,3,1]);let s=this.size[0]*n[2],a=this.size[1]*n[3],i=this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a]);return Ue(i,[0,3,1,2])}else{let s=this.size[0]*n[1],a=this.size[1]*n[2];return this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Wf.className="UpSampling2D";J.registerClass(Wf);function vq(r,e,t=[1,1],o="valid",n,s){return V(()=>{n==null&&(n=Kr()),$t(n);let a=zf(r,n);if(r.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(e.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${e.rank}-D`);return a=Cs(a,e,t,o==="same"?"same":"valid","NHWC",s),n==="channelsFirst"&&(a=Ue(a,[0,3,1,2])),a})}var Uf=class extends Ip{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=dt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=bt(e.depthwiseRegularizer)}build(e){if(e=Qe(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 o=e[t],n=[this.kernelSize[0],this.kernelSize[1],o,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[o*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{e=Oe(e);let o=vq(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(o=ro(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),o})}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=io(t,this.kernelSize[0],this.padding,this.strides[0]),a=io(o,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,s,a]:[e[0],s,a,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=It(this.depthwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};Uf.className="DepthwiseConv2D";J.registerClass(Uf);function Pw(r,e,t,o){if(Array.isArray(r)){if(e!=null||t!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");o!=null&&(t=r.slice(r.length-o,r.length),r=r.slice(0,r.length-o)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function n(s){return s==null||Array.isArray(s)?s:[s]}return e=n(e),t=n(t),{inputs:r,initialState:e,constants:t}}function Mw(r,e,t,o=!1,n,s,a=!1,i=!1){return V(()=>{let l=e.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Pr(2,l));if(e=Ue(e,u),s!=null)throw new Ne("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),n!=null&&(n=n.asType("bool").asType("float32"),n.rank===l-1&&(n=sr(n,-1)),n=Ue(n,u)),o&&(e=qt(e,0),n!=null&&(n=qt(n,0)));let c=[],p,m=t,f=e.shape[0],d=ur(e),h;n!=null&&(h=ur(n));for(let x=0;x<f;++x){let b=d[x],_=V(()=>r(b,m));if(n==null)p=_[0],m=_[1];else{let w=V(()=>{let v=h[x],$=er(v).sub(v),A=_[0].mul(v).add(m[0].mul($)),R=m.map((M,z)=>_[1][z].mul(v).add(M.mul($)));return{output:A,newStates:R}});p=w.output,m=w.newStates}i&&c.push(p)}let g;return i&&(g=Bt(c,1)),[p,g,m]})}var ao=class extends Me{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 Np({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 Et({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 Pr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Eg(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let o=t[0],n;if(this.returnSequences?n=[e[0],e[1],o]:n=[e[0],o],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[n].concat(s)}else return n}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let o=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(s=>null);return[o].concat(n)}else return o})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let o=0;o<e;++o)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 Ne("Constants support is not implemented in RNN yet.");Eg(e)&&(e=e[0]),e=e;let o=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new Et({shape:[o,null,...n]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new Ne("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!y.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))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=a.map(i=>new Et({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new ko("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape[0];if(o==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(n=>gt([o,n])):this.states_=[gt([o,this.cell.stateSize])];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>gt([o,n])):this.states_[0]=gt([o,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()):Te(this.states_);for(let n=0;n<this.states_.length;++n){let s=e[n],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[o,a];if(!y.arraysEqual(s.shape,i))throw new B(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${s.shape}`);this.states_[n]=s}}this.states_=this.states_.map(n=>At(n.clone()))})}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=Pw(e,o,n,this.numConstants);e=s.inputs,o=s.initialState,n=s.constants;let a=[],i=[];if(o!=null){t.initialState=o,a=a.concat(o),this.stateSpec=[];for(let u of o)this.stateSpec.push(new Et({shape:u.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,a=a.concat(n),this.numConstants=n.length),a[0]instanceof Lr){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;e=Oe(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new B(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},u=Mw((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,o,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,n);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(e){return V(()=>{let t=gt(e.shape);return t=ye(t,[1,2]),t=La(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(o=>o>1?Cg(t,[1,o]):t):this.cell.stateSize>1?[Cg(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 o=this.cell.getConfig();return this.getClassName()===ao.className&&(t.cell={className:this.cell.getClassName(),config:o}),Object.assign({},o,e,t)}static fromConfig(e,t,o={}){let n=t.cell,s=Yr(n,o);return new e(Object.assign(t,{cell:s}))}};ao.className="RNN";J.registerClass(ao);var bl=class extends Me{},Sp=class extends bl{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,Wt(this.units,"units"),this.activation=Ps(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=qu([1,Rs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=qu([1,Rs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Qe(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 V(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let o=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ba({ones:()=>er(e),rate:this.dropout,training:n})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ba({ones:()=>er(o),rate:this.recurrentDropout,training:n}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=Hn(O(e,a),this.kernel.read()):s=Hn(e,this.kernel.read()),this.bias!=null&&(s=ro(s,this.bias.read())),i!=null&&(o=O(o,i));let l=Q(s,Hn(o,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Sp.className="SimpleRNNCell";J.registerClass(Sp);var jf=class extends ao{constructor(e){e.cell=new Sp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return new e(t)}};jf.className="SimpleRNN";J.registerClass(jf);var Tp=class extends bl{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,Wt(this.units,"units"),this.activation=Ps(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ps(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=qu([1,Rs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=qu([1,Rs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Qe(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 V(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ba({ones:()=>er(e),rate:this.dropout,training:o,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ba({ones:()=>er(n),rate:this.recurrentDropout,training:o,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,i,l,u;0<this.dropout&&this.dropout<1&&(e=O(e,s[0]));let c=Hn(e,this.kernel.read());this.useBias&&(c=ro(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=O(n,a[0]));let p=this.recurrentKernel.read(),[m,f]=lr(p,[2*this.units,this.units],p.rank-1),d=Hn(n,m),[h,g,x]=lr(c,3,c.rank-1),[b,_]=lr(d,2,d.rank-1);i=this.recurrentActivation.apply(Q(h,b)),l=this.recurrentActivation.apply(Q(g,_));let w=Hn(O(l,n),f);u=this.activation.apply(Q(x,w));let v=Q(O(i,n),O(Q(1,je(i)),u));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),recurrentActivation:Os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Tp.className="GRUCell";J.registerClass(Tp);var Hf=class extends ao{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 Tp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Hf.className="GRU";J.registerClass(Hf);var _l=class extends bl{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,Wt(this.units,"units"),this.activation=Ps(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ps(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(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=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=qu([1,Rs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=qu([1,Rs([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=Qe(e);let o=e[e.length-1];this.kernel=this.addWeight("kernel",[o,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 n;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;n=new(t=class extends oo{apply(l,u){let c=s.apply([a]),p=new Xu().apply([a]),m=s.apply([a*2]);return aw(aw(c,p),m)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return V(()=>{let o=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 n=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ba({ones:()=>er(e),rate:this.dropout,training:o,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ba({ones:()=>er(n),rate:this.recurrentDropout,training:o,count:4}));let a=this.dropoutMask,i=this.recurrentDropoutMask,l,u,c,p;0<this.dropout&&this.dropout<1&&(e=O(e,a[0]));let m=Hn(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=O(n,i[0])),m=Q(m,Hn(n,this.recurrentKernel.read())),this.useBias&&(m=ro(m,this.bias.read()));let[f,d,h,g]=lr(m,4,m.rank-1);l=this.recurrentActivation.apply(f),u=this.recurrentActivation.apply(d),c=Q(O(u,s),O(l,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let x=O(p,this.activation.apply(c));return[x,x,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),recurrentActivation:Os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};_l.className="LSTMCell";J.registerClass(_l);var qf=class extends ao{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 _l(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};qf.className="LSTM";J.registerClass(qf);var Np=class extends bl{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 V(()=>{e=e;let o=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(o.splice(0,i.stateSize.length)):n.push(o.splice(0,1));n.reverse();let s=[],a;for(let i=0;i<this.cells.length;++i){let l=this.cells[i];o=n[i],i===0?a=[e[0]].concat(o):a=[a[0]].concat(o),a=l.call(a,t),s.push(a.slice(1))}o=[];for(let i of s.slice().reverse())o.push(...i);return[a[0]].concat(o)})}build(e){Eg(e)&&(e=e[0]),e=e;let t;this.cells.forEach((o,n)=>{$s(`RNNCell_${n}`,()=>{o.build(e),Array.isArray(o.stateSize)?t=o.stateSize[0]:t=o.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,o={}){let n=[];for(let s of t.cells)n.push(Yr(s,o));return new e({cells:n})}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 o of this.cells)t.push(...o.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return vf(e)}setWeights(e){let t=[];for(let o of this.cells){let n=o.weights.length,s=e.splice(n);for(let a=0;a<o.weights.length;++a)t.push([o.weights[a],s[a]])}_p(t)}};Np.className="StackedRNNCells";J.registerClass(Np);function Ba(r){let{ones:e,rate:t,training:o=!1,count:n=1}=r,s=()=>Ng(e(),t),a=()=>cl(s,e,o);return!n||n<=1?At(a().clone()):Array(n).fill(void 0).map(a).map(l=>At(l.clone()))}var Cq=function(r,e){var t={};for(var o in r)Object.prototype.hasOwnProperty.call(r,o)&&e.indexOf(o)<0&&(t[o]=r[o]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var n=0,o=Object.getOwnPropertySymbols(r);n<o.length;n++)e.indexOf(o[n])<0&&Object.prototype.propertyIsEnumerable.call(r,o[n])&&(t[o[n]]=r[o[n]]);return t};var Lw=class extends ao{constructor(e){if(e.unroll)throw new Ne("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ne("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Et({ndim:5})]}call(e,t){return V(()=>{if(this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}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 V(()=>{let{stateSize:t}=this.cell,o=e.shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)],a=gt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new ko("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)];if(o[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(()=>gt(s)):this.states_=[gt(s)];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>gt(s)):this.states_[0]=gt(s);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()):Te(this.states_);for(let i=0;i<this.states_.length;++i){let l=e[i],u=s;if(!y.arraysEqual(l.shape,u))throw new B(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${l.shape}`);this.states_[i]=l}}this.states_=this.states_.map(i=>At(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:o,kernelSize:n,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=io(u,n[0],s,a[0],i[0]),m=io(c,n[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[o,p,m]:[p,m,o]]}};Lw.className="ConvRNN2D";var Ep=class extends _l{constructor(e){let{filters:t,kernelSize:o,strides:n,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Wt(this.filters,"filters"),this.kernelSize=xl(o,2,"kernelSize"),this.kernelSize.forEach(l=>Wt(l,"kernelSize")),this.strides=xl(n||1,2,"strides"),this.strides.forEach(l=>Wt(l,"strides")),this.padding=s||"valid",Xr(this.padding),this.dataFormat=a||"channelsLast",$t(this.dataFormat),this.dilationRate=xl(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>Wt(l,"dilationRate"))}build(e){var t;e=Qe(e);let o=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[o]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[o]}`);let n=e[o],s=4,a=this.kernelSize.concat([n,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let l;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;l=new(t=class extends oo{apply(m,f){let d=u.apply([c]),h=Cr([c]),g=u.apply([c*2]);return cp([d,h,g])}},t.className="CustomInit",t)}else l=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,l,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return V(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training||!1,n=e[0],s=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ba({ones:()=>er(n),rate:this.dropout,training:o,count:i}));let l=this.dropoutMask,u=(ie,se,pe)=>!se||!se[pe]?ie:O(se[pe],ie),c=u(n,l,0),p=u(n,l,1),m=u(n,l,2),f=u(n,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ba({ones:()=>er(s),rate:this.recurrentDropout,training:o,count:i}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),x=u(s,d,2),b=u(s,d,3),_=3,[w,v,$,A]=lr(this.kernel.read(),i,_),[R,M,z,W]=this.useBias?lr(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,w,R,this.padding),p=this.inputConv(p,v,M,this.padding),m=this.inputConv(m,$,z,this.padding),f=this.inputConv(f,A,W,this.padding);let[U,q,Z,X]=lr(this.recurrentKernel.read(),i,_);h=this.recurrentConv(h,U),g=this.recurrentConv(g,q),x=this.recurrentConv(x,Z),b=this.recurrentConv(b,X);let Y=this.recurrentActivation.apply(Q(c,h)),te=this.recurrentActivation.apply(Q(p,g)),K=Q(O(te,a),O(Y,this.activation.apply(Q(m,x)))),re=O(this.recurrentActivation.apply(Q(f,b)),this.activation.apply(K));return[re,re,K]})}getConfig(){let e=super.getConfig(),{units:t}=e,o=Cq(e,["units"]),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},o,n)}inputConv(e,t,o,n){let s=jr(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return o?ro(s,o,this.dataFormat):s}recurrentConv(e,t){return jr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Ep.className="ConvLSTM2DCell";J.registerClass(Ep);var Kf=class extends Lw{constructor(e){let t=new Ep(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Kf.className="ConvLSTM2D";J.registerClass(Kf);var Ap=class extends Me{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,o=[];for(let n=0;n<this.noiseShape.length;++n)o.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return o}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,s=this.getNoiseShape(o);return cl(()=>Ng(o,this.rate,s,this.seed),()=>o,n)}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()}};Ap.className="Dropout";J.registerClass(Ap);var Xf=class extends Ap{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Xf.className="SpatialDropout1D";J.registerClass(Xf);var Yf=class extends Me{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,Wt(this.units,"units"),this.activation=Ps(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(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=Qe(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=Qe(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e),n=wg(this.activation.getClassName()),s;return n!=null?s=Hn(o,this.kernel.read(),n,this.bias?this.bias.read():null):(s=Hn(o,this.kernel.read()),this.bias!=null&&(s=ro(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Os(this.activation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Yf.className="Dense";J.registerClass(Yf);var Zf=class extends Me{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Qe(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],jn(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e);if(this.dataFormat==="channelsFirst"&&o.rank>1){let n=[0];for(let s=2;s<o.rank;++s)n.push(s);n.push(1),o=o.transpose(n)}return OT(o)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Zf.className="Flatten";J.registerClass(Zf);var Jf=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ps(e.activation)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e);return this.activation.apply(o)})}getConfig(){let e={activation:Os(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Jf.className="Activation";J.registerClass(Jf);var Qf=class extends Me{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 V(()=>(e=Oe(e),RT(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Qf.className="RepeatVector";J.registerClass(Qf);var ed=class extends Me{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 o="Total size of new array must be unchanged.",n=t.slice(),s=1,a=null;for(let l=0;l<n.length;++l){let u=n[l];if(this.isUnknown(u))if(a===null)a=l;else throw new B("Can only specifiy one unknown dimension.");else s*=u}let i=jn(e);if(a!==null){if(s===0||i%s!=0)throw new B(o);n[a]=i/s}else if(i!==s)throw new B(o);return n}computeOutputShape(e){let t=!1;for(let o=0;o<e.length;++o)if(this.isUnknown(e[o])){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 V(()=>{this.invokeCallHook(e,t);let o=Oe(e),n=o.shape,s=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return o.reshape(s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};ed.className="Reshape";J.registerClass(ed);var td=class extends Me{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=Pr(1,e.dims.length+1);if(!y.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 Et({ndim:this.dims.length+1})]}computeOutputShape(e){e=Qe(e);let t=e.slice();return this.dims.forEach((o,n)=>{t[n+1]=e[o]}),t}call(e,t){return Ue(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};td.className="Permute";J.registerClass(td);var rd=class extends Me{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 o=Oe(e),n=-1;return nl(Ln(o,this.maskValue),n)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e),n=-1,s=!0,a=nl(Ln(o,this.maskValue),n,s);return o.mul(a.asType(o.dtype))})}};rd.className="Masking";J.registerClass(rd);var od=class extends Me{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(yt(e.inputLength))}this.inputDim=e.inputDim,Wt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Wt(this.outputDim,"outputDim"),this.embeddingsInitializer=dt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=bt(e.embeddingsRegularizer),this.activityRegularizer=bt(e.activityRegularizer),this.embeddingsConstraint=Pt(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 V(()=>this.maskZero?(e=Oe(e),Ln(e,Ie(e))):null)}computeOutputShape(e){if(e=Qe(e),this.inputLength==null)return[...e,this.outputDim];let t=yt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let o=0;for(let n=0;n<t.length;++n){let s=t[n],a=e[n+1];if(s!=null&&a!=null&&s!==a)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[o]=a),o++}}return[e[0],...t,this.outputDim]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e);return o.dtype!=="int32"&&(o=Ma(o,"int32")),Ig(this.embeddings.read(),o.as1D()).reshape(Qe(this.computeOutputShape(o.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:It(this.embeddingsInitializer),embeddingsRegularizer:lt(this.embeddingsRegularizer),activityRegularizer:lt(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};od.className="Embedding";J.registerClass(od);var wl=class extends Me{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ne}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 o=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let s=e[e.length-t.length+n],a=t[n];if(s==null||a==null||s<0||a<0)o.push(null);else if(s===1)o.push(a);else if(a===1)o.push(s);else{if(s!==a)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));o.push(s)}}return o}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Qe(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 s of e)s!=null&&s[0]!==null&&t.push(s[0]);if(t=Un(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let o=e[0]==null?null:e[0].slice(1);for(let s=1;s<e.length;++s){let a=e[s]==null?null:e[s].slice(1);o=this.computeElementwiseOpOutputShape(o,a)}let n=e.map(s=>s.length);e.indexOf(null)===-1&&Un(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return V(()=>{if(e=e,this.reshapeRequired){let o=[],n=e.map(s=>s.rank);if(n.indexOf(null)===-1){let s=Rs(n);for(let a of e){let i=a.rank;for(let l=0;l<s-i;++l)a=La(a,1);o.push(a)}return this.mergeFunction(o)}else{let s=!1;for(let l of e){let u=l.rank;if(u==null){let c=l.shape,p=c[0],m=c.slice(1).concat([p]),f=l.reshape([p].concat(jn(c.slice(1))));f=Ue(f,[1,0]),f=f.reshape(m),o.push(f),s=!0}else if(u>1){let c=Pr(1,u).concat([0]);o.push(Ue(l,c)),s=!0}else o.push(l)}let a=this.mergeFunction(o),i=a.rank;if(s){if(i==null){let l=a.shape,u=l.length,c=l[u-1],p=[c].concat(l.slice(0,l.length-1));a=Ue(a.reshape([-1,c]),[1,0]).reshape(p)}else if(i>1){let l=[i-1].concat(Pr(0,i-1));a=Ue(a,l)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let s=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let o=[];for(let n of e)n!=null&&n[0]!==null&&o.push(n[0]);return o=Un(o),o.length===1?t=o.concat(t):t=[null].concat(t),t}computeMask(e,t){return V(()=>{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:sr(n,0));let o=t[0];for(let n=1;n<t.length-1;++n)o=fr(o,t[n]);return o})}},nd=class extends wl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=Q(t,e[o]);return t})}};nd.className="Add";J.registerClass(nd);var sd=class extends wl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=O(t,e[o]);return t})}};sd.className="Multiply";J.registerClass(sd);var id=class extends wl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=Q(t,e[o]);return O(1/e.length,t)})}};id.className="Average";J.registerClass(id);var ad=class extends wl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=qr(t,e[o]);return t})}};ad.className="Maximum";J.registerClass(ad);var ld=class extends wl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=Ts(t,e[o]);return t})}};ld.className="Minimum";J.registerClass(ld);var ud=class extends wl{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 n of e)if(n!=null){t=!1;break}if(t)return;let o=[];for(let n=0;n<e.length;++n){let s=e[n].slice();s.splice(this.axis,1);let a=!1;for(let i of o)if(y.arraysEqual(i,s)){a=!0;break}a||o.push(s)}if(o.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 V(()=>cp(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,o=t[0].slice(),n=this.axis<0?o.length+this.axis:this.axis;for(let s of t.slice(1)){if(o[n]==null||s[n]==null){o[n]=null;break}o[n]+=s[n]}return o}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 V(()=>{let o=!0;if(t.forEach(a=>{if(a!=null){o=!1;return}}),o)return null;let n=[];for(let a=0;a<e.length;++a)t[a]==null?n.push(er(e[a]).asType("bool")):t[a].rank<e[a].rank?n.push(sr(t[a],-1)):n.push(t[a]);let s=Je(n,this.axis);return _u(s,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ud.className="Concatenate";J.registerClass(ud);function cd(r,e){for(;r<0;)r+=e;return r}function Iq(r,e,t){if(r.shape.length>3||e.shape.length>3)throw new Ne("batchDot is not implemented for tensors of 4D or higher rank yet");if(y.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`),typeof t=="number"&&(t=[t,t]),r.dtype==="complex64"||e.dtype==="complex64")throw new Ne("batchDot is not implemented for complex64-type Tensors yet.");let o=r.shape.length,n=e.shape.length;t==null&&(t=[o-1,n-2]);let s=t;return V(()=>{let a;if(o>n){a=o-n;let l=[];for(let u=0;u<a;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else if(n>o){a=n-o;let l=[];for(let u=0;u<a;++u)l.push(1);r=r.reshape(r.shape.concat(l))}else a=0;let i;if(r.shape.length===2&&e.shape.length===2)s[0]===s[1]?i=r.mul(e).sum(s[0]):i=r.transpose([1,0]).mul(e).sum(s[1]);else{let l=s[0]!==r.shape.length-1,u=s[1]===e.shape.length-1;i=r.matMul(e,l,u)}if(a>0){let l;o>n?l=o+n-3:l=o-1;let u=[];for(let c=l;c<l+a;++c)u.push(c);i=i.squeeze(u)}return i.shape.length===1&&(i=i.expandDims(1)),i})}var pd=class extends wl{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){y.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],o=e[1];if(t.length>3||o.length>3)throw new Ne("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);if(t[n[0]]!==o[n[1]])throw new B(`Dimension incompatibility: ${t[n[0]]} !== ${o[n[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],o=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((s,a)=>cd(s,e[a].shape.length)):n=[cd(this.axes,t.shape.length),cd(this.axes,o.shape.length)],this.normalize&&(t=Cf(t,n[0]),o=Cf(o,n[1])),Iq(t,o,n)}interpretAxes(e,t){let o;return Array.isArray(this.axes)?o=this.axes:o=[cd(this.axes,e.length),cd(this.axes,t.length)],o}computeOutputShape(e){y.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(),o=e[1].slice();if(t.length>3||o.length>3)throw new Ne("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);t.splice(n[0],1),o.splice(n[1],1),o.splice(0,1);let s=t.concat(o);return s.length===1&&s.push(1),s}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};pd.className="Dot";J.registerClass(pd);var md=class extends Me{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 V(()=>{this.invokeCallHook(e,t);let o=Oe(e);return cl(()=>pp(o.shape,0,this.stddev).add(o),()=>o,t.training||!1)})}};md.className="GaussianNoise";J.registerClass(md);var fd=class extends Me{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 V(()=>{this.invokeCallHook(e,t);let o=Oe(e);return this.rate>0&&this.rate<1?cl(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return o.mul(pp(o.shape,1,s))},()=>o,t.training||!1):o})}};fd.className="GaussianDropout";J.registerClass(fd);var dd=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(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 V(()=>{if(this.rate<1&&this.rate>0){let o=this._getNoiseShape(e);return cl(()=>{let s=Oe(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=to(Es(o),this.rate);u=Ma(u,"float32");let c=((1-this.rate)*(1+this.rate*l**2))**-.5,p=-c*l*this.rate;return s.mul(u).add(u.add(-1).mul(l)).mul(c).add(p)},()=>Oe(e),t.training||!1)}return e})}};dd.className="AlphaDropout";J.registerClass(dd);function hd(r,e,t,o,n,s=.001){let a;if(r.rank===2)a=s_(r,e,t,o,n,s);else if(r.rank===3)a=i_(r,e,t,o,n,s);else if(r.rank===4)a=a_(r,e,t,o,n,s);else throw new Ne(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function Nq(r,e,t,o,n=.001){return V(()=>{let s=Xc(r,o),a=s.mean,i=s.variance;return[hd(r,a,i,t,e,n),a,i]})}function Sq(r,e,t,o,n=.001){return V(()=>{let s=Xc(r,o),a=s.mean,i=s.variance,l=[];for(let d of Pr(0,r.rank))o.indexOf(d)!==-1?l.push(1):l.push(r.shape[d]);let u=a.reshape(l),c=i.reshape(l),p=e==null?null:e.reshape(l),m=t==null?null:t.reshape(l);return[hd(r,u,c,m,p,n),a,i]})}function Tq(r,e,t,o,n=.001){return y.arraysEqual(o.slice().sort(),Pr(0,r.rank-1))?Nq(r,e,t,o,n):Sq(r,e,t,o,n)}var gd=class extends Me{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=dt(e.betaInitializer||"zeros"),this.gammaInitializer=dt(e.gammaInitializer||"ones"),this.movingMeanInitializer=dt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=dt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer)}build(e){e=Qe(e);let t=this.axis>=0?this.axis:this.axis+e.length,o=e[t];if(o==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Et({ndim:e.length,axes:{[t]:o}})];let n=[o];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return V(()=>{let o=t.training==null?!1:t.training,n=Oe(e),s=n.shape,a=s.length,i=Pr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=Vn(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!y.arraysEqual(c,Pr(0,a).slice(0,a-1)),m=()=>{if(p){let b=this.movingMean.read().reshape(u),_=this.movingVariance.read().reshape(u),w=this.center?this.beta.read().reshape(u):null,v=this.scale?this.gamma.read().reshape(u):null;return hd(n,b,_,w,v,this.epsilon)}else return hd(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!o)return m();let[f,d,h]=Tq(n,this.gamma.read(),this.beta.read(),i,this.epsilon),g=(b,_,w)=>{V(()=>{let v=1-w,$=b.read(),A=$.sub(_).mul(v);b.write($.sub(A))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:It(this.betaInitializer),gammaInitializer:It(this.gammaInitializer),movingMeanInitializer:It(this.movingMeanInitializer),movingVarianceInitializer:It(this.movingVarianceInitializer),betaRegularizer:lt(this.betaRegularizer),gammaRegularizer:lt(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};gd.className="BatchNormalization";J.registerClass(gd);var xd=class extends Me{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=dt(e.betaInitializer||"zeros"),this.gammaInitializer=dt(e.gammaInitializer||"ones"),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Qe(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==Un(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let o=this.axis.map(s=>e[s]),n=!0;this.scale?this.gamma=this.addWeight("gamma",o,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",o,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let o=Oe(e),n=o.shape,s=n.length;return V(()=>{let a=!0,{mean:i,variance:l}=Xc(o,this.axis,a),u=Vn(1,s);for(let h of this.axis)u[h]=n[h];let c=h=>h!=null&&h.shape.length!==s&&this.axis!==[s-1]?h.reshape(u):h,p=c(this.gamma.read()),m=c(this.beta.read()),f=[],d=[];for(let h=0;h<s;++h)this.axis.indexOf(h)!==-1?(f.push(n[h]),d.push(1)):(f.push(1),d.push(n[h]));return i=i.tile(f),l=l.tile(f),p=p.tile(d),m=m.tile(d),hd(o,i,l,m,p,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:It(this.betaInitializer),gammaInitializer:It(this.gammaInitializer),betaRegularizer:lt(this.betaRegularizer),gammaRegularizer:lt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};xd.className="LayerNormalization";J.registerClass(xd);function Eq(r,e,t){return V(()=>{if(r.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(e==null&&(e=[[1,1],[1,1]]),e.length!==2||e[0].length!==2||e[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(t==null&&(t=Kr()),t!=="channelsLast"&&t!=="channelsFirst")throw new B(`Unknown data format: ${t}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let o;return t==="channelsFirst"?o=[[0,0],[0,0],e[0],e[1]]:o=[[0,0],e[0],e[1],[0,0]],$r(r,o)})}var yd=class extends Me{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Kr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,o;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],o=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new B(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new B(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);o=e.padding[1]}this.padding=[t,o]}this.inputSpec=[new Et({ndim:4})]}computeOutputShape(e){e=Qe(e);let t,o;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?o=e[3]+this.padding[1][0]+this.padding[1][1]:o=null,[e[0],e[1],t,o]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?o=e[2]+this.padding[1][0]+this.padding[1][1]:o=null,[e[0],t,o,e[3]])}call(e,t){return V(()=>Eq(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};yd.className="ZeroPadding2D";J.registerClass(yd);function Xg(r,e,t,o,n,s){return V(()=>{$t(n),sw(s),Xr(o),t==null&&(t=[1,1]),o==null&&(o="valid"),n==null&&(n=Kr()),s==null&&(s="max"),r=zf(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=Ea(r,e,t,i):a=va(r,e,t,i),n==="channelsFirst"&&(a=Ue(a,[0,3,1,2])),a})}function k1(r,e,t,o,n,s){return V(()=>{$t(n),sw(s),Xr(o),t==null&&(t=[1,1,1]),o==null&&(o="valid"),n==null&&(n=Kr()),s==null&&(s="max"),r=Fw(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=Gm(r,e,t,i):a=Dm(r,e,t,i),n==="channelsFirst"&&(a=Ue(a,[0,4,1,2,3])),a})}var zw=class extends Me{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(Wt(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)}`);Wt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Xr(this.padding),this.inputSpec=[new Et({ndim:3})]}computeOutputShape(e){e=Qe(e);let t=io(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return V(()=>{this.invokeCallHook(e,t),e=La(Oe(e),2);let o=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return wo(o,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},bd=class extends zw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Xr(n),Xg(e,t,o,n,s,"max")}};bd.className="MaxPooling1D";J.registerClass(bd);var _d=class extends zw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Xr(n),Xg(e,t,o,n,s,"avg")}};_d.className="AveragePooling1D";J.registerClass(_d);var Bw=class extends Me{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 ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Wt(this.poolSize,"poolSize"),Wt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),Xr(this.padding),this.inputSpec=[new Et({ndim:4})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=io(t,this.poolSize[0],this.padding,this.strides[0]),o=io(o,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o]:[e[0],t,o,e[3]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(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}},wd=class extends Bw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Xr(n),Xg(e,t,o,n,s,"max")}};wd.className="MaxPooling2D";J.registerClass(wd);var kd=class extends Bw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Xr(n),Xg(e,t,o,n,s,"avg")}};kd.className="AveragePooling2D";J.registerClass(kd);var Vw=class extends Me{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 ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Wt(this.poolSize,"poolSize"),Wt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),Xr(this.padding),this.inputSpec=[new Et({ndim:5})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=io(t,this.poolSize[0],this.padding,this.strides[0]),o=io(o,this.poolSize[1],this.padding,this.strides[1]),n=io(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o,n]:[e[0],t,o,n,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(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}},vd=class extends Vw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Xr(n),k1(e,t,o,n,s,"max")}};vd.className="MaxPooling3D";J.registerClass(vd);var Cd=class extends Vw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Xr(n),k1(e,t,o,n,s,"avg")}};Cd.className="AveragePooling3D";J.registerClass(Cd);var Gw=class extends Me{constructor(e){super(e);this.inputSpec=[new Et({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ne}},Id=class extends Gw{constructor(e){super(e||{})}call(e,t){return V(()=>{let o=Oe(e);return ht(o,1)})}};Id.className="GlobalAveragePooling1D";J.registerClass(Id);var Nd=class extends Gw{constructor(e){super(e||{})}call(e,t){return V(()=>{let o=Oe(e);return ar(o,1)})}};Nd.className="GlobalMaxPooling1D";J.registerClass(Nd);var Ww=class extends Me{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),this.inputSpec=[new Et({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ne}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Sd=class extends Ww{call(e,t){return V(()=>{let o=Oe(e);return this.dataFormat==="channelsLast"?ht(o,[1,2]):ht(o,[2,3])})}};Sd.className="GlobalAveragePooling2D";J.registerClass(Sd);var Td=class extends Ww{call(e,t){return V(()=>{let o=Oe(e);return this.dataFormat==="channelsLast"?ar(o,[1,2]):ar(o,[2,3])})}};Td.className="GlobalMaxPooling2D";J.registerClass(Td);var Uw=class extends Me{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,o={}){let n=t.layer,s=Yr(n,o);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},Ed=class extends Uw{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Qe(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=Qe(e);let t=[e[0]].concat(e.slice(2)),o=this.layer.computeOutputShape(t),n=e[1];return[o[0],n].concat(o.slice(1))}call(e,t){return V(()=>(e=Oe(e),Mw((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Ed.className="TimeDistributed";J.registerClass(Ed);function Aq(r){Gi(ST,"BidirectionalMergeMode",r)}var Dq="concat",Ad=class extends Uw{constructor(e){super(e);let t=e.layer.getConfig(),o={};o.className=e.layer.getClassName(),o.config=t,this.forwardLayer=Yr(o),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=Yr(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Dq:e.mergeMode,Aq(this.mergeMode),e.weights)throw new Ne("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,o=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,o)),this.backwardLayer.setWeights(e.slice(o))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let o,n,s;return this.returnState&&(s=t.slice(1)),o=t[0],o=o,this.mergeMode==="concat"?(o[o.length-1]*=2,n=[o]):this.mergeMode==null?n=[o,o.slice()]:n=[o],this.returnState?this.mergeMode==null?n.concat(s).concat(s.slice()):[o].concat(s).concat(s.slice()):dr(n)}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=Pw(e,o,n,this.numConstants);if(e=s.inputs,o=s.initialState,n=s.constants,Array.isArray(e)&&(o=e.slice(1),e=e[0]),(o==null||o.length===0)&&n==null)return super.apply(e,t);let a=[],i=[];if(o!=null){let u=o.length;if(u%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=o,a.push(...o);let c=o.map(p=>new Et({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,u/2),this.backwardLayer.stateSpec=c.slice(u/2),i.push(...c)}if(n!=null)throw new Ne("Support for constants in Bidirectional layers is not implemented yet.");let l=a[0]instanceof Lr;for(let u of a)if(u instanceof Lr!==l)throw new B("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(l){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let o=t.initialState,n,s;if(o==null)n=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let l=o.slice(0,o.length/2),u=o.slice(o.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:l})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(n)&&(a=n.slice(1).concat(s.slice(1))),n=n[0],s=s[0]),this.returnSequences&&(s=qt(s,1));let i;return this.mergeMode==="concat"?i=cp([n,s]):this.mergeMode==="sum"?i=Q(n,s):this.mergeMode==="ave"?i=O(.5,Q(n,s)):this.mergeMode==="mul"?i=O(n,s):this.mergeMode==null&&(i=[n,s]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){$s(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),$s(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let o;if(this.returnSequences?this.mergeMode==null?o=[t,t]:o=t:this.mergeMode==null?o=[null,null]:o=null,this.returnState){let s=this.forwardLayer.states.map(a=>null);return Array.isArray(o)?o.concat(s).concat(s):[o].concat(s).concat(s)}else return o}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let o=Yr(t.layer);if(delete t.layer,t.numConstants!=null)throw new Ne("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let n=t;return n.layer=o,new e(n)}};Ad.className="Bidirectional";J.registerClass(Ad);function $q(r){return new Wi(r)}function Rq(r){return new Of(r)}function Fq(r){return new $f(r)}function Oq(r){return new Rf(r)}function Pq(r){return new Ff(r)}function Mq(r){return new Mf(r)}function Lq(r){return new Pf(r)}function zq(r){return new rc(r)}function Bq(r){return new yl(r)}function Vq(r){return new Bf(r)}function 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The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){B(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function fq(r,e,t){let o=e.length;if(r==null||Array.isArray(r)&&r.length===0)return e.map(n=>null);if(o===1)return Array.isArray(r)&&r.length===1?r:typeof r=="object"&&e[0]in r?[r[e[0]]]:[r];if(Array.isArray(r)){if(r.length!==o)throw new Error(`Provided ${t} is an array of ${r.length} element(s), but the model has ${o} outputs. Make sure a set of weights is provided for each model output.`);return r}else if(typeof r=="object"&&Object.keys(r).length>0&&typeof r[Object.keys(r)[0]]=="object"){let n=[];return e.forEach(s=>{s in r?n.push(r[s]):n.push(null)}),n}else throw new Error(`The model has multiple (${o}) outputs, so ${t} must be either an array with ${o} elements or an object with ${e} keys. Provided ${t} not understood: ${JSON.stringify(r)}`)}function Kg(r,e){return fq(r,e,"classWeight")}async function Xg(r,e,t,o){if(e!=null||o!=null)throw new Error("Support sampleWeight is not implemented yet");if(t!=null){let n=B(()=>{if(r.shape.length===1)return r.clone();if(r.shape.length===2)if(r.shape[1]>1){let i=1;return r.argMax(i)}else{if(r.shape[1]===1)return r.reshape([r.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${r.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${r.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await n.data());Te(n);let a=[];return s.forEach(i=>{if(t[i]==null)throw new Error(`classWeight must contain all classes in the training data. 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t}makeTrainFunction(){return e=>{let t=[],o=e.slice(0,this.inputs.length),n=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],i=()=>{let p=[];for(let h=0;h<this.inputs.length;++h)p.push({key:this.inputs[h],value:o[h]});let m=new Ps(p),f=Qu(this.outputs,m,{training:!0}),d;for(let h=0;h<this.lossFunctions.length;++h){let x=this.lossFunctions[h](n[h],f[h]);s[h]!=null&&(x=n1(x,s[h]));let b=ht(x);t.push(b),h===0?d=x:d=Q(d,x)}for(let h=0;h<this.metricsTensors.length;++h){let g;if(this.outputs.length>1&&h<this.outputs.length)g=t[h];else{let x=this.metricsTensors[h][0],b=this.metricsTensors[h][1];g=ht(x(n[b],f[b]))}Dt(g),a.push(g)}return d=ht(d),this.calculateLosses().forEach(h=>{d=Q(d,h)}),d},l=this.collectedTrainableWeights.map(p=>p.read()),u=!0;return[this.optimizer_.minimize(i,u,l)].concat(a)}}makeTestFunction(){this.testFunction=e=>B(()=>{let t=[],o,n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let u=0;u<this.inputs.length;++u)a.push({key:this.inputs[u],value:n[u]});let i=new Ps(a),l=Qu(this.outputs,i);for(let u=0;u<this.lossFunctions.length;++u){let c=this.lossFunctions[u],p=ht(c(s[u],l[u]));u===0?o=p:o=Q(o,p),t.push(o)}for(let u=0;u<this.metricsTensors.length;++u){let c=this.metricsTensors[u][0],p=this.metricsTensors[u][1],m=ht(c(s[p],l[p]));t.push(m)}return t})}async fit(e,t,o={}){return c1(this,e,t,o)}async fitDataset(e,t){return l1(this,e,t)}async trainOnBatch(e,t){let o=await this.standardizeUserData(e,t),n=o[0],s=o[1],i=this.makeTrainFunction()(n.concat(s)),l=[];for(let u of i){let c=await u.data();l.push(c[0])}return Te(i),dr(l)}getNamedWeights(e){let t=[],o=e!=null&&e.trainableOnly,n=o?this.trainableWeights:this.weights,s=this.getWeights(o);for(let a=0;a<n.length;++a)o&&!n[a].trainable||t.push({name:n[a].originalName,tensor:s[a]});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=Hc().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Hc().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Hn(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=>Hn(t))}else{let t=Object.keys(this.loss);e={};let o=this.loss;for(let n of t)if(typeof o[n]=="string")e[n]=Hn(o[n]);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[Hn(Rf(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Hn(Rf(e)));{let e={};for(let t in this.metrics)e[t]=Hn(Rf(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=Ju(e.optimizer_config),o=Yr(t),n;if(typeof e.loss=="string")n=Pa(e.loss);else if(Array.isArray(e.loss))n=e.loss.map(a=>Pa(a));else if(e.loss!=null){n={};for(let a in e.loss)n[a]=Pa(e.loss[a])}let s;if(Array.isArray(e.metrics))s=e.metrics.map(a=>Pa(a));else if(e.metrics!=null){s={};for(let a in e.metrics)s[a]=Pa(e.metrics[a])}this.compile({loss:n,metrics:s,optimizer:o})}async save(e,t){if(typeof e=="string"){let u=vr.getSaveHandlers(e);if(u.length===0)throw new z(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new z(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new z("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let o=await vr.encodeWeights(this.getNamedWeights(t)),n=!1,s=null,i={modelTopology:this.toJSON(s,n),format:vq,generatedBy:`TensorFlow.js tfjs-layers v${hl}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u="optimizer",{data:c,specs:p}=await vr.encodeWeights(await this.optimizer.getWeights(),u);o.specs.push(...p),o.data=vr.concatenateArrayBuffers([o.data,c])}if(this.userDefinedMetadata!=null){let u=!0;bw(this.userDefinedMetadata,this.name,u),i.userDefinedMetadata=this.userDefinedMetadata}return i.weightData=o.data,i.weightSpecs=o.specs,e.save(i)}setUserDefinedMetadata(e){bw(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};vo.className="Model";J.registerClass(vo);var vw=class extends vo{};vw.className="Functional";J.registerClass(vw);async function d1(r,e){"modelTopology"in r||(r={modelTopology:r}),r=r;let t=r.modelTopology;t.model_config!=null&&(t=t.model_config);let o=Ju(t),n=Yr(o,e);if(r.weightsManifest!=null){let s=await vr.loadWeights(r.weightsManifest,r.pathPrefix,n.weights.map(i=>i.originalName)),a={};for(let i of n.weights)a[i.originalName]=s[i.originalName];n.loadWeights(a),Te(s)}return n}async function h1(r,e){if(e==null&&(e={}),typeof r=="string"){let t=vr.getLoadHandlers(r,e);if(t.length===0)t.push(vr.browserHTTPRequest(r,e));else if(t.length>1)throw new z(`Found more than one (${t.length}) load handlers for URL '${r}'`);r=t[0]}return Cq(r,void 0,e)}async function Cq(r,e,t){if(t==null&&(t={}),r.load==null)throw new z("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let o=await r.load(),n=o.modelTopology;n.model_config!=null&&(n=n.model_config);let s=t.strict==null?!0:t.strict,a=o.weightData!=null&&o.weightSpecs!=null&&s,i=Yr(Ju(n),e,a),l=o.trainingConfig;if(l!=null&&i.loadTrainingConfig(l),o.userDefinedMetadata!=null&&i.setUserDefinedMetadata(o.userDefinedMetadata),o.weightData!=null){if(o.weightSpecs==null)throw new z("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:c}=Iq(o.weightData,o.weightSpecs);i.loadWeights(u,s),i.optimizer!=null&&c.length>0&&await i.optimizer.setWeights(c),Te(u),Te(c.map(p=>p.tensor))}return i}function Iq(r,e){let t=vr.decodeWeights(r,e),o={},n=[];return e.forEach(s=>{s.group==="optimizer"?n.push({name:s.name,tensor:t[s.name]}):o[s.name]=t[s.name]}),{modelWeights:o,optimizerWeights:n}}var Hi=class extends vo{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:pl("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(o=>o<0))throw new z(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Hi||e instanceof vo,o;if(t){if(o=e,o.outputs.length!==1)throw new z("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(o.inputs.length!==1)throw new z("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new z("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let n=Og({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(n)}if(t)this.outputs=o.outputs,this.inputs=o.inputs;else{if(e.inboundNodes.length!==1)throw new z(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new z("All layers in a Sequential model should have a single output tensor. 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compiled before being used.");return this.model.evaluate(e,t,o)}async evaluateDataset(e,t){if(!this.built)throw new Or("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,o={}){if(!this.built)throw new Or("The model needs to be compiled before being used.");return this.model.fit(e,t,o)}async fitDataset(e,t){if(!this.built)throw new Or("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,o={},n=!1){let s,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new z("Legacy serialization format not supported yet.");s=t}else y.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."),s=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Hi))throw new Ne(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let l of s){let c=Yr(l,void 0,n);n&&c.setFastWeightInitDuringBuild(!0),i.add(c)}return i}set stopTraining(e){if(this.model==null)throw new z("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 z("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let o={};o.className=t.getClassName(),o.config=t.getConfig(),e.push(o)}return{name:this.name,layers:e}}};Hi.className="Sequential";J.registerClass(Hi);function g1(r){return new vo(r)}function x1(r){return new Hi(r)}function y1(r,e){return e==null&&(e={}),h1(r,e)}function Qg(r){return Og(r)}function b1(r,e){no.registerCallbackConstructor(r,e)}var so=class extends J.Serializable{getConfig(){return{}}},Cw=class extends so{apply(e,t=1){return zT(e,t)}};Cw.className="elu";J.registerClass(Cw);var Iw=class extends so{apply(e){return Mu(e)}};Iw.className="selu";J.registerClass(Iw);var Nw=class extends so{apply(e){return Ir(e)}};Nw.className="relu";J.registerClass(Nw);var Sw=class extends so{apply(e){return B(()=>As(6,Ir(e)))}};Sw.className="relu6";J.registerClass(Sw);var Tw=class extends so{apply(e){return e}};Tw.className="linear";J.registerClass(Tw);var Ew=class extends so{apply(e){return 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e.className=r,e.config={},Pw(e)}else return r instanceof so?r:Pw(r)}function Mw(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var Lw=class extends J.Serializable{},ec=class extends Lw{constructor(e){super();Mw(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return B(()=>{let t=gt([1]);return this.hasL1&&(t=Q(t,ye(O(this.l1,Tt(e))))),this.hasL2&&(t=Q(t,ye(O(this.l2,Xu(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};ec.className="L1L2";J.registerClass(ec);function _1(r){return Mw(r),new ec({l1:r!=null?r.l1:null,l2:0})}function w1(r){return Mw(r),new ec({l2:r!=null?r.l2:null,l1:0})}var k1={l1l2:"L1L2"};function lt(r){return lp(r)}function v1(r,e={}){return Vi(r,J.SerializationMap.getMap().classNameMap,e,"regularizer")}function bt(r){if(r==null)return null;if(typeof r=="string"){let t={className:r in k1?k1[r]:r,config:{}};return v1(t)}else return r instanceof Lw?r:v1(r)}var Of=class extends Me{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let o=Ir(e);return this.maxValue!=null&&(o=nr(o,0,this.maxValue)),o}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Of.className="ReLU";J.registerClass(Of);var Pf=class extends Me{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let o=Oe(e);return Sa(o,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Pf.className="LeakyReLU";J.registerClass(Pf);var Mf=class extends Me{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=dt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=bt(e.alphaRegularizer),this.alphaConstraint=Pt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Qe(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let o={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)o[n]=e[n];this.inputSpec=[new Et({ndim:e.length,axes:o})],this.built=!0}call(e,t){return e=Oe(e),Da(e,this.alpha.read())}getConfig(){let e={alphaInitializer:It(this.alphaInitializer),alphaRegularizer:lt(this.alphaRegularizer),alphaConstraint:Ot(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Mf.className="PReLU";J.registerClass(Mf);var Lf=class extends Me{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ne(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let o=Oe(e);return Ss(o)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Lf.className="ELU";J.registerClass(Lf);var zf=class extends Me{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let o=Oe(e);return o.mul(Ma(o.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};zf.className="ThresholdedReLU";J.registerClass(zf);var Bf=class extends Me{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Ff().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let o=Oe(e);return this.softmax(o,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Bf.className="Softmax";J.registerClass(Bf);function xl(r,e,t){if(typeof r=="number")return jn(r,e);if(r.length!==e)throw new z(`The ${t} argument must be an integer or tuple of ${e} integers. Received: ${r.length} elements.`);for(let o=0;o<e;++o){let n=r[o];if(!FT(n))throw new z(`The ${t} argument must be an integer or tuple of ${e} integers. Received: ${JSON.stringify(r)} including a non-integer number ${n}`)}return r}function io(r,e,t,o,n=1){if(r==null)return r;let s=e+(e-1)*(n-1),a;return t==="same"?a=r:a=r-s+1,Math.floor((a+o-1)/o)}function Vf(r,e,t,o){if(r==null)return null;if(o==="valid")r=r*e+Os([t-e,0]);else if(o==="same")r=r*e;else throw new z(`Unsupport padding mode: ${o}.`);return r}function Gf(r,e){return B(()=>(Rt(e),e==="channelsFirst"?Ue(r,[0,2,3,1]):r))}function zw(r,e){return B(()=>(Rt(e),e==="channelsFirst"?Ue(r,[0,2,3,4,1]):r))}function Nq(r,e,t,o=1,n="valid",s,a=1){return B(()=>{if(s==null&&(s=Kr()),Rt(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(e.shape.length!==3)throw new z(`The kernel for a conv1dWithBias operation should be 3, but is ${e.shape.length} instead`);if(t!=null&&t.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${e.shape.length} instead`);if(s==="channelsFirst"&&(r=Ue(r,[0,2,1])),n==="causal")throw new Ne("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=Cu(r,e,o,n==="same"?"same":"valid","NWC",a);return t!=null&&(i=ro(i,t)),i})}function C1(r,e,t,o=[1,1],n="valid",s,a,i=null){return B(()=>{if(s==null&&(s=Kr()),Rt(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(e.rank!==3&&e.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let l=Gf(r,s);if(n==="causal")throw new Ne("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Wn.conv2d({x:l,filter:e,strides:o,pad:n==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:t,activation:i}),s==="channelsFirst"&&(l=Ue(l,[0,3,1,2])),l})}function Sq(r,e,t,o=[1,1,1],n="valid",s,a){return B(()=>{if(s==null&&(s=Kr()),Rt(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(e.rank!==4&&e.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let i=zw(r,s);if(n==="causal")throw new Ne("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Pm(i,e,o,n==="same"?"same":"valid","NDHWC",a),t!=null&&(i=ro(i,t)),s==="channelsFirst"&&(i=Ue(i,[0,4,1,2,3])),i})}var Np=class extends Me{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Np.verifyArgs(t),this.rank=e,Wt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ne(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=xl(t.kernelSize,e,"kernelSize"),this.strides=xl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Xr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=Ls(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=dt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=bt(t.biasRegularizer),this.activityRegularizer=bt(t.activityRegularizer),this.dilationRate=xl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`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 z(`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 z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Lo("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Ig(e.kernelSize,"number",1,3))throw new z(`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:Ms(this.activation),useBias:this.useBias,biasInitializer:It(this.biasInitializer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},tc=class extends Np{constructor(e,t){super(e,t);this.kernel=null,tc.verifyArgs(t),this.filters=t.filters,Wt(this.filters,"filters"),this.kernelInitializer=dt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=bt(t.kernelRegularizer)}build(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[t]}`);let o=e[t],n=this.kernelSize.concat([o,this.filters]);this.kernel=this.addWeight("kernel",n,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]:o}}],this.built=!0}call(e,t){return B(()=>{e=Oe(e);let o,n=this.bias==null?null:this.bias.read(),s=Ng(this.activation.getClassName());if(s!=null&&this.rank===2)o=C1(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)o=Nq(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)o=C1(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)o=Sq(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ne("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(o=this.activation.apply(o))}return o})}computeOutputShape(e){e=Qe(e);let t=[],o=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<o.length;++s){let a=io(o[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:It(this.kernelInitializer),kernelRegularizer:lt(this.kernelRegularizer),kernelConstraint:Ot(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 z(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},yl=class extends tc{constructor(e){super(2,e);yl.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Ig(e.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};yl.className="Conv2D";J.registerClass(yl);var rc=class extends tc{constructor(e){super(3,e);rc.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 z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};rc.className="Conv3D";J.registerClass(rc);var Wf=class extends yl{constructor(e){super(e);if(this.inputSpec=[new Et({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Qe(e),e.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let o=e[t],n=this.kernelSize.concat([this.filters,o]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Et({ndim:4,axes:{[t]:o}})],this.built=!0}call(e,t){return B(()=>{let o=Oe(e);if(o.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${o.shape.length}`);let n=o.shape,s=n[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let l=n[a],u=n[i],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=Vf(l,m,c,this.padding),h=Vf(u,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(o=Ue(o,[0,2,3,1]));let x=Iu(o,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Ue(x,[0,3,1,2])),this.bias!=null&&(x=ro(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(e){e=Qe(e);let t=e.slice(),o,n,s;this.dataFormat==="channelsFirst"?(o=1,n=2,s=3):(o=3,n=1,s=2);let a=this.kernelSize[0],i=this.kernelSize[1],l=this.strides[0],u=this.strides[1];return t[o]=this.filters,t[n]=Vf(t[n],l,a,this.padding),t[s]=Vf(t[s],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Wf.className="Conv2DTranspose";J.registerClass(Wf);var Bw=class extends tc{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 z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new z("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 z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=dt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=bt(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=dt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=bt(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=Qe(e),e.length<this.rank+2)throw new z(`Inputs to SeparableConv${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 z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let o=e[t],n=this.kernelSize.concat([o,this.depthMultiplier]),s=[];for(let i=0;i<this.rank;++i)s.push(1);s.push(o*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Et({ndim:this.rank+2,axes:{[t]:o}})],this.built=!0}call(e,t){return B(()=>{e=Oe(e);let o;if(this.rank===1)throw new Ne("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ue(e,[0,2,3,1])),o=Zm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(o=ro(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),this.dataFormat==="channelsFirst"&&(o=Ue(o,[0,3,1,2])),o})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=It(this.depthwiseInitializer),e.pointwiseInitializer=It(this.pointwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.pointwiseRegularizer=lt(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};Bw.className="SeparableConv";var Uf=class extends Bw{constructor(e){super(2,e)}};Uf.className="SeparableConv2D";J.registerClass(Uf);var oc=class extends tc{constructor(e){super(1,e);oc.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"&&!Ig(e.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};oc.className="Conv1D";J.registerClass(oc);var jf=class extends Me{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 B(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let o=xf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return xf(o,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let o=xf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return xf(o,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}};jf.className="Cropping2D";J.registerClass(jf);var Hf=class extends Me{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,DT(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],o=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,o]}else{let t=e[1]==null?null:this.size[0]*e[1],o=e[2]==null?null:this.size[1]*e[2];return[e[0],t,o,e[3]]}}call(e,t){return B(()=>{let o=Oe(e),n=o.shape;if(this.dataFormat==="channelsFirst"){o=Ue(o,[0,2,3,1]);let s=this.size[0]*n[2],a=this.size[1]*n[3],i=this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a]);return Ue(i,[0,3,1,2])}else{let s=this.size[0]*n[1],a=this.size[1]*n[2];return this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Hf.className="UpSampling2D";J.registerClass(Hf);function Tq(r,e,t=[1,1],o="valid",n,s){return B(()=>{n==null&&(n=Kr()),Rt(n);let a=Gf(r,n);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(e.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${e.rank}-D`);return a=Ns(a,e,t,o==="same"?"same":"valid","NHWC",s),n==="channelsFirst"&&(a=Ue(a,[0,3,1,2])),a})}var qf=class extends Np{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=dt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=bt(e.depthwiseRegularizer)}build(e){if(e=Qe(e),e.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let o=e[t],n=[this.kernelSize[0],this.kernelSize[1],o,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[o*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{e=Oe(e);let o=Tq(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(o=ro(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),o})}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=io(t,this.kernelSize[0],this.padding,this.strides[0]),a=io(o,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,s,a]:[e[0],s,a,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=It(this.depthwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};qf.className="DepthwiseConv2D";J.registerClass(qf);function Vw(r,e,t,o){if(Array.isArray(r)){if(e!=null||t!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");o!=null&&(t=r.slice(r.length-o,r.length),r=r.slice(0,r.length-o)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function n(s){return s==null||Array.isArray(s)?s:[s]}return e=n(e),t=n(t),{inputs:r,initialState:e,constants:t}}function Gw(r,e,t,o=!1,n,s,a=!1,i=!1){return B(()=>{let l=e.shape.length;if(l<3)throw new z(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Pr(2,l));if(e=Ue(e,u),s!=null)throw new Ne("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),n!=null&&(n=n.asType("bool").asType("float32"),n.rank===l-1&&(n=sr(n,-1)),n=Ue(n,u)),o&&(e=qt(e,0),n!=null&&(n=qt(n,0)));let c=[],p,m=t,f=e.shape[0],d=ur(e),h;n!=null&&(h=ur(n));for(let x=0;x<f;++x){let b=d[x],_=B(()=>r(b,m));if(n==null)p=_[0],m=_[1];else{let w=B(()=>{let v=h[x],$=er(v).sub(v),A=_[0].mul(v).add(m[0].mul($)),R=m.map((M,V)=>_[1][V].mul(v).add(M.mul($)));return{output:A,newStates:R}});p=w.output,m=w.newStates}i&&c.push(p)}let g;return i&&(g=Bt(c,1)),[p,g,m]})}var ao=class extends Me{constructor(e){super(e);let t;if(e.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Sp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new z("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 Et({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 Pr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Fg(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let o=t[0],n;if(this.returnSequences?n=[e[0],e[1],o]:n=[e[0],o],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[n].concat(s)}else return n}computeMask(e,t){return B(()=>{Array.isArray(t)&&(t=t[0]);let o=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(s=>null);return[o].concat(n)}else return o})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let o=0;o<e;++o)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 Ne("Constants support is not implemented in RNN yet.");Fg(e)&&(e=e[0]),e=e;let o=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new Et({shape:[o,null,...n]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new Ne("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!y.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new z(`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=a.map(i=>new Et({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ko("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape[0];if(o==null)throw new z("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(n=>gt([o,n])):this.states_=[gt([o,this.cell.stateSize])];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>gt([o,n])):this.states_[0]=gt([o,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`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()):Te(this.states_);for(let n=0;n<this.states_.length;++n){let s=e[n],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[o,a];if(!y.arraysEqual(s.shape,i))throw new z(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${s.shape}`);this.states_[n]=s}}this.states_=this.states_.map(n=>Dt(n.clone()))})}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=Vw(e,o,n,this.numConstants);e=s.inputs,o=s.initialState,n=s.constants;let a=[],i=[];if(o!=null){t.initialState=o,a=a.concat(o),this.stateSpec=[];for(let u of o)this.stateSpec.push(new Et({shape:u.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,a=a.concat(n),this.numConstants=n.length),a[0]instanceof Lr){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return B(()=>{let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;e=Oe(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new z(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},u=Gw((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,o,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,n);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(e){return B(()=>{let t=gt(e.shape);return t=ye(t,[1,2]),t=La(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(o=>o>1?Eg(t,[1,o]):t):this.cell.stateSize>1?[Eg(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 o=this.cell.getConfig();return this.getClassName()===ao.className&&(t.cell={className:this.cell.getClassName(),config:o}),Object.assign({},o,e,t)}static fromConfig(e,t,o={}){let n=t.cell,s=Yr(n,o);return new e(Object.assign(t,{cell:s}))}};ao.className="RNN";J.registerClass(ao);var bl=class extends Me{},Tp=class extends bl{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,Wt(this.units,"units"),this.activation=Ls(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Ku([1,Os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ku([1,Os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Qe(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 B(()=>{if(e=e,e.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let o=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ba({ones:()=>er(e),rate:this.dropout,training:n})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ba({ones:()=>er(o),rate:this.recurrentDropout,training:n}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=Yn(O(e,a),this.kernel.read()):s=Yn(e,this.kernel.read()),this.bias!=null&&(s=ro(s,this.bias.read())),i!=null&&(o=O(o,i));let l=Q(s,Yn(o,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ms(this.activation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Tp.className="SimpleRNNCell";J.registerClass(Tp);var Kf=class extends ao{constructor(e){e.cell=new Tp(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return new e(t)}};Kf.className="SimpleRNN";J.registerClass(Kf);var Ep=class extends bl{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 z("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Wt(this.units,"units"),this.activation=Ls(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ls(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Ku([1,Os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ku([1,Os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Qe(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 B(()=>{if(e=e,e.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ba({ones:()=>er(e),rate:this.dropout,training:o,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ba({ones:()=>er(n),rate:this.recurrentDropout,training:o,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,i,l,u;0<this.dropout&&this.dropout<1&&(e=O(e,s[0]));let c=Yn(e,this.kernel.read());this.useBias&&(c=ro(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=O(n,a[0]));let p=this.recurrentKernel.read(),[m,f]=lr(p,[2*this.units,this.units],p.rank-1),d=Yn(n,m),[h,g,x]=lr(c,3,c.rank-1),[b,_]=lr(d,2,d.rank-1);i=this.recurrentActivation.apply(Q(h,b)),l=this.recurrentActivation.apply(Q(g,_));let w=Yn(O(l,n),f);u=this.activation.apply(Q(x,w));let v=Q(O(i,n),O(Q(1,je(i)),u));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ms(this.activation),recurrentActivation:Ms(this.recurrentActivation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Ep.className="GRUCell";J.registerClass(Ep);var Xf=class extends ao{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 Ep(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Xf.className="GRU";J.registerClass(Xf);var _l=class extends bl{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,Wt(this.units,"units"),this.activation=Ls(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ls(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(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=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Ku([1,Os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ku([1,Os([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=Qe(e);let o=e[e.length-1];this.kernel=this.addWeight("kernel",[o,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 n;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;n=new(t=class extends oo{apply(l,u){let c=s.apply([a]),p=new Yu().apply([a]),m=s.apply([a*2]);return mw(mw(c,p),m)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return B(()=>{let o=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ba({ones:()=>er(e),rate:this.dropout,training:o,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ba({ones:()=>er(n),rate:this.recurrentDropout,training:o,count:4}));let a=this.dropoutMask,i=this.recurrentDropoutMask,l,u,c,p;0<this.dropout&&this.dropout<1&&(e=O(e,a[0]));let m=Yn(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=O(n,i[0])),m=Q(m,Yn(n,this.recurrentKernel.read())),this.useBias&&(m=ro(m,this.bias.read()));let[f,d,h,g]=lr(m,4,m.rank-1);l=this.recurrentActivation.apply(f),u=this.recurrentActivation.apply(d),c=Q(O(u,s),O(l,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let x=O(p,this.activation.apply(c));return[x,x,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ms(this.activation),recurrentActivation:Ms(this.recurrentActivation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};_l.className="LSTMCell";J.registerClass(_l);var Yf=class extends ao{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 _l(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Yf.className="LSTM";J.registerClass(Yf);var Sp=class extends bl{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 B(()=>{e=e;let o=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(o.splice(0,i.stateSize.length)):n.push(o.splice(0,1));n.reverse();let s=[],a;for(let i=0;i<this.cells.length;++i){let l=this.cells[i];o=n[i],i===0?a=[e[0]].concat(o):a=[a[0]].concat(o),a=l.call(a,t),s.push(a.slice(1))}o=[];for(let i of s.slice().reverse())o.push(...i);return[a[0]].concat(o)})}build(e){Fg(e)&&(e=e[0]),e=e;let t;this.cells.forEach((o,n)=>{Fs(`RNNCell_${n}`,()=>{o.build(e),Array.isArray(o.stateSize)?t=o.stateSize[0]:t=o.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,o={}){let n=[];for(let s of t.cells)n.push(Yr(s,o));return new e({cells:n})}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 o of this.cells)t.push(...o.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Nf(e)}setWeights(e){let t=[];for(let o of this.cells){let n=o.weights.length,s=e.splice(n);for(let a=0;a<o.weights.length;++a)t.push([o.weights[a],s[a]])}wp(t)}};Sp.className="StackedRNNCells";J.registerClass(Sp);function Ba(r){let{ones:e,rate:t,training:o=!1,count:n=1}=r,s=()=>Dg(e(),t),a=()=>cl(s,e,o);return!n||n<=1?Dt(a().clone()):Array(n).fill(void 0).map(a).map(l=>Dt(l.clone()))}var Eq=function(r,e){var t={};for(var o in r)Object.prototype.hasOwnProperty.call(r,o)&&e.indexOf(o)<0&&(t[o]=r[o]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var n=0,o=Object.getOwnPropertySymbols(r);n<o.length;n++)e.indexOf(o[n])<0&&Object.prototype.propertyIsEnumerable.call(r,o[n])&&(t[o[n]]=r[o[n]]);return t};var Ww=class extends ao{constructor(e){if(e.unroll)throw new Ne("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ne("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Et({ndim:5})]}call(e,t){return B(()=>{if(this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new z("ConvRNN2D cell does not support constants");let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}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 B(()=>{let{stateSize:t}=this.cell,o=e.shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)],a=gt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ko("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)];if(o[0]==null)throw new z("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(()=>gt(s)):this.states_=[gt(s)];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>gt(s)):this.states_[0]=gt(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`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()):Te(this.states_);for(let i=0;i<this.states_.length;++i){let l=e[i],u=s;if(!y.arraysEqual(l.shape,u))throw new z(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${l.shape}`);this.states_[i]=l}}this.states_=this.states_.map(i=>Dt(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:o,kernelSize:n,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=io(u,n[0],s,a[0],i[0]),m=io(c,n[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[o,p,m]:[p,m,o]]}};Ww.className="ConvRNN2D";var Ap=class extends _l{constructor(e){let{filters:t,kernelSize:o,strides:n,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Wt(this.filters,"filters"),this.kernelSize=xl(o,2,"kernelSize"),this.kernelSize.forEach(l=>Wt(l,"kernelSize")),this.strides=xl(n||1,2,"strides"),this.strides.forEach(l=>Wt(l,"strides")),this.padding=s||"valid",Xr(this.padding),this.dataFormat=a||"channelsLast",Rt(this.dataFormat),this.dilationRate=xl(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>Wt(l,"dilationRate"))}build(e){var t;e=Qe(e);let o=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[o]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[o]}`);let n=e[o],s=4,a=this.kernelSize.concat([n,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let l;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;l=new(t=class extends oo{apply(m,f){let d=u.apply([c]),h=Cr([c]),g=u.apply([c*2]);return pp([d,h,g])}},t.className="CustomInit",t)}else l=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,l,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return B(()=>{if(e.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training||!1,n=e[0],s=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ba({ones:()=>er(n),rate:this.dropout,training:o,count:i}));let l=this.dropoutMask,u=(ie,se,pe)=>!se||!se[pe]?ie:O(se[pe],ie),c=u(n,l,0),p=u(n,l,1),m=u(n,l,2),f=u(n,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ba({ones:()=>er(s),rate:this.recurrentDropout,training:o,count:i}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),x=u(s,d,2),b=u(s,d,3),_=3,[w,v,$,A]=lr(this.kernel.read(),i,_),[R,M,V,G]=this.useBias?lr(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,w,R,this.padding),p=this.inputConv(p,v,M,this.padding),m=this.inputConv(m,$,V,this.padding),f=this.inputConv(f,A,G,this.padding);let[W,q,Z,X]=lr(this.recurrentKernel.read(),i,_);h=this.recurrentConv(h,W),g=this.recurrentConv(g,q),x=this.recurrentConv(x,Z),b=this.recurrentConv(b,X);let Y=this.recurrentActivation.apply(Q(c,h)),te=this.recurrentActivation.apply(Q(p,g)),K=Q(O(te,a),O(Y,this.activation.apply(Q(m,x)))),re=O(this.recurrentActivation.apply(Q(f,b)),this.activation.apply(K));return[re,re,K]})}getConfig(){let e=super.getConfig(),{units:t}=e,o=Eq(e,["units"]),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},o,n)}inputConv(e,t,o,n){let s=jr(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return o?ro(s,o,this.dataFormat):s}recurrentConv(e,t){return jr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Ap.className="ConvLSTM2DCell";J.registerClass(Ap);var Zf=class extends Ww{constructor(e){let t=new Ap(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Zf.className="ConvLSTM2D";J.registerClass(Zf);var Dp=class extends Me{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,o=[];for(let n=0;n<this.noiseShape.length;++n)o.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return o}call(e,t){return B(()=>{this.invokeCallHook(e,t);let o=Oe(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,s=this.getNoiseShape(o);return cl(()=>Dg(o,this.rate,s,this.seed),()=>o,n)}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()}};Dp.className="Dropout";J.registerClass(Dp);var Jf=class extends Dp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Jf.className="SpatialDropout1D";J.registerClass(Jf);var Qf=class extends Me{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,Wt(this.units,"units"),this.activation=Ls(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(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=Qe(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=Qe(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let o=Oe(e),n=Ng(this.activation.getClassName()),s;return n!=null?s=Yn(o,this.kernel.read(),n,this.bias?this.bias.read():null):(s=Yn(o,this.kernel.read()),this.bias!=null&&(s=ro(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Ms(this.activation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Qf.className="Dense";J.registerClass(Qf);var ed=class extends Me{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Qe(e);for(let t of e.slice(1))if(t==null)throw new z(`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],Xn(e,1)]}call(e,t){return B(()=>{this.invokeCallHook(e,t);let o=Oe(e);if(this.dataFormat==="channelsFirst"&&o.rank>1){let n=[0];for(let s=2;s<o.rank;++s)n.push(s);n.push(1),o=o.transpose(n)}return LT(o)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};ed.className="Flatten";J.registerClass(ed);var td=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ls(e.activation)}call(e,t){return B(()=>{this.invokeCallHook(e,t);let o=Oe(e);return this.activation.apply(o)})}getConfig(){let e={activation:Ms(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};td.className="Activation";J.registerClass(td);var rd=class extends Me{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 B(()=>(e=Oe(e),PT(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};rd.className="RepeatVector";J.registerClass(rd);var od=class extends Me{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 o="Total size of new array must be unchanged.",n=t.slice(),s=1,a=null;for(let l=0;l<n.length;++l){let u=n[l];if(this.isUnknown(u))if(a===null)a=l;else throw new z("Can only specifiy one unknown dimension.");else s*=u}let i=Xn(e);if(a!==null){if(s===0||i%s!=0)throw new z(o);n[a]=i/s}else if(i!==s)throw new z(o);return n}computeOutputShape(e){let t=!1;for(let o=0;o<e.length;++o)if(this.isUnknown(e[o])){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 B(()=>{this.invokeCallHook(e,t);let o=Oe(e),n=o.shape,s=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return o.reshape(s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};od.className="Reshape";J.registerClass(od);var nd=class extends Me{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=Pr(1,e.dims.length+1);if(!y.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 Et({ndim:this.dims.length+1})]}computeOutputShape(e){e=Qe(e);let t=e.slice();return this.dims.forEach((o,n)=>{t[n+1]=e[o]}),t}call(e,t){return Ue(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};nd.className="Permute";J.registerClass(nd);var sd=class extends Me{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 o=Oe(e),n=-1;return nl(Gn(o,this.maskValue),n)}call(e,t){return B(()=>{this.invokeCallHook(e,t);let o=Oe(e),n=-1,s=!0,a=nl(Gn(o,this.maskValue),n,s);return o.mul(a.asType(o.dtype))})}};sd.className="Masking";J.registerClass(sd);var id=class extends Me{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(yt(e.inputLength))}this.inputDim=e.inputDim,Wt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Wt(this.outputDim,"outputDim"),this.embeddingsInitializer=dt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=bt(e.embeddingsRegularizer),this.activityRegularizer=bt(e.activityRegularizer),this.embeddingsConstraint=Pt(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 B(()=>this.maskZero?(e=Oe(e),Gn(e,Ie(e))):null)}computeOutputShape(e){if(e=Qe(e),this.inputLength==null)return[...e,this.outputDim];let t=yt(this.inputLength);if(t.length!==e.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let o=0;for(let n=0;n<t.length;++n){let s=t[n],a=e[n+1];if(s!=null&&a!=null&&s!==a)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[o]=a),o++}}return[e[0],...t,this.outputDim]}call(e,t){return B(()=>{this.invokeCallHook(e,t);let o=Oe(e);return o.dtype!=="int32"&&(o=Ma(o,"int32")),Ag(this.embeddings.read(),o.as1D()).reshape(Qe(this.computeOutputShape(o.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:It(this.embeddingsInitializer),embeddingsRegularizer:lt(this.embeddingsRegularizer),activityRegularizer:lt(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};id.className="Embedding";J.registerClass(id);var wl=class extends Me{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ne}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 o=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let s=e[e.length-t.length+n],a=t[n];if(s==null||a==null||s<0||a<0)o.push(null);else if(s===1)o.push(a);else if(a===1)o.push(s);else{if(s!==a)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));o.push(s)}}return o}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Qe(e)]),e=e,e.length<2)throw new z(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let s of e)s!=null&&s[0]!==null&&t.push(s[0]);if(t=Kn(t),t.length>1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let o=e[0]==null?null:e[0].slice(1);for(let s=1;s<e.length;++s){let a=e[s]==null?null:e[s].slice(1);o=this.computeElementwiseOpOutputShape(o,a)}let n=e.map(s=>s.length);e.indexOf(null)===-1&&Kn(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return B(()=>{if(e=e,this.reshapeRequired){let o=[],n=e.map(s=>s.rank);if(n.indexOf(null)===-1){let s=Os(n);for(let a of e){let i=a.rank;for(let l=0;l<s-i;++l)a=La(a,1);o.push(a)}return this.mergeFunction(o)}else{let s=!1;for(let l of e){let u=l.rank;if(u==null){let c=l.shape,p=c[0],m=c.slice(1).concat([p]),f=l.reshape([p].concat(Xn(c.slice(1))));f=Ue(f,[1,0]),f=f.reshape(m),o.push(f),s=!0}else if(u>1){let c=Pr(1,u).concat([0]);o.push(Ue(l,c)),s=!0}else o.push(l)}let a=this.mergeFunction(o),i=a.rank;if(s){if(i==null){let l=a.shape,u=l.length,c=l[u-1],p=[c].concat(l.slice(0,l.length-1));a=Ue(a.reshape([-1,c]),[1,0]).reshape(p)}else if(i>1){let l=[i-1].concat(Pr(0,i-1));a=Ue(a,l)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let s=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let o=[];for(let n of e)n!=null&&n[0]!==null&&o.push(n[0]);return o=Kn(o),o.length===1?t=o.concat(t):t=[null].concat(t),t}computeMask(e,t){return B(()=>{if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an Array");if(!Array.isArray(e))throw new z("`inputs` should be an Array");if(t.length!==e.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:sr(n,0));let o=t[0];for(let n=1;n<t.length-1;++n)o=fr(o,t[n]);return o})}},ad=class extends wl{constructor(e){super(e)}mergeFunction(e){return B(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=Q(t,e[o]);return t})}};ad.className="Add";J.registerClass(ad);var ld=class extends wl{constructor(e){super(e)}mergeFunction(e){return B(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=O(t,e[o]);return t})}};ld.className="Multiply";J.registerClass(ld);var ud=class extends wl{constructor(e){super(e)}mergeFunction(e){return B(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=Q(t,e[o]);return O(1/e.length,t)})}};ud.className="Average";J.registerClass(ud);var cd=class extends wl{constructor(e){super(e)}mergeFunction(e){return B(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=qr(t,e[o]);return t})}};cd.className="Maximum";J.registerClass(cd);var pd=class extends wl{constructor(e){super(e)}mergeFunction(e){return B(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=As(t,e[o]);return t})}};pd.className="Minimum";J.registerClass(pd);var md=class extends wl{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 z("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let n of e)if(n!=null){t=!1;break}if(t)return;let o=[];for(let n=0;n<e.length;++n){let s=e[n].slice();s.splice(this.axis,1);let a=!1;for(let i of o)if(y.arraysEqual(i,s)){a=!0;break}a||o.push(s)}if(o.length>1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};md.className="Concatenate";J.registerClass(md);function fd(r,e){for(;r<0;)r+=e;return r}function Aq(r,e,t){if(r.shape.length>3||e.shape.length>3)throw new Ne("batchDot is not implemented for tensors of 4D or higher rank yet");if(y.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`),typeof t=="number"&&(t=[t,t]),r.dtype==="complex64"||e.dtype==="complex64")throw new Ne("batchDot is not implemented for complex64-type Tensors yet.");let o=r.shape.length,n=e.shape.length;t==null&&(t=[o-1,n-2]);let s=t;return B(()=>{let a;if(o>n){a=o-n;let l=[];for(let u=0;u<a;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else if(n>o){a=n-o;let l=[];for(let u=0;u<a;++u)l.push(1);r=r.reshape(r.shape.concat(l))}else a=0;let i;if(r.shape.length===2&&e.shape.length===2)s[0]===s[1]?i=r.mul(e).sum(s[0]):i=r.transpose([1,0]).mul(e).sum(s[1]);else{let l=s[0]!==r.shape.length-1,u=s[1]===e.shape.length-1;i=r.matMul(e,l,u)}if(a>0){let l;o>n?l=o+n-3:l=o-1;let u=[];for(let c=l;c<l+a;++c)u.push(c);i=i.squeeze(u)}return i.shape.length===1&&(i=i.expandDims(1)),i})}var dd=class extends wl{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){y.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],o=e[1];if(t.length>3||o.length>3)throw new Ne("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);if(t[n[0]]!==o[n[1]])throw new z(`Dimension incompatibility: ${t[n[0]]} !== ${o[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],o=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((s,a)=>fd(s,e[a].shape.length)):n=[fd(this.axes,t.shape.length),fd(this.axes,o.shape.length)],this.normalize&&(t=Sf(t,n[0]),o=Sf(o,n[1])),Aq(t,o,n)}interpretAxes(e,t){let o;return Array.isArray(this.axes)?o=this.axes:o=[fd(this.axes,e.length),fd(this.axes,t.length)],o}computeOutputShape(e){y.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(),o=e[1].slice();if(t.length>3||o.length>3)throw new Ne("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);t.splice(n[0],1),o.splice(n[1],1),o.splice(0,1);let s=t.concat(o);return s.length===1&&s.push(1),s}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};dd.className="Dot";J.registerClass(dd);var hd=class extends Me{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 B(()=>{this.invokeCallHook(e,t);let o=Oe(e);return cl(()=>mp(o.shape,0,this.stddev).add(o),()=>o,t.training||!1)})}};hd.className="GaussianNoise";J.registerClass(hd);var gd=class extends Me{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 B(()=>{this.invokeCallHook(e,t);let o=Oe(e);return this.rate>0&&this.rate<1?cl(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return o.mul(mp(o.shape,1,s))},()=>o,t.training||!1):o})}};gd.className="GaussianDropout";J.registerClass(gd);var xd=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(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 B(()=>{if(this.rate<1&&this.rate>0){let o=this._getNoiseShape(e);return cl(()=>{let s=Oe(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=to(Ds(o),this.rate);u=Ma(u,"float32");let c=((1-this.rate)*(1+this.rate*l**2))**-.5,p=-c*l*this.rate;return s.mul(u).add(u.add(-1).mul(l)).mul(c).add(p)},()=>Oe(e),t.training||!1)}return e})}};xd.className="AlphaDropout";J.registerClass(xd);function yd(r,e,t,o,n,s=.001){let a;if(r.rank===2)a=c_(r,e,t,o,n,s);else if(r.rank===3)a=p_(r,e,t,o,n,s);else if(r.rank===4)a=m_(r,e,t,o,n,s);else throw new Ne(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function Dq(r,e,t,o,n=.001){return B(()=>{let s=Yc(r,o),a=s.mean,i=s.variance;return[yd(r,a,i,t,e,n),a,i]})}function $q(r,e,t,o,n=.001){return B(()=>{let s=Yc(r,o),a=s.mean,i=s.variance,l=[];for(let d of Pr(0,r.rank))o.indexOf(d)!==-1?l.push(1):l.push(r.shape[d]);let u=a.reshape(l),c=i.reshape(l),p=e==null?null:e.reshape(l),m=t==null?null:t.reshape(l);return[yd(r,u,c,m,p,n),a,i]})}function Rq(r,e,t,o,n=.001){return y.arraysEqual(o.slice().sort(),Pr(0,r.rank-1))?Dq(r,e,t,o,n):$q(r,e,t,o,n)}var bd=class extends Me{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=dt(e.betaInitializer||"zeros"),this.gammaInitializer=dt(e.gammaInitializer||"ones"),this.movingMeanInitializer=dt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=dt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer)}build(e){e=Qe(e);let t=this.axis>=0?this.axis:this.axis+e.length,o=e[t];if(o==null)throw new z(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Et({ndim:e.length,axes:{[t]:o}})];let n=[o];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return B(()=>{let o=t.training==null?!1:t.training,n=Oe(e),s=n.shape,a=s.length,i=Pr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=jn(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!y.arraysEqual(c,Pr(0,a).slice(0,a-1)),m=()=>{if(p){let b=this.movingMean.read().reshape(u),_=this.movingVariance.read().reshape(u),w=this.center?this.beta.read().reshape(u):null,v=this.scale?this.gamma.read().reshape(u):null;return yd(n,b,_,w,v,this.epsilon)}else return yd(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!o)return m();let[f,d,h]=Rq(n,this.gamma.read(),this.beta.read(),i,this.epsilon),g=(b,_,w)=>{B(()=>{let v=1-w,$=b.read(),A=$.sub(_).mul(v);b.write($.sub(A))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:It(this.betaInitializer),gammaInitializer:It(this.gammaInitializer),movingMeanInitializer:It(this.movingMeanInitializer),movingVarianceInitializer:It(this.movingVarianceInitializer),betaRegularizer:lt(this.betaRegularizer),gammaRegularizer:lt(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};bd.className="BatchNormalization";J.registerClass(bd);var _d=class extends Me{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=dt(e.betaInitializer||"zeros"),this.gammaInitializer=dt(e.gammaInitializer||"ones"),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Qe(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==Kn(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let o=this.axis.map(s=>e[s]),n=!0;this.scale?this.gamma=this.addWeight("gamma",o,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",o,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let o=Oe(e),n=o.shape,s=n.length;return B(()=>{let a=!0,{mean:i,variance:l}=Yc(o,this.axis,a),u=jn(1,s);for(let h of this.axis)u[h]=n[h];let c=h=>h!=null&&h.shape.length!==s&&this.axis!==[s-1]?h.reshape(u):h,p=c(this.gamma.read()),m=c(this.beta.read()),f=[],d=[];for(let h=0;h<s;++h)this.axis.indexOf(h)!==-1?(f.push(n[h]),d.push(1)):(f.push(1),d.push(n[h]));return i=i.tile(f),l=l.tile(f),p=p.tile(d),m=m.tile(d),yd(o,i,l,m,p,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:It(this.betaInitializer),gammaInitializer:It(this.gammaInitializer),betaRegularizer:lt(this.betaRegularizer),gammaRegularizer:lt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};_d.className="LayerNormalization";J.registerClass(_d);function Fq(r,e,t){return B(()=>{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(e==null&&(e=[[1,1],[1,1]]),e.length!==2||e[0].length!==2||e[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(t==null&&(t=Kr()),t!=="channelsLast"&&t!=="channelsFirst")throw new z(`Unknown data format: ${t}. 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length-${e.padding[1].length} array.`);o=e.padding[1]}this.padding=[t,o]}this.inputSpec=[new Et({ndim:4})]}computeOutputShape(e){e=Qe(e);let t,o;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?o=e[3]+this.padding[1][0]+this.padding[1][1]:o=null,[e[0],e[1],t,o]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?o=e[2]+this.padding[1][0]+this.padding[1][1]:o=null,[e[0],t,o,e[3]])}call(e,t){return B(()=>Fq(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};wd.className="ZeroPadding2D";J.registerClass(wd);function ex(r,e,t,o,n,s){return B(()=>{Rt(n),cw(s),Xr(o),t==null&&(t=[1,1]),o==null&&(o="valid"),n==null&&(n=Kr()),s==null&&(s="max"),r=Gf(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=Ea(r,e,t,i):a=va(r,e,t,i),n==="channelsFirst"&&(a=Ue(a,[0,3,1,2])),a})}function I1(r,e,t,o,n,s){return B(()=>{Rt(n),cw(s),Xr(o),t==null&&(t=[1,1,1]),o==null&&(o="valid"),n==null&&(n=Kr()),s==null&&(s="max"),r=zw(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=jm(r,e,t,i):a=Fm(r,e,t,i),n==="channelsFirst"&&(a=Ue(a,[0,4,1,2,3])),a})}var Uw=class extends Me{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 z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Wt(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 z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Wt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Xr(this.padding),this.inputSpec=[new Et({ndim:3})]}computeOutputShape(e){e=Qe(e);let t=io(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return B(()=>{this.invokeCallHook(e,t),e=La(Oe(e),2);let o=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return wo(o,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},kd=class extends Uw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Xr(n),ex(e,t,o,n,s,"max")}};kd.className="MaxPooling1D";J.registerClass(kd);var vd=class extends Uw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Xr(n),ex(e,t,o,n,s,"avg")}};vd.className="AveragePooling1D";J.registerClass(vd);var jw=class extends Me{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 z(`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];Wt(this.poolSize,"poolSize"),Wt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),Xr(this.padding),this.inputSpec=[new Et({ndim:4})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=io(t,this.poolSize[0],this.padding,this.strides[0]),o=io(o,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o]:[e[0],t,o,e[3]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(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}},Cd=class extends jw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Xr(n),ex(e,t,o,n,s,"max")}};Cd.className="MaxPooling2D";J.registerClass(Cd);var Id=class extends jw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Xr(n),ex(e,t,o,n,s,"avg")}};Id.className="AveragePooling2D";J.registerClass(Id);var Hw=class extends Me{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 z(`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];Wt(this.poolSize,"poolSize"),Wt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),Xr(this.padding),this.inputSpec=[new Et({ndim:5})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=io(t,this.poolSize[0],this.padding,this.strides[0]),o=io(o,this.poolSize[1],this.padding,this.strides[1]),n=io(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o,n]:[e[0],t,o,n,e[4]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(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}},Nd=class extends Hw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Xr(n),I1(e,t,o,n,s,"max")}};Nd.className="MaxPooling3D";J.registerClass(Nd);var Sd=class extends Hw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Xr(n),I1(e,t,o,n,s,"avg")}};Sd.className="AveragePooling3D";J.registerClass(Sd);var qw=class extends Me{constructor(e){super(e);this.inputSpec=[new Et({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ne}},Td=class extends qw{constructor(e){super(e||{})}call(e,t){return B(()=>{let o=Oe(e);return ht(o,1)})}};Td.className="GlobalAveragePooling1D";J.registerClass(Td);var Ed=class extends qw{constructor(e){super(e||{})}call(e,t){return B(()=>{let o=Oe(e);return ar(o,1)})}};Ed.className="GlobalMaxPooling1D";J.registerClass(Ed);var Kw=class extends Me{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.inputSpec=[new Et({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ne}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ad=class extends Kw{call(e,t){return B(()=>{let o=Oe(e);return this.dataFormat==="channelsLast"?ht(o,[1,2]):ht(o,[2,3])})}};Ad.className="GlobalAveragePooling2D";J.registerClass(Ad);var Dd=class extends Kw{call(e,t){return B(()=>{let o=Oe(e);return this.dataFormat==="channelsLast"?ar(o,[1,2]):ar(o,[2,3])})}};Dd.className="GlobalMaxPooling2D";J.registerClass(Dd);var Xw=class extends Me{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,o={}){let n=t.layer,s=Yr(n,o);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},$d=class extends Xw{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Qe(e),e.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=Qe(e);let t=[e[0]].concat(e.slice(2)),o=this.layer.computeOutputShape(t),n=e[1];return[o[0],n].concat(o.slice(1))}call(e,t){return B(()=>(e=Oe(e),Gw((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};$d.className="TimeDistributed";J.registerClass($d);function Oq(r){Gi(AT,"BidirectionalMergeMode",r)}var Pq="concat",Rd=class extends Xw{constructor(e){super(e);let t=e.layer.getConfig(),o={};o.className=e.layer.getClassName(),o.config=t,this.forwardLayer=Yr(o),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=Yr(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Pq:e.mergeMode,Oq(this.mergeMode),e.weights)throw new Ne("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,o=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,o)),this.backwardLayer.setWeights(e.slice(o))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let o,n,s;return this.returnState&&(s=t.slice(1)),o=t[0],o=o,this.mergeMode==="concat"?(o[o.length-1]*=2,n=[o]):this.mergeMode==null?n=[o,o.slice()]:n=[o],this.returnState?this.mergeMode==null?n.concat(s).concat(s.slice()):[o].concat(s).concat(s.slice()):dr(n)}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=Vw(e,o,n,this.numConstants);if(e=s.inputs,o=s.initialState,n=s.constants,Array.isArray(e)&&(o=e.slice(1),e=e[0]),(o==null||o.length===0)&&n==null)return super.apply(e,t);let a=[],i=[];if(o!=null){let u=o.length;if(u%2>0)throw new z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=o,a.push(...o);let c=o.map(p=>new Et({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,u/2),this.backwardLayer.stateSpec=c.slice(u/2),i.push(...c)}if(n!=null)throw new Ne("Support for constants in Bidirectional layers is not implemented yet.");let l=a[0]instanceof Lr;for(let u of a)if(u instanceof Lr!==l)throw new z("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(l){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return B(()=>{let o=t.initialState,n,s;if(o==null)n=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let l=o.slice(0,o.length/2),u=o.slice(o.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:l})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(n)&&(a=n.slice(1).concat(s.slice(1))),n=n[0],s=s[0]),this.returnSequences&&(s=qt(s,1));let i;return this.mergeMode==="concat"?i=pp([n,s]):this.mergeMode==="sum"?i=Q(n,s):this.mergeMode==="ave"?i=O(.5,Q(n,s)):this.mergeMode==="mul"?i=O(n,s):this.mergeMode==null&&(i=[n,s]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Fs(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Fs(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let o;if(this.returnSequences?this.mergeMode==null?o=[t,t]:o=t:this.mergeMode==null?o=[null,null]:o=null,this.returnState){let s=this.forwardLayer.states.map(a=>null);return Array.isArray(o)?o.concat(s).concat(s):[o].concat(s).concat(s)}else return o}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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z1=(r,e,t)=>{switch(r.op){case"BiasAdd":case"AddV2":case"Add":return[Q(C("a",r,e,t),C("b",r,e,t))];case"AddN":return[i_(C("tensors",r,e,t))];case"FloorMod":case"Mod":return[qm(C("a",r,e,t),C("b",r,e,t))];case"Mul":return[O(C("a",r,e,t),C("b",r,e,t))];case"RealDiv":case"Div":return[de(C("a",r,e,t),C("b",r,e,t))];case"DivNoNan":return[zm(C("a",r,e,t),C("b",r,e,t))];case"FloorDiv":return[_u(C("a",r,e,t),C("b",r,e,t))];case"Sub":return[ue(C("a",r,e,t),C("b",r,e,t))];case"Minimum":return[As(C("a",r,e,t),C("b",r,e,t))];case"Maximum":return[qr(C("a",r,e,t),C("b",r,e,t))];case"Pow":return[Rr(C("a",r,e,t),C("b",r,e,t))];case"SquaredDifference":return[Vu(C("a",r,e,t),C("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var 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o=C("elementShape",r,e,t),n=C("elementDType",r,e,t),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let a=C(s,r,e,t),i=z1(o,n,a);return t.addTensorList(i),[i.idTensor]}case"TensorListGather":{let o=C("tensorListId",r,e,t),n=C("indices",r,e,t),s=C("elementShape",r,e,t),a=C("elementDType",r,e,t);return[t.getTensorList(o.id).gather(n,a,s)]}case"TensorListStack":{let o=C("tensorListId",r,e,t),n=C("elementShape",r,e,t),s=C("elementDType",r,e,t),a=C("numElements",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case"TensorListFromTensor":{let o=C("tensor",r,e,t),n=C("elementShape",r,e,t),s=C("elementDType",r,e,t),a=L1(o,n,s);return t.addTensorList(a),[a.idTensor]}case"TensorListConcat":{let o=C("tensorListId",r,e,t),n=t.getTensorList(o.id),s=C("dtype",r,e,t),a=C("elementShape",r,e,t);return[n.concat(s,a)]}case"TensorListPushBack":{let o=C("tensorListId",r,e,t),n=C("tensor",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case"TensorListPopBack":{let o=C("tensorListId",r,e,t),n=C("elementShape",r,e,t),s=C("elementDType",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case"TensorListSplit":{let o=C("tensor",r,e,t),n=C("elementShape",r,e,t),s=C("lengths",r,e,t),a=V1(o,s,n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function W1(r,e,t){let[o,n]=C("fusedOps",r,e,t),s=o==="biasadd",a=n==="prelu",i=o==="fusedbatchnorm",l=C("numArgs",r,e,t);if(s){if(a&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(i)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let u=C("strides",r,e,t),c=Dd(r,e,t),p=C("dataFormat",r,e,t).toUpperCase(),m=C("dilations",r,e,t),[f,d]=C("args",r,e,t),h=C("leakyreluAlpha",r,e,t);return{stride:u,pad:c,dataFormat:p,dilations:m,biasArg:f,preluArg:d,activationFunc:n,leakyreluAlpha:h}}var U1=(r,e,t)=>{switch(r.op){case"Conv1D":{let o=C("stride",r,e,t),n=C("pad",r,e,t),s=C("dataFormat",r,e,t).toUpperCase(),a=C("dilation",r,e,t);return[vu(C("x",r,e,t),C("filter",r,e,t),o,n,s,a)]}case"Conv2D":{let o=C("strides",r,e,t),n=Dd(r,e,t),s=C("dataFormat",r,e,t).toUpperCase(),a=C("dilations",r,e,t);return[jr(C("x",r,e,t),C("filter",r,e,t),[o[1],o[2]],n,s,[a[1],a[2]])]}case"_FusedConv2D":{let{stride:o,pad:n,dataFormat:s,dilations:a,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=W1(r,e,t);return[zn.conv2d({x:C("x",r,e,t),filter:C("filter",r,e,t),strides:[o[1],o[2]],pad:n,dataFormat:s,dilations:[a[1],a[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:o,pad:n,dataFormat:s,dilations:a,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=W1(r,e,t);return[zn.depthwiseConv2d({x:C("x",r,e,t),filter:C("filter",r,e,t),strides:[o[1],o[2]],pad:n,dataFormat:s,dilations:[a[1],a[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let 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o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t),a=C("includeBatchInIndex",r,e,t),{result:i,indexes:l}=C_(C("x",r,e,t),[s[1],s[2]],[o[1],o[2]],n,a);return[i,l]}case"AvgPool3D":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[Dm(C("x",r,e,t),[s[1],s[2],s[3]],[o[1],o[2],o[3]],n)]}case"MaxPool3D":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[Gm(C("x",r,e,t),[s[1],s[2],s[3]],[o[1],o[2],o[3]],n)]}case"Dilation2D":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("dilations",r,e,t),a=o[1],i=o[2],l=s[1],u=s[2];return[Om(C("x",r,e,t),C("filter",r,e,t),[a,i],n,[l,u],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var j1=(r,e,t)=>{switch(r.op){case"Fill":{let o=C("shape",r,e,t),n=C("dtype",r,e,t),s=C("value",r,e,t);return[Na(o,s,n)]}case"LinSpace":{let o=C("start",r,e,t),n=C("stop",r,e,t),s=C("num",r,e,t);return[y_(o,n,s)]}case"Multinomial":{let 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o=C("boxes",r,e,t),n=C("scores",r,e,t),s=C("maxOutputSize",r,e,t),a=C("iouThreshold",r,e,t),i=C("scoreThreshold",r,e,t),l=C("softNmsSigma",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:l}}var H1=async(r,e,t)=>{switch(r.op){case"NonMaxSuppressionV5":{let{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:l}=bk(r,e,t),u=await Ds.nonMaxSuppressionWithScoreAsync(o,n,s,a,i,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i}=bk(r,e,t),l=C("padToMaxOutputSize",r,e,t),u=await Ds.nonMaxSuppressionPaddedAsync(o,n,s,a,i,l);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i}=bk(r,e,t);return[await Ds.nonMaxSuppressionAsync(o,n,s,a,i)]}case"Where":{let o=oe(C("condition",r,e,t),"bool"),n=[await rf(o)];return 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Z1=(r,e,t)=>{switch(r.op){case"Equal":return[_o(C("a",r,e,t),C("b",r,e,t))];case"NotEqual":return[Ln(C("a",r,e,t),C("b",r,e,t))];case"Greater":return[Qt(C("a",r,e,t),C("b",r,e,t))];case"GreaterEqual":return[to(C("a",r,e,t),C("b",r,e,t))];case"Less":return[Tu(C("a",r,e,t),C("b",r,e,t))];case"LessEqual":return[Oo(C("a",r,e,t),C("b",r,e,t))];case"LogicalAnd":return[fr(C("a",r,e,t),C("b",r,e,t))];case"LogicalNot":return[Ta(C("a",r,e,t))];case"LogicalOr":return[Du(C("a",r,e,t),C("b",r,e,t))];case"Select":case"SelectV2":return[Dt(C("condition",r,e,t),C("a",r,e,t),C("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var J1=(r,e,t)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[We(C("a",r,e,t),C("b",r,e,t),C("transposeA",r,e,t),C("transposeB",r,e,t))];case"Transpose":return[Ue(C("x",r,e,t),C("perm",r,e,t))];case"_FusedMatMul":let[o,n]=C("fusedOps",r,e,t),s=o==="biasadd",a=n==="prelu",i=C("numArgs",r,e,t),l=C("leakyreluAlpha",r,e,t);if(s){if(a&&i!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&i!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=C("args",r,e,t);return[zn.matMul({a:C("a",r,e,t),b:C("b",r,e,t),transposeA:C("transposeA",r,e,t),transposeB:C("transposeB",r,e,t),bias:u,activation:n,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var Q1=(r,e,t)=>{switch(r.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[On(C("x",r,e,t),C("mean",r,e,t),C("variance",r,e,t),C("offset",r,e,t),C("scale",r,e,t),C("epsilon",r,e,t))];case"FusedBatchNormV3":return[On(C("x",r,e,t),C("mean",r,e,t),C("variance",r,e,t),C("offset",r,e,t),C("scale",r,e,t),C("epsilon",r,e,t))];case"LRN":return[zm(C("x",r,e,t),C("radius",r,e,t),C("bias",r,e,t),C("alpha",r,e,t),C("beta",r,e,t))];case"Softmax":return[$a(C("x",r,e,t))];case"LogSoftmax":return[Au(C("x",r,e,t))];case"SparseToDense":return[of(C("sparseIndices",r,e,t),C("outputShape",r,e,t),C("sparseValues",r,e,t),C("defaultValue",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var eE=(r,e,t)=>{switch(r.op){case"Max":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ar(C("x",r,e,t),a,i)]}case"Mean":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ht(C("x",r,e,t),a,i)]}case"Min":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[Li(C("x",r,e,t),a,i)]}case"Sum":{let 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oE=(r,e,t)=>{switch(r.op){case"Cast":return[oe(C("x",r,e,t),C("dtype",r,e,t))];case"ExpandDims":{let o=C("axis",r,e,t);return[sr(C("x",r,e,t),o)]}case"Squeeze":{let o=C("axis",r,e,t);return[wo(C("x",r,e,t),o)]}case"Reshape":return[L(C("x",r,e,t),C("shape",r,e,t))];case"MirrorPad":return[Wm(C("x",r,e,t),C("padding",r,e,t),C("mode",r,e,t))];case"PadV2":case"Pad":return[$r(C("x",r,e,t),C("padding",r,e,t),C("constantValue",r,e,t))];case"SpaceToBatchND":{let o=C("blockShape",r,e,t),n=C("paddings",r,e,t);return[Aa(C("x",r,e,t),o,n)]}case"BatchToSpaceND":{let o=C("blockShape",r,e,t),n=C("crops",r,e,t);return[Ca(C("x",r,e,t),o,n)]}case"DepthToSpace":{let o=C("blockSize",r,e,t),n=C("dataFormat",r,e,t).toUpperCase();return[Fm(C("x",r,e,t),o,n)]}case"BroadcastTo":return[il(C("x",r,e,t),C("shape",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function wk(r,e,t,o){let n=((s,a,i)=>{switch(s.category){case"arithmetic":return V(()=>P1(s,a,i));case"basic_math":return 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this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function vk(r,e,t,o){let n=new Set,s=[],a=null,i=null,l=new Set,u=Object.keys(r).map(m=>Zr(m)[0]),c=[];o!=null&&(c=o.map(m=>Zr(m.name)[0]));let p=[...e];for(;p.length>0;){let m=p.pop();if((kk(m)||I6(m)||N6(m))&&a==null&&(a=m,i=a.children.map(f=>f.name).filter(f=>n.has(f))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{l.has(f.name)||(l.add(f.name),p.push(f))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function 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c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[l]=Ms(i.name,o);s[l]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!hr(u,n,o))&&(s[l]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!hr(u,n,o))&&(s[l]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Zr(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((l,u)=>a[u]===-1||a[u]===l);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){let t={};for(let o in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[o]!=null){let n=this._signature.inputs[o];t[n.name]=e[o]}else t[o]=e[o];return t}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Zr(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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[o]=Zr(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var Ck=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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t=vr.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(vr.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,o;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?o=this.artifacts.userDefinedMetadata.signature:o=this.artifacts.signature,this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=vr.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new 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this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new _E(this,e,t)}columnMajorBatch(e,t=!0,o=Ik){return this.rowMajorBatch(e,t).map(s=>lE(s,o))}concatenate(e,t){return new Sk(Nk([this,e]),t)}take(e){return e<0||e==null?this:new bE(this,e)}skip(e){return e<0||e==null?this:new yE(this,e)}prefetch(e){return new Ek(this,e)}shuffle(e,t){return new IE(this,e,t)}serial(){return new xE(this)}},mE=class extends Kt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:cE(e),done:!1}}},fE=class extends Kt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},xE=class extends Kt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},yE=class extends Kt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Te(e.value)}return this.upstream.next()}},bE=class extends Kt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},_E=class extends Kt{constructor(e,t,o=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=o,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},wE=class extends Kt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Te(e.value)}}},kE=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Fn.getTensorsInContainer(e.value),o=this.transform(e.value),n=Fn.getTensorsInContainer(o);for(let s of t)Fn.isTensorInList(s,n)||s.dispose();return{value:o,done:!1}}},vE=class extends Kt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Tk=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Fn.getTensorsInContainer(e.value),o=await this.transform(e.value),n=Fn.getTensorsInContainer(o);for(let s of t)Fn.isTensorInList(s,n)||s.dispose();return{value:o,done:!1}}},Rp=class extends Kt{constructor(){super();this.outputQueue=new $p,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},CE=class extends Rp{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Fn.getTensorsInContainer(e.value),o=this.transform(e.value),n=Fn.getTensorsInContainer(o);this.outputQueue.pushAll(o);for(let s of t)Fn.isTensorInList(s,n)||s.dispose();return!0}},Sk=class extends Kt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let o=await this.moreIterators.next();if(o.done)return{value:null,done:!0};this.iterator=o.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Va;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Va||(Va={}));var hE=class extends Kt{constructor(e,t=Va.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,o=0;function n(a){return a instanceof 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-      ${e}`);let n;return this.size===Infinity||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),lo(async()=>(await o.iterator()).columnMajorBatch(e,t,F6),n)}concatenate(e){let t=this,o;return this.size===Infinity||e.size===Infinity?o=Infinity:this.size!=null&&e.size!=null?o=this.size+e.size:o=null,lo(async()=>(await t.iterator()).concatenate(await e.iterator()),o)}filter(e){let t=this,o;return this.size===Infinity?o=Infinity:o=null,lo(async()=>(await t.iterator()).filter(n=>V(()=>e(n))),o)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return lo(async()=>(await t.iterator()).map(o=>V(()=>e(o))),this.size)}mapAsync(e){let t=this;return lo(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return lo(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,o;return this.size!=null&&e>0?o=this.size*e:e===0?o=0:this.size!=null&&(e===void 0||e<0)?o=Infinity:o=null,lo(async()=>{let n=Rd(async()=>({value:await t.iterator(),done:!1}));return dE(n.take(e))},o)}skip(e){let t=this,o;return this.size!=null&&e>=0&&this.size>=e?o=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?o=0:o=null,lo(async()=>(await t.iterator()).skip(e),o)}shuffle(e,t,o=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,s=NE.alea(t||y.now().toString());return lo(async()=>{let a=s.int32();return o&&(a+=s.int32()),(await n.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,o;return this.size!=null&&this.size>e?o=e:this.size!=null&&this.size<=e?o=this.size:o=null,lo(async()=>(await t.iterator()).take(e),o)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};qi.MAX_BUFFER_SIZE=1e4;function lo(r,e=null){return new class extends qi{constructor(){super(...arguments);this.size=e}async iterator(){return r()}}}function SE(r){return lo(async()=>Nk(r),r.length)}function TE(r){if(!kl(r))throw new Error("The argument to zip() must be an object or array.");let e;if(Array.isArray(r))for(let t=0;t<r.length;t++)e=e==null?r[t].size:Math.min(e,r[t].size);else if(r instanceof Object)for(let t in r)e=e==null?r[t].size:Math.min(e,r[t].size);return lo(async()=>{let t=await gx(r,o=>{if(o instanceof qi)return{value:o.iterator(),recurse:!1};if(kl(o))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return gE(t,Va.SHORTEST)},e)}function F6(r){if(r===null)return null;let e=r[0];return uE(e)?{value:O6(r),recurse:!1}:{value:null,recurse:!0}}function O6(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Ve?Bt(r):Dr(r)}var Fd=class extends qi{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
-`).map(n=>(n.endsWith("\r")&&(n=n.slice(0,-1)),n))}};var xx='"',Od=Symbol("out"),EE=Symbol("field"),yx=Symbol("quote"),Ak=Symbol("quoteafterquote"),AE=Symbol("quoteinquote"),Pd=class extends qi{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new Fd(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(y.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&y.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((n,s)=>(n[s]=n[s]+1||1,n),{}),o=Object.keys(t).filter(n=>t[n]>1);if(y.assert(o.length===0,()=>"Duplicate column names found: "+o.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let o=t.value;return this.parseRow(o,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),o={},n={};for(let s=0;s<this.fullColumnNames.length;s++){let a=this.fullColumnNames[s],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let l=t[s],u=null;if(l==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let c=Number(l);if(isNaN(c))i&&i.dtype==="bool"?u=this.getBoolean(l):u=l;else if(!i||!i.dtype)u=c;else switch(i.dtype){case"float32":u=c;break;case"int32":u=Math.floor(c);break;case"bool":u=this.getBoolean(l);break;default:u=c}}i&&i.isLabel?n[a]=u:o[a]=u}}return Object.keys(n).length===0?o:{xs:o,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let o=[],n=0,s=e.length,a=Od;for(let i=0;i<s;i++)switch(a){case Od:switch(e.charAt(i)){case xx:n=i+1,a=yx;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;o.push(""),a=Od;break;default:a=EE,n=i;break}break;case EE:switch(e.charAt(i)){case this.delimiter:o.push(e.substring(n,i)),a=Od,n=i+1;break;default:}break;case yx:switch(e.charAt(i)){case xx:a=Ak;break;default:}break;case Ak:switch(e.charAt(i)){case this.delimiter:o.push(e.substring(n,i-1)),a=Od,n=i+1;break;case xx:a=yx;break;default:a=AE;break}break;case AE:switch(e.charAt(i)){case xx:a=yx;break;default:}break;default:}if(a===Ak?o.push(e.substring(n,s-1)):o.push(e.substring(n)),t&&o.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${o}`);return o}};var Md=class extends Kt{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(G().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Md(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(o){throw new Error(`Error thrown while initializing video stream: ${o.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,o=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(o.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(o.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[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=[],o=0;return new Promise(n=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++o===this.numFrames&&(clearInterval(s),n({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,o=new Float32Array(e.length*t);return e.forEach((n,s)=>o.set(n,s*t)),o}getTensorFromAudioDataArray(e,t){let o=new Float32Array(y.sizeFromShape(t));return o.set(e,o.length-e.length),Dr(o,t)}};var Ld=class extends Kt{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=Vt([0],"int32"),this.webcamConfig.centerCrop){let o=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-o)/2,a=(1-n)/2,i=s+o,l=n+a;this.cropBox=Bi([a,s,l,i],[1,4])}else this.cropBox=Bi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(G().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 o=new Ld(e,t);return await o.start(),o}async start(){this.webcamConfig.facingMode&&y.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=Yh.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 V(()=>{let t=sr(oe(e,"float32"),0),o;o=Ds.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=o.shape;return L(o,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var zd=class{};var bx=class extends Kt{split(e){return new DE(this,e)}},DE=class extends bx{constructor(e,t){super();this.upstream=e,this.impl=new $E(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},$E=class extends Rp{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 o of t.slice(0,-1))this.outputQueue.push(o);return this.carryover=t[t.length-1],!0}};var Dk=class extends Kt{decodeUTF8(){return new FE(this)}},FE=class extends bx{constructor(e){super();this.upstream=e,this.impl=new OE(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},OE=class extends Rp{constructor(e){super();if(this.upstream=e,G().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=RE();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 o;return G().get("IS_BROWSER")?o=this.decoder.decode(t,{stream:!0}):o=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(o),!0}};var Bd=class extends Dk{constructor(e,t={}){super();this.file=e,this.options=t,y.assert(e instanceof Uint8Array||(G().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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,o)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,n)));else{let s=new FileReader;s.onload=i=>{let l=s.result;if(l instanceof 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extends zd{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return _x(this.url)?new Vd(this.url,this.fileOptions).iterator():PE(this.url,this.fileOptions)}};function ME(r,e={}){return new Pd(new Gd(r),e)}function LE(r){let e=Rd(r);return lo(async()=>e)}function zE(r){return lo(async()=>{let e=await r();return Rd(()=>e.next())})}async function BE(r,e){return Ld.create(r,e)}async function VE(r){return Md.create(r)}var wx="3.0.0";function ee(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the CPU backend.`)})}var M6=Sr.whereImpl,Rk=class extends Ws{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Ya(this,vs())}write(e,t,o){this.firstUse&&(this.firstUse=!1,G().get("IS_NODE")&&N.warn(`
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t.functionMap[o].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);let p=u.map(f=>f.id);c.forEach(f=>{!f.kept&&i.indexOf(f.id)===-1&&p.indexOf(f.id)===-1&&f.dispose()});let m=await t.functionMap[n].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);l=await m[0].data(),m.forEach(f=>{!f.kept&&i.indexOf(f.id)===-1&&p.indexOf(f.id)===-1&&f.dispose()})}return u}case"LoopCond":{let o=C("pred",r,e,t);return[Bs(o)]}case"Switch":{let o=C("pred",r,e,t),n=C("data",r,e,t);return n.kept||(n=Bs(n)),(await o.data())[0]?[void 0,n]:[n,void 0]}case"Merge":{let o=r.inputNames.find(n=>hr(n,e,t)!==void 0);if(o){let n=hr(o,e,t);return[Bs(n)]}return}case"Enter":{let o=C("frameName",r,e,t),n=C("tensor",r,e,t);return t.enterFrame(o),[Bs(n)]}case"Exit":{let o=C("tensor",r,e,t);return t.exitFrame(),[Bs(o)]}case"NextIteration":{let o=C("tensor",r,e,t);return t.nextIteration(),[Bs(o)]}case"TensorArrayV3":{let o=C("size",r,e,t),n=C("dtype",r,e,t),s=C("elementShape",r,e,t),a=C("dynamicSize",r,e,t),i=C("clearAfterRead",r,e,t),l=C("identicalElementShapes",r,e,t),u=C("name",r,e,t),c=new vk(u,n,o,s,l,a,i);return t.addTensorArray(c),[c.idTensor,le(1)]}case"TensorArrayWriteV3":{let o=C("tensorArrayId",r,e,t),n=C("index",r,e,t),s=C("tensor",r,e,t),a=t.getTensorArray(o.id);return a.write(n,s),[a.idTensor]}case"TensorArrayReadV3":{let o=C("tensorArrayId",r,e,t),n=C("index",r,e,t);return[t.getTensorArray(o.id).read(n)]}case"TensorArrayGatherV3":{let o=C("tensorArrayId",r,e,t),n=C("indices",r,e,t),s=C("dtype",r,e,t);return[t.getTensorArray(o.id).gather(n,s)]}case"TensorArrayScatterV3":{let o=C("tensorArrayId",r,e,t),n=C("indices",r,e,t),s=C("tensor",r,e,t),a=t.getTensorArray(o.id);return a.scatter(n,s),[a.idTensor]}case"TensorArrayConcatV3":{let o=C("tensorArrayId",r,e,t),n=t.getTensorArray(o.id),s=C("dtype",r,e,t);return[n.concat(s)]}case"TensorArraySplitV3":{let o=C("tensorArrayId",r,e,t),n=C("tensor",r,e,t),s=C("lengths",r,e,t),a=t.getTensorArray(o.id);return a.split(s,n),[a.idTensor]}case"TensorArraySizeV3":{let o=C("tensorArrayId",r,e,t),n=t.getTensorArray(o.id);return[le(n.size(),"int32")]}case"TensorArrayCloseV3":{let o=C("tensorArrayId",r,e,t),n=t.getTensorArray(o.id);return n.clearAndClose(),[n.idTensor]}case"TensorListSetItem":{let o=C("tensorListId",r,e,t),n=C("index",r,e,t),s=C("tensor",r,e,t),a=t.getTensorList(o.id);return a.setItem(n,s),[a.idTensor]}case"TensorListGetItem":{let o=C("tensorListId",r,e,t),n=C("index",r,e,t),s=C("elementShape",r,e,t),a=C("elementDType",r,e,t);return[t.getTensorList(o.id).getItem(n,s,a)]}case"TensorListScatterV2":case"TensorListScatter":{let o=C("indices",r,e,t),n=C("tensor",r,e,t),s=C("elementShape",r,e,t),a=C("numElements",r,e,t),i=U1(n,o,s,a);return t.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let o=C("elementShape",r,e,t),n=C("elementDType",r,e,t),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let a=C(s,r,e,t),i=W1(o,n,a);return t.addTensorList(i),[i.idTensor]}case"TensorListGather":{let o=C("tensorListId",r,e,t),n=C("indices",r,e,t),s=C("elementShape",r,e,t),a=C("elementDType",r,e,t);return[t.getTensorList(o.id).gather(n,a,s)]}case"TensorListStack":{let o=C("tensorListId",r,e,t),n=C("elementShape",r,e,t),s=C("elementDType",r,e,t),a=C("numElements",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case"TensorListFromTensor":{let o=C("tensor",r,e,t),n=C("elementShape",r,e,t),s=C("elementDType",r,e,t),a=G1(o,n,s);return t.addTensorList(a),[a.idTensor]}case"TensorListConcat":{let o=C("tensorListId",r,e,t),n=t.getTensorList(o.id),s=C("dtype",r,e,t),a=C("elementShape",r,e,t);return[n.concat(s,a)]}case"TensorListPushBack":{let o=C("tensorListId",r,e,t),n=C("tensor",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case"TensorListPopBack":{let o=C("tensorListId",r,e,t),n=C("elementShape",r,e,t),s=C("elementDType",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case"TensorListSplit":{let o=C("tensor",r,e,t),n=C("elementShape",r,e,t),s=C("lengths",r,e,t),a=j1(o,s,n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function q1(r,e,t){let[o,n]=C("fusedOps",r,e,t),s=o==="biasadd",a=n==="prelu",i=o==="fusedbatchnorm",l=C("numArgs",r,e,t);if(s){if(a&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(i)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let u=C("strides",r,e,t),c=Fd(r,e,t),p=C("dataFormat",r,e,t).toUpperCase(),m=C("dilations",r,e,t),[f,d]=C("args",r,e,t),h=C("leakyreluAlpha",r,e,t);return{stride:u,pad:c,dataFormat:p,dilations:m,biasArg:f,preluArg:d,activationFunc:n,leakyreluAlpha:h}}var K1=(r,e,t)=>{switch(r.op){case"Conv1D":{let o=C("stride",r,e,t),n=C("pad",r,e,t),s=C("dataFormat",r,e,t).toUpperCase(),a=C("dilation",r,e,t);return[Cu(C("x",r,e,t),C("filter",r,e,t),o,n,s,a)]}case"Conv2D":{let o=C("strides",r,e,t),n=Fd(r,e,t),s=C("dataFormat",r,e,t).toUpperCase(),a=C("dilations",r,e,t);return[jr(C("x",r,e,t),C("filter",r,e,t),[o[1],o[2]],n,s,[a[1],a[2]])]}case"_FusedConv2D":{let{stride:o,pad:n,dataFormat:s,dilations:a,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=q1(r,e,t);return[Wn.conv2d({x:C("x",r,e,t),filter:C("filter",r,e,t),strides:[o[1],o[2]],pad:n,dataFormat:s,dilations:[a[1],a[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:o,pad:n,dataFormat:s,dilations:a,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=q1(r,e,t);return[Wn.depthwiseConv2d({x:C("x",r,e,t),filter:C("filter",r,e,t),strides:[o[1],o[2]],pad:n,dataFormat:s,dilations:[a[1],a[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let 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o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t),a=C("includeBatchInIndex",r,e,t),{result:i,indexes:l}=E_(C("x",r,e,t),[s[1],s[2]],[o[1],o[2]],n,a);return[i,l]}case"AvgPool3D":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[Fm(C("x",r,e,t),[s[1],s[2],s[3]],[o[1],o[2],o[3]],n)]}case"MaxPool3D":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[jm(C("x",r,e,t),[s[1],s[2],s[3]],[o[1],o[2],o[3]],n)]}case"Dilation2D":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("dilations",r,e,t),a=o[1],i=o[2],l=s[1],u=s[2];return[Lm(C("x",r,e,t),C("filter",r,e,t),[a,i],n,[l,u],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var X1=(r,e,t)=>{switch(r.op){case"Fill":{let o=C("shape",r,e,t),n=C("dtype",r,e,t),s=C("value",r,e,t);return[Na(o,s,n)]}case"LinSpace":{let o=C("start",r,e,t),n=C("stop",r,e,t),s=C("num",r,e,t);return[v_(o,n,s)]}case"Multinomial":{let 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o=C("boxes",r,e,t),n=C("scores",r,e,t),s=C("maxOutputSize",r,e,t),a=C("iouThreshold",r,e,t),i=C("scoreThreshold",r,e,t),l=C("softNmsSigma",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:l}}var Y1=async(r,e,t)=>{switch(r.op){case"NonMaxSuppressionV5":{let{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:l}=Ck(r,e,t),u=await Rs.nonMaxSuppressionWithScoreAsync(o,n,s,a,i,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i}=Ck(r,e,t),l=C("padToMaxOutputSize",r,e,t),u=await Rs.nonMaxSuppressionPaddedAsync(o,n,s,a,i,l);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i}=Ck(r,e,t);return[await Rs.nonMaxSuppressionAsync(o,n,s,a,i)]}case"Where":{let o=oe(C("condition",r,e,t),"bool"),n=[await sf(o)];return 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C("x",r,e,t).map(u=>Vt(u.shape));case"Size":return[le(C("x",r,e,t).size,"int32")];case"Rank":return[le(C("x",r,e,t).rank,"int32")];case"NoOp":return[le(1)];case"Print":let s=C("x",r,e,t),a=C("data",r,e,t),i=C("message",r,e,t),l=C("summarize",r,e,t);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(i);for(let u=0;u<a.length;u++)console.log(Array.prototype.slice.call(a[u].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var Ik=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=le(0),this.tensorMap=new Map,Dt(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),B(()=>{let n=ur(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i<s;i++){let l=o[i],u=n[i];Dt(u),this.tensorMap.set(l,u)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return B(()=>{let n=[];for(let s=0;s<o.length;s++){let a=o[s],i=this.findWithDefault(a,t);n.push(i)}return Bt(n)})}findWithDefault(e,t){let o=this.tensorMap.get(e);return o!=null?o:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}};var Q1=async(r,e,t,o)=>{switch(r.op){case"HashTable":case"HashTableV2":{let n=C("keyDType",r,e,t),s=C("valueDType",r,e,t),a=new Ik(n,s);return o.addHashTable(r.name,a),[a.handle]}case"LookupTableImport":case"LookupTableImportV2":{let n=C("tableHandle",r,e,t,o),s=C("keys",r,e,t),a=C("values",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case"LookupTableFind":case"LookupTableFindV2":{let n=C("tableHandle",r,e,t,o),s=C("keys",r,e,t),a=C("defaultValue",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var eE=(r,e,t)=>{switch(r.op){case"ResizeBilinear":{let o=C("images",r,e,t),n=C("size",r,e,t),s=C("alignCorners",r,e,t),a=C("halfPixelCenters",r,e,t);return[Rs.resizeBilinear(o,[n[0],n[1]],s,a)]}case"ResizeNearestNeighbor":{let o=C("images",r,e,t),n=C("size",r,e,t),s=C("alignCorners",r,e,t),a=C("halfPixelCenters",r,e,t);return[Rs.resizeNearestNeighbor(o,[n[0],n[1]],s,a)]}case"CropAndResize":{let o=C("image",r,e,t),n=C("boxes",r,e,t),s=C("boxInd",r,e,t),a=C("cropSize",r,e,t),i=C("method",r,e,t),l=C("extrapolationValue",r,e,t);return[Rs.cropAndResize(o,n,s,a,i,l)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var tE=(r,e,t)=>{switch(r.op){case"Equal":return[_o(C("a",r,e,t),C("b",r,e,t))];case"NotEqual":return[Gn(C("a",r,e,t),C("b",r,e,t))];case"Greater":return[Qt(C("a",r,e,t),C("b",r,e,t))];case"GreaterEqual":return[to(C("a",r,e,t),C("b",r,e,t))];case"Less":return[Eu(C("a",r,e,t),C("b",r,e,t))];case"LessEqual":return[Mo(C("a",r,e,t),C("b",r,e,t))];case"LogicalAnd":return[fr(C("a",r,e,t),C("b",r,e,t))];case"LogicalNot":return[Ta(C("a",r,e,t))];case"LogicalOr":return[$u(C("a",r,e,t),C("b",r,e,t))];case"Select":case"SelectV2":return[$t(C("condition",r,e,t),C("a",r,e,t),C("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var rE=(r,e,t)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[We(C("a",r,e,t),C("b",r,e,t),C("transposeA",r,e,t),C("transposeB",r,e,t))];case"Transpose":return[Ue(C("x",r,e,t),C("perm",r,e,t))];case"_FusedMatMul":let[o,n]=C("fusedOps",r,e,t),s=o==="biasadd",a=n==="prelu",i=C("numArgs",r,e,t),l=C("leakyreluAlpha",r,e,t);if(s){if(a&&i!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&i!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=C("args",r,e,t);return[Wn.matMul({a:C("a",r,e,t),b:C("b",r,e,t),transposeA:C("transposeA",r,e,t),transposeB:C("transposeB",r,e,t),bias:u,activation:n,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var oE=(r,e,t)=>{switch(r.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[zn(C("x",r,e,t),C("mean",r,e,t),C("variance",r,e,t),C("offset",r,e,t),C("scale",r,e,t),C("epsilon",r,e,t))];case"FusedBatchNormV3":return[zn(C("x",r,e,t),C("mean",r,e,t),C("variance",r,e,t),C("offset",r,e,t),C("scale",r,e,t),C("epsilon",r,e,t))];case"LRN":return[Gm(C("x",r,e,t),C("radius",r,e,t),C("bias",r,e,t),C("alpha",r,e,t),C("beta",r,e,t))];case"Softmax":return[$a(C("x",r,e,t))];case"LogSoftmax":return[Du(C("x",r,e,t))];case"SparseToDense":return[af(C("sparseIndices",r,e,t),C("outputShape",r,e,t),C("sparseValues",r,e,t),C("defaultValue",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var nE=(r,e,t)=>{switch(r.op){case"Max":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ar(C("x",r,e,t),a,i)]}case"Mean":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ht(C("x",r,e,t),a,i)]}case"Min":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[Li(C("x",r,e,t),a,i)]}case"Sum":{let 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this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Tk(r,e,t,o){let n=new Set,s=[],a=null,i=null,l=new Set,u=Object.keys(r).map(m=>Zr(m)[0]),c=[];o!=null&&(c=o.map(m=>Zr(m.name)[0]));let p=[...e];for(;p.length>0;){let m=p.pop();if((Sk(m)||E6(m)||A6(m))&&a==null&&(a=m,i=a.children.map(f=>f.name).filter(f=>n.has(f))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{l.has(f.name)||(l.add(f.name),p.push(f))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function lE(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>Zr(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{o.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{o.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{o.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),e[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&o.has(p.name)&&p.inputs.every(m=>l.has(m.name))&&s.push(p)})}return u}var D6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],$6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],R6=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function Sk(r){return D6.indexOf(r.op)>=0}function E6(r){return $6.indexOf(r.op)>=0}function A6(r){return R6.indexOf(r.op)>=0}var Rp=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new Rp(e.functions[o],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(o=>e[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let o=Tk(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. 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t=vr.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(vr.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,o;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?o=this.artifacts.userDefinedMetadata.signature:o=this.artifacts.signature,this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=vr.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new 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n=0;n<o;n++)t[n]=this.get(this.wrap(this.begin+n));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=o}};Fp.INITIAL_CAPACITY=32;function Dk(r){return new gE(r)}function Pd(r){return new xE(r)}function yE(r,e){return new $k(r,e)}function _E(r,e=Va.FAIL){return new bE(r,e)}var Kt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],o=await e.next();for(;!o.done;)t.push(o.value),o=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),o=e(t.value);for(;!t.done&&o;)t=await this.next(),o=e(t.value)}handleErrors(e){return new SE(this,e)}filter(e){return new IE(this,e)}map(e){return new NE(this,e)}mapAsync(e){return new Rk(this,e)}serialMapAsync(e){return new Rk(this,e).serial()}flatmap(e){return new TE(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new CE(this,e,t)}columnMajorBatch(e,t=!0,o=Ak){return this.rowMajorBatch(e,t).map(s=>mE(s,o))}concatenate(e,t){return new $k(Dk([this,e]),t)}take(e){return e<0||e==null?this:new vE(this,e)}skip(e){return e<0||e==null?this:new kE(this,e)}prefetch(e){return new Fk(this,e)}shuffle(e,t){return new EE(this,e,t)}serial(){return new wE(this)}},gE=class extends Kt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:dE(e),done:!1}}},xE=class extends Kt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},wE=class extends Kt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},kE=class extends Kt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Te(e.value)}return this.upstream.next()}},vE=class extends Kt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},CE=class extends Kt{constructor(e,t,o=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=o,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},IE=class extends Kt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Te(e.value)}}},NE=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ln.getTensorsInContainer(e.value),o=this.transform(e.value),n=Ln.getTensorsInContainer(o);for(let s of t)Ln.isTensorInList(s,n)||s.dispose();return{value:o,done:!1}}},SE=class extends Kt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Rk=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ln.getTensorsInContainer(e.value),o=await this.transform(e.value),n=Ln.getTensorsInContainer(o);for(let s of t)Ln.isTensorInList(s,n)||s.dispose();return{value:o,done:!1}}},Op=class extends Kt{constructor(){super();this.outputQueue=new Fp,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},TE=class extends Op{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ln.getTensorsInContainer(e.value),o=this.transform(e.value),n=Ln.getTensorsInContainer(o);this.outputQueue.pushAll(o);for(let s of t)Ln.isTensorInList(s,n)||s.dispose();return!0}},$k=class extends Kt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let o=await this.moreIterators.next();if(o.done)return{value:null,done:!0};this.iterator=o.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Va;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Va||(Va={}));var bE=class extends Kt{constructor(e,t=Va.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,o=0;function n(a){return a instanceof 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Mismatched at element ${this.count}.`);case Va.SHORTEST:return{value:null,done:!0};case Va.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Fk=class extends Kt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new Od(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},EE=class extends Fk{constructor(e,t,o){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=hE.alea(o||y.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}};var qi=class{constructor(){this.size=null}batch(e,t=!0){let o=this;y.assert(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let n;return this.size===Infinity||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),lo(async()=>(await o.iterator()).columnMajorBatch(e,t,L6),n)}concatenate(e){let t=this,o;return this.size===Infinity||e.size===Infinity?o=Infinity:this.size!=null&&e.size!=null?o=this.size+e.size:o=null,lo(async()=>(await t.iterator()).concatenate(await e.iterator()),o)}filter(e){let t=this,o;return this.size===Infinity?o=Infinity:o=null,lo(async()=>(await t.iterator()).filter(n=>B(()=>e(n))),o)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return lo(async()=>(await t.iterator()).map(o=>B(()=>e(o))),this.size)}mapAsync(e){let t=this;return lo(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return lo(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,o;return this.size!=null&&e>0?o=this.size*e:e===0?o=0:this.size!=null&&(e===void 0||e<0)?o=Infinity:o=null,lo(async()=>{let n=Pd(async()=>({value:await t.iterator(),done:!1}));return yE(n.take(e))},o)}skip(e){let t=this,o;return this.size!=null&&e>=0&&this.size>=e?o=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?o=0:o=null,lo(async()=>(await t.iterator()).skip(e),o)}shuffle(e,t,o=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,s=AE.alea(t||y.now().toString());return lo(async()=>{let a=s.int32();return o&&(a+=s.int32()),(await n.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,o;return this.size!=null&&this.size>e?o=e:this.size!=null&&this.size<=e?o=this.size:o=null,lo(async()=>(await t.iterator()).take(e),o)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};qi.MAX_BUFFER_SIZE=1e4;function lo(r,e=null){return new class extends qi{constructor(){super(...arguments);this.size=e}async iterator(){return r()}}}function DE(r){return lo(async()=>Dk(r),r.length)}function $E(r){if(!kl(r))throw new Error("The argument to zip() must be an object or array.");let e;if(Array.isArray(r))for(let t=0;t<r.length;t++)e=e==null?r[t].size:Math.min(e,r[t].size);else if(r instanceof Object)for(let t in r)e=e==null?r[t].size:Math.min(e,r[t].size);return lo(async()=>{let t=await kx(r,o=>{if(o instanceof qi)return{value:o.iterator(),recurse:!1};if(kl(o))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return _E(t,Va.SHORTEST)},e)}function L6(r){if(r===null)return null;let e=r[0];return fE(e)?{value:z6(r),recurse:!1}:{value:null,recurse:!0}}function z6(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Ve?Bt(r):Dr(r)}var Md=class extends qi{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
+`).map(n=>(n.endsWith("\r")&&(n=n.slice(0,-1)),n))}};var vx='"',Ld=Symbol("out"),RE=Symbol("field"),Cx=Symbol("quote"),Ok=Symbol("quoteafterquote"),FE=Symbol("quoteinquote"),zd=class extends qi{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new Md(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(y.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&y.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((n,s)=>(n[s]=n[s]+1||1,n),{}),o=Object.keys(t).filter(n=>t[n]>1);if(y.assert(o.length===0,()=>"Duplicate column names found: "+o.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let o=t.value;return this.parseRow(o,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),o={},n={};for(let s=0;s<this.fullColumnNames.length;s++){let a=this.fullColumnNames[s],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let l=t[s],u=null;if(l==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let c=Number(l);if(isNaN(c))i&&i.dtype==="bool"?u=this.getBoolean(l):u=l;else if(!i||!i.dtype)u=c;else switch(i.dtype){case"float32":u=c;break;case"int32":u=Math.floor(c);break;case"bool":u=this.getBoolean(l);break;default:u=c}}i&&i.isLabel?n[a]=u:o[a]=u}}return Object.keys(n).length===0?o:{xs:o,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let o=[],n=0,s=e.length,a=Ld;for(let i=0;i<s;i++)switch(a){case Ld:switch(e.charAt(i)){case vx:n=i+1,a=Cx;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;o.push(""),a=Ld;break;default:a=RE,n=i;break}break;case RE:switch(e.charAt(i)){case this.delimiter:o.push(e.substring(n,i)),a=Ld,n=i+1;break;default:}break;case Cx:switch(e.charAt(i)){case vx:a=Ok;break;default:}break;case Ok:switch(e.charAt(i)){case this.delimiter:o.push(e.substring(n,i-1)),a=Ld,n=i+1;break;case vx:a=Cx;break;default:a=FE;break}break;case FE:switch(e.charAt(i)){case vx:a=Cx;break;default:}break;default:}if(a===Ok?o.push(e.substring(n,s-1)):o.push(e.substring(n)),t&&o.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${o}`);return o}};var Bd=class extends Kt{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(U().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Bd(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(o){throw new Error(`Error thrown while initializing video stream: ${o.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,o=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(o.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(o.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[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=[],o=0;return new Promise(n=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++o===this.numFrames&&(clearInterval(s),n({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,o=new Float32Array(e.length*t);return e.forEach((n,s)=>o.set(n,s*t)),o}getTensorFromAudioDataArray(e,t){let o=new Float32Array(y.sizeFromShape(t));return o.set(e,o.length-e.length),Dr(o,t)}};var Vd=class extends Kt{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=Vt([0],"int32"),this.webcamConfig.centerCrop){let o=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-o)/2,a=(1-n)/2,i=s+o,l=n+a;this.cropBox=Bi([a,s,l,i],[1,4])}else this.cropBox=Bi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(U().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 o=new Vd(e,t);return await o.start(),o}async start(){this.webcamConfig.facingMode&&y.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: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=tg.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: 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c=N.computePool3DInfo(s.shape,a,i,1,l,u),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,_=c.dilationWidth,w=c.effectiveFilterDepth,v=c.effectiveFilterHeight,$=c.effectiveFilterWidth,A=w-1-c.padInfo.front,R=$-1-c.padInfo.left,M=v-1-c.padInfo.top,z=Ce(s.shape,"float32"),W=1/(d*h*g),U=t.bufferSync(n);for(let q=0;q<c.batchSize;++q)for(let Z=0;Z<c.inChannels;++Z)for(let X=0;X<c.inDepth;++X)for(let Y=0;Y<c.inHeight;++Y)for(let te=0;te<c.inWidth;++te){let K=X-A,re=Y-M,ie=te-R,se=0;for(let pe=0;pe<w;pe+=x){let ae=(K+pe)/p;if(!(ae<0||ae>=c.outDepth||Math.floor(ae)!==ae))for(let xe=0;xe<v;xe+=b){let ge=(re+xe)/m;if(!(ge<0||ge>=c.outHeight||Math.floor(ge)!==ge))for(let _e=0;_e<$;_e+=_){let ke=(ie+_e)/f;if(ke<0||ke>=c.outWidth||Math.floor(ke)!==ke)continue;se+=U.get(q,ae,ge,ke,Z)}}}z.set(se*W,q,X,Y,te,Z)}return t.makeTensorInfo(z.shape,z.dtype,z.values)}var QA={kernelName:Ul,backendName:"cpu",kernelFunc:y5};function b5(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;ee([n,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=o,c=N.computePool2DInfo(a.shape,i,l,1,u),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,_=b-1-c.padInfo.left,w=x-1-c.padInfo.top,v=Ce(a.shape,"float32"),$=1/(f*d),A=t.data.get(n.dataId).values,R=Ce(n.shape,"float32",A);for(let M=0;M<c.batchSize;++M)for(let z=0;z<c.inChannels;++z)for(let W=0;W<c.inHeight;++W)for(let U=0;U<c.inWidth;++U){let q=W-w,Z=U-_,X=0;for(let Y=0;Y<x;Y+=h){let te=(q+Y)/p;if(!(te<0||te>=c.outHeight||Math.floor(te)!==te))for(let K=0;K<b;K+=g){let re=(Z+K)/m;if(re<0||re>=c.outWidth||Math.floor(re)!==re)continue;X+=R.get(M,te,re,z)}}v.set(X*$,M,W,U,z)}return t.makeTensorInfo(v.shape,v.dtype,v.values)}var e2={kernelName:Wl,backendName:"cpu",kernelFunc:b5};function 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t.makeTensorInfo(n.shape,n.dtype,h)}var t2={kernelName:on,backendName:"cpu",kernelFunc:_5};function w5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;ee([n],"batchToSpaceND");let i=s.reduce((x,b)=>x*b),l=N.getReshaped(n.shape,s,i),u=N.getPermuted(l.length,s.length),c=N.getReshapedPermuted(n.shape,s,i),p=N.getSliceBeginCoords(a,s.length),m=N.getSliceSize(c,a,s.length),f=tt({inputs:{x:n},backend:t,attrs:{shape:l}}),d=tr({inputs:{x:f},backend:t,attrs:{perm:u}}),h=tt({inputs:{x:d},backend:t,attrs:{shape:c}}),g=Jn({inputs:{x:h},backend:t,attrs:{begin:p,size:m}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var r2={kernelName:sa,backendName:"cpu",kernelFunc:w5};function k5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,l=t.data.get(s.dataId).values,u=Wd(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var o2={kernelName:jl,backendName:"cpu",kernelFunc:k5};var v5=Ae(Ao,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r<t.clipValueMin?t.clipValueMin:r}),n2={kernelName:Ao,backendName:"cpu",kernelFunc:v5};var C5=r=>{let{x:e}=r.inputs,t=r.backend,o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=t.data.get(s.dataId).values,l=t.data.get(a.dataId).values;for(let u=0;u<i.length;u++){let c=i[u],p=l[u];o[u]=Math.hypot(c,p)}return t.makeOutput(o,e.shape,"float32")},s2={kernelName:ia,backendName:"cpu",kernelFunc:C5};function Ki(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.data.get(o.dataId).complexTensorInfos.imag,s=t.data.get(n.dataId).values;return t.makeTensorInfo(n.shape,n.dtype,s)}var i2={kernelName:ou,backendName:"cpu",kernelFunc:Ki};function vl(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=N.computeOutShape(e.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return 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w2={kernelName:Zl,backendName:"cpu",kernelFunc:P5};function M5(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=o;ee([n,s],"depthwiseConv2DNativeBackpropInput");let p=y.computeStrides(n.shape),m=y.computeStrides(s.shape),f=N.computeConv2DInfo(c,s.shape,a,i,l,u,!0),d=new ct(f.inShape,"float32"),h=d.values,[g,x,b]=d.strides,_=t.data.get(n.dataId).values,[w,v,$]=p,A=t.data.get(s.dataId).values,[R,M,z]=m,{batchSize:W,filterHeight:U,filterWidth:q,inChannels:Z,inHeight:X,inWidth:Y,outChannels:te,outHeight:K,outWidth:re,strideHeight:ie,strideWidth:se}=f,pe=U-1-f.padInfo.top,ae=q-1-f.padInfo.left,xe=te/Z;for(let ge=0;ge<W;++ge)for(let _e=0;_e<Z;++_e)for(let ke=0;ke<X;++ke){let De=ke-pe,$e=Math.max(0,Math.ceil(De/ie)),Re=Math.min(K,(U+De)/ie);for(let He=0;He<Y;++He){let ut=He-ae,wt=Math.max(0,Math.ceil(ut/se)),kt=Math.min(re,(q+ut)/se),pt=0;for(let vt=$e;vt<Re;++vt){let qe=vt*ie-De;for(let Ft=wt;Ft<kt;++Ft){let 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o2={kernelName:Ul,backendName:"cpu",kernelFunc:k5};function v5(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;ee([n,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=o,c=N.computePool2DInfo(a.shape,i,l,1,u),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,_=b-1-c.padInfo.left,w=x-1-c.padInfo.top,v=Ce(a.shape,"float32"),$=1/(f*d),A=t.data.get(n.dataId).values,R=Ce(n.shape,"float32",A);for(let M=0;M<c.batchSize;++M)for(let V=0;V<c.inChannels;++V)for(let G=0;G<c.inHeight;++G)for(let W=0;W<c.inWidth;++W){let q=G-w,Z=W-_,X=0;for(let Y=0;Y<x;Y+=h){let te=(q+Y)/p;if(!(te<0||te>=c.outHeight||Math.floor(te)!==te))for(let K=0;K<b;K+=g){let re=(Z+K)/m;if(re<0||re>=c.outWidth||Math.floor(re)!==re)continue;X+=R.get(M,te,re,V)}}v.set(X*$,M,G,W,V)}return t.makeTensorInfo(v.shape,v.dtype,v.values)}var n2={kernelName:Wl,backendName:"cpu",kernelFunc:v5};function 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t.makeTensorInfo(n.shape,n.dtype,h)}var s2={kernelName:ln,backendName:"cpu",kernelFunc:C5};function I5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;ee([n],"batchToSpaceND");let i=s.reduce((x,b)=>x*b),l=N.getReshaped(n.shape,s,i),u=N.getPermuted(l.length,s.length),c=N.getReshapedPermuted(n.shape,s,i),p=N.getSliceBeginCoords(a,s.length),m=N.getSliceSize(c,a,s.length),f=tt({inputs:{x:n},backend:t,attrs:{shape:l}}),d=tr({inputs:{x:f},backend:t,attrs:{perm:u}}),h=tt({inputs:{x:d},backend:t,attrs:{shape:c}}),g=rs({inputs:{x:h},backend:t,attrs:{begin:p,size:m}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var i2={kernelName:sa,backendName:"cpu",kernelFunc:I5};function N5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,l=t.data.get(s.dataId).values,u=Hd(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var 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t.makeTensorInfo(h.shape,h.dtype,h.values)}var d2={kernelName:Qo,backendName:"cpu",kernelFunc:A5};function D5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o;ee([n,s],"conv3d");let u=N.computeConv3DInfo(n.shape,s.shape,a,l,i),{filterDepth:c,filterHeight:p,filterWidth:m,dilationDepth:f,dilationHeight:d,dilationWidth:h,padInfo:g}=u,x=g.front,b=g.left,_=g.top,w=new ct(u.outShape,n.dtype),v=t.data.get(n.dataId).values,$=t.data.get(s.dataId).values,A=w.values,R=y.computeStrides(n.shape),M=y.computeStrides(s.shape);for(let V=0;V<u.batchSize;++V){let G=V*R[0],W=V*w.strides[0];for(let q=0;q<u.outDepth;++q){let Z=W+q*w.strides[1],X=q*u.strideDepth-x;for(let Y=0;Y<c;++Y){let te=X+Y*f;if(te<0||te>=u.inDepth)continue;let K=Y*M[0],re=G+te*R[1];for(let ie=0;ie<u.outHeight;++ie){let se=Z+ie*w.strides[2],pe=ie*u.strideHeight-_;for(let ae=0;ae<p;++ae){let xe=pe+ae*d;if(xe<0||xe>=u.inHeight)continue;let ge=K+ae*M[1],_e=re+xe*R[2];for(let ke=0;ke<u.outWidth;++ke){let 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I2={kernelName:Zl,backendName:"cpu",kernelFunc:B5};function V5(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=o;ee([n,s],"depthwiseConv2DNativeBackpropInput");let p=y.computeStrides(n.shape),m=y.computeStrides(s.shape),f=N.computeConv2DInfo(c,s.shape,a,i,l,u,!0),d=new ct(f.inShape,"float32"),h=d.values,[g,x,b]=d.strides,_=t.data.get(n.dataId).values,[w,v,$]=p,A=t.data.get(s.dataId).values,[R,M,V]=m,{batchSize:G,filterHeight:W,filterWidth:q,inChannels:Z,inHeight:X,inWidth:Y,outChannels:te,outHeight:K,outWidth:re,strideHeight:ie,strideWidth:se}=f,pe=W-1-f.padInfo.top,ae=q-1-f.padInfo.left,xe=te/Z;for(let ge=0;ge<G;++ge)for(let _e=0;_e<Z;++_e)for(let ke=0;ke<X;++ke){let De=ke-pe,$e=Math.max(0,Math.ceil(De/ie)),Re=Math.min(K,(W+De)/ie);for(let He=0;He<Y;++He){let ut=He-ae,wt=Math.max(0,Math.ceil(ut/se)),kt=Math.min(re,(q+ut)/se),pt=0;for(let vt=$e;vt<Re;++vt){let qe=vt*ie-De;for(let Ft=wt;Ft<kt;++Ft){let 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pe=y.locToIndex([X,Y,K,ie],W,y.computeStrides(V));q[pe]=se}}}return{dataId:l.write(y.toTypedArray(q,o.dtype),V,o.dtype),shape:V,dtype:o.dtype}}};var E2={kernelName:Fc,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:l}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),p=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:_,strideWidth:w,filterHeight:v,filterWidth:$,dilationHeight:A,dilationWidth:R,outShape:M}=N.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",l);y.assert(s.rank===M.length,()=>`Error in ${Fc}, dy must have the same rank as output ${M.length}, but got ${s.rank}`);let V=y.toNestedArray(M,u.data.get(s.dataId).values),G=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let q=0;q<m;++q)for(let Z=0;Z<g;++Z){let X=Z*_-b.top;for(let Y=0;Y<x;++Y){let te=Y*w-b.left;for(let K=0;K<h;++K){let 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         return (val > 0.0 || val < 0.0) ? false : val != 0.0;
       }
@@ -86,11 +86,11 @@ 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:r,attribute:e,varyingVs:t,varyingFs:o,texture2D:n,output:s,defineOutput:a,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function zs(r,e,t="index"){let o=y.computeStrides(e);return o.map((n,s)=>{let a=`int ${r[s]} = ${t} / ${n}`,i=s===o.length-1?`int ${r[s+1]} = ${t} - ${r[s]} * ${n}`:`index -= ${r[s]} * ${n}`;return`${a}; ${i};`}).join("")}function zp(r){let e=y.computeStrides(r).map(t=>t.toString());return`
+    `),{version:r,attribute:e,varyingVs:t,varyingFs:o,texture2D:n,output:s,defineOutput:a,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function Vs(r,e,t="index"){let o=y.computeStrides(e);return o.map((n,s)=>{let a=`int ${r[s]} = ${t} / ${n}`,i=s===o.length-1?`int ${r[s+1]} = ${t} - ${r[s]} * ${n}`:`index -= ${r[s]} * ${n}`;return`${a}; ${i};`}).join("")}function Gp(r){let e=y.computeStrides(r).map(t=>t.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z;
   }
-`}var $x=`
+`}var Lx=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -129,9 +129,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
-`;var cv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Cl.DENSE;let t=Il(e),o=Mt();this.outputShape=e,this.userCode=`
+`;var dv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Cl.DENSE;let t=Il(e),o=Mt();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${zs(["r","c","d"],e)}
+        ${Vs(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -150,9 +150,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${o.output} = result;
       }
-    `}};var pv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Cl.DENSE;let t=Il(e),o=Mt();this.outputShape=e,this.userCode=`
+    `}};var hv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Cl.DENSE;let t=Il(e),o=Mt();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${zs(["r","c","d"],e)}
+        ${Vs(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -171,23 +171,23 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${o.output} = result;
       }
-    `}};var mv=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Er.DOWNLOAD;let t=Mt();this.outputShape=e,this.userCode=`
-      ${$x}
+    `}};var gv=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Er.DOWNLOAD;let t=Mt();this.outputShape=e,this.userCode=`
+      ${Lx}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
-    `}};var fv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Er.DOWNLOAD;let t=Mt();this.outputShape=e,this.userCode=`
-      ${$x}
+    `}};var xv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Er.DOWNLOAD;let t=Mt();this.outputShape=e,this.userCode=`
+      ${Lx}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
         ${t.output} = encode_float(x);
       }
-    `}};var dv=class{constructor(e,t,o=!1){this.variableNames=["A"];let n=Mt(),[s,a]=t;this.outputShape=e;let i="result";o&&(i="floor(result * 255. + 0.5)"),this.userCode=`
-      ${zp(e)}
+    `}};var yv=class{constructor(e,t,o=!1){this.variableNames=["A"];let n=Mt(),[s,a]=t;this.outputShape=e;let i="result";o&&(i="floor(result * 255. + 0.5)"),this.userCode=`
+      ${Gp(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -216,7 +216,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = vec4(${i}, 0., 0., 0.);
       }
-    `}};var hv=class{constructor(e,t,o=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let n=Mt(),[s,a]=t;this.outputShape=e;let i="",l="result";o&&(l="floor(result * 255. + 0.5)");for(let u=0;u<=1;u++)for(let c=0;c<=1;c++){let p=u*2+c;i+=`
+    `}};var bv=class{constructor(e,t,o=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let n=Mt(),[s,a]=t;this.outputShape=e;let i="",l="result";o&&(l="floor(result * 255. + 0.5)");for(let u=0;u<=1;u++)for(let c=0;c<=1;c++){let p=u*2+c;i+=`
           localCoords = coords;
           if(localCoords[2] + ${c} < ${e[2]}) {
             localCoords[2] += ${c};
@@ -245,7 +245,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
             }
           }
         `}this.userCode=`
-      ${zp(e)}
+      ${Gp(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -260,7 +260,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = ${l};
       }
-    `}};function v$(r){let e=Mt(),t=`${e.version}
+    `}};function S$(r){let e=Mt(),t=`${e.version}
     precision highp float;
     ${e.attribute} vec3 clipSpacePos;
     ${e.attribute} vec2 uv;
@@ -269,22 +269,22 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return n$(r,t)}function C$(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return l$(r,e)}function I$(r){let e=new Uint16Array([0,1,2,2,1,3]);return u$(r,e)}function oh(r,e,t,o,n,s){p$(e,t);let a=c$(r),i=r.TEXTURE_2D;return Ee(r,()=>r.bindTexture(i,a)),Ee(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),Ee(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),Ee(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),Ee(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),Ee(r,()=>r.texImage2D(i,0,o,e,t,0,n,s,null)),Ee(r,()=>r.bindTexture(r.TEXTURE_2D,null)),a}function gv(r){return r.internalFormatFloat}function N$(r,e,t,o){let[n,s]=ic(e,t);return oh(r,n,s,gv(o),o.textureFormatFloat,r.FLOAT)}function xv(r){return r.internalFormatHalfFloat}function S$(r,e,t,o){let[n,s]=ic(e,t);return oh(r,n,s,xv(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function yv(r){return r.downloadTextureFormat}function T$(r,e,t,o){let[n,s]=ic(e,t);return oh(r,n,s,yv(o),r.RGBA,r.UNSIGNED_BYTE)}function bv(r){return r.internalFormatPackedFloat}function E$(r,e,t,o){let[n,s]=Xi(e,t);return oh(r,n,s,bv(o),r.RGBA,r.FLOAT)}function _v(r){return r.internalFormatPackedHalfFloat}function A$(r,e,t,o){let[n,s]=Xi(e,t);return oh(r,n,s,_v(o),r.RGBA,o.textureTypeHalfFloat)}function D$(r,e,t){let o=0,n=3*4,s=3*4+2*4;return Ee(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),nv(r,e,"clipSpacePos",t,3,s,o)&&nv(r,e,"uv",t,2,s,n)}function $$(r,e,t,o,n,s){Ee(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,l;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,l=r.RGBA):(a=new Float32Array(t*o*4),i=r.FLOAT,l=s.internalFormatPackedFloat),a.set(n),Ee(r,()=>r.texImage2D(r.TEXTURE_2D,0,l,t,o,0,r.RGBA,i,a)),Ee(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function R$(r,e,t){Ee(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?Ee(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):Ee(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),Ee(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function F$(r,e,t,o){let n=r.createBuffer();Ee(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,n));let i=4*4*e*t;return Ee(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),Ee(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),Ee(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function O$(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function P$(r,e,t,o){let[n,s]=ic(e,t),a=4,i=new Uint8Array(t$(e*t,a));return Ee(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function M$(r,e,t,o,n,s,a,i){let l=r,u=new Float32Array(r$(s,a));return l.bindBuffer(l.PIXEL_PACK_BUFFER,e),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function L$(r,e,t){let o=new Float32Array(e*t*4);return Ee(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var wv=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=G().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,e$(t,e)):this.gl=Lo(t);let o="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(G().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=th(this.gl,s),zo(this.gl,a))this.textureHalfFloatExtension=th(this.gl,a);else if(G().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(o),zo(this.gl,n))this.colorBufferHalfFloatExtension=th(this.gl,n);else if(G().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(o="EXT_color_buffer_float",zo(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(zo(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=C$(this.gl),this.indexBuffer=I$(this.gl),this.framebuffer=m$(this.gl),this.textureConfig=eh(this.gl,this.textureHalfFloatExtension)}get debug(){return G().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 l$(r,t)}function T$(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return m$(r,e)}function E$(r){let e=new Uint16Array([0,1,2,2,1,3]);return f$(r,e)}function ih(r,e,t,o,n,s){h$(e,t);let a=d$(r),i=r.TEXTURE_2D;return Ee(r,()=>r.bindTexture(i,a)),Ee(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),Ee(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),Ee(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),Ee(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),Ee(r,()=>r.texImage2D(i,0,o,e,t,0,n,s,null)),Ee(r,()=>r.bindTexture(r.TEXTURE_2D,null)),a}function _v(r){return r.internalFormatFloat}function A$(r,e,t,o){let[n,s]=ac(e,t);return ih(r,n,s,_v(o),o.textureFormatFloat,r.FLOAT)}function wv(r){return r.internalFormatHalfFloat}function D$(r,e,t,o){let[n,s]=ac(e,t);return ih(r,n,s,wv(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function kv(r){return r.downloadTextureFormat}function $$(r,e,t,o){let[n,s]=ac(e,t);return ih(r,n,s,kv(o),r.RGBA,r.UNSIGNED_BYTE)}function vv(r){return r.internalFormatPackedFloat}function R$(r,e,t,o){let[n,s]=Xi(e,t);return ih(r,n,s,vv(o),r.RGBA,r.FLOAT)}function Cv(r){return r.internalFormatPackedHalfFloat}function F$(r,e,t,o){let[n,s]=Xi(e,t);return ih(r,n,s,Cv(o),r.RGBA,o.textureTypeHalfFloat)}function O$(r,e,t){let o=0,n=3*4,s=3*4+2*4;return Ee(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),lv(r,e,"clipSpacePos",t,3,s,o)&&lv(r,e,"uv",t,2,s,n)}function P$(r,e,t,o,n,s){Ee(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,l;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,l=r.RGBA):(a=new Float32Array(t*o*4),i=r.FLOAT,l=s.internalFormatPackedFloat),a.set(n),Ee(r,()=>r.texImage2D(r.TEXTURE_2D,0,l,t,o,0,r.RGBA,i,a)),Ee(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function M$(r,e,t){Ee(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?Ee(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):Ee(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),Ee(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function L$(r,e,t,o){let n=r.createBuffer();Ee(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,n));let i=4*4*e*t;return Ee(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),Ee(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),Ee(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function z$(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function B$(r,e,t,o){let[n,s]=ac(e,t),a=4,i=new Uint8Array(s$(e*t,a));return Ee(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function V$(r,e,t,o,n,s,a,i){let l=r,u=new Float32Array(i$(s,a));return l.bindBuffer(l.PIXEL_PACK_BUFFER,e),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function G$(r,e,t){let o=new Float32Array(e*t*4);return Ee(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var Iv=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=U().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,n$(t,e)):this.gl=Bo(t);let o="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(U().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=nh(this.gl,s),Vo(this.gl,a))this.textureHalfFloatExtension=nh(this.gl,a);else if(U().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(o),Vo(this.gl,n))this.colorBufferHalfFloatExtension=nh(this.gl,n);else if(U().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(o="EXT_color_buffer_float",Vo(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(Vo(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=T$(this.gl),this.indexBuffer=E$(this.gl),this.framebuffer=g$(this.gl),this.textureConfig=oh(this.gl,this.textureHalfFloatExtension)}get debug(){return U().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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Ee(t,()=>t.attachShader(s,n)),Ee(t,()=>t.attachShader(s,o)),p$(t,s),this.debug&&Fx(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=O$(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ee(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Fx(this.gl,this.program),Ee(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?x$(this.gl,e,t):y$(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ee(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),b$(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Xi(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Fx(this.gl,this.program),sh(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ee(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ee(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return 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y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let o=this.gl;return o.getQueryParameter(e,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(e,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(e,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=L8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Ox(this.gl,e,this.framebuffer),this.debug&&sh(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Ox(this.gl,this.outputTexture,this.framebuffer),this.debug&&sh(this.gl)):uv(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;Ox(n,e,this.framebuffer),this.debug&&sh(n),this.outputTexture=e,Ee(n,()=>n.viewport(0,0,t,o)),Ee(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),Ee(this.gl,()=>this.gl.scissor(e,t,o,n))}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 L8(r){let e=0;for(;e<r.length&&r[e]();++e);return e-1}var{getBroadcastDims:W$}=N;function U$(r,e,t,o){let n=[];r.forEach(d=>{let h=y.sizeFromShape(d.shapeInfo.logicalShape);d.shapeInfo.isUniform?n.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(n.push(`uniform sampler2D ${d.name};`),n.push(`uniform int offset${d.name};`))});let s=n.join(`
+`),a=r.map(d=>z8(d,e,o)).join(`
+`),i=e.texShape,l=Mt(),u=G8(l),c,p,m=j8(l);return e.isPacked?(c=B8(e.logicalShape,i),p=U8(l)):(c=V8(e.logicalShape,i),p=W8(l)),o&&(m+=H8),[m,u,p,s,c,a,t].join(`
+`)}function Wp(r){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return q8(r);case 1:return K8(r);case 2:return X8(r);case 3:return Y8(r);case 4:return Z8(r);case 5:return J8(r);case 6:return Q8(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function j$(r){switch(r.shapeInfo.logicalShape.length){case 0:return eY(r);case 1:return tY(r);case 2:return rY(r);case 3:return oY(r);default:return nY(r)}}function z8(r,e,t=!1){let o="";t?o+=j$(r):o+=Wp(r);let n=r.shapeInfo.logicalShape,s=e.logicalShape;return n.length<=s.length&&(t?o+=sY(r,e):o+=iY(r,e)),o}function B8(r,e){switch(r.length){case 0:return H$();case 1:return aY(r,e);case 2:return cY(r,e);case 3:return lY(r,e);default:return uY(r,e)}}function V8(r,e){switch(r.length){case 0:return H$();case 1:return pY(r,e);case 2:return gY(r,e);case 3:return mY(r,e);case 4:return fY(r,e);case 5:return dY(r,e);case 6:return hY(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function G8(r){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${r.texture2D}(textureSampler, uv).r;
     }
-  `}function z8(r){return`
+  `}function W8(r){return`
     void setOutput(float val) {
       ${r.output} = vec4(val, 0, 0, 0);
     }
-  `}function B8(r){return`
+  `}function U8(r){return`
     void setOutput(vec4 val) {
       ${r.output} = val;
     }
-  `}function V8(r){return`${r.version}
+  `}function j8(r){return`${r.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
@@ -339,10 +339,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       return fract((p3.x + p3.y) * p3.z);
     }
 
-    ${fY}
-    ${dY}
-    ${hY}
-  `}var fY=`
+    ${xY}
+    ${yY}
+    ${bY}
+  `}var xY=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -354,7 +354,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,dY=`
+`,yY=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -362,7 +362,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,hY=`
+`,bY=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -371,7 +371,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,G8=`
+`,H8=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -382,11 +382,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function G$(){return`
+`;function H$(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function oY(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return t[0]===1?`
+  `}function aY(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return t[0]===1?`
       int getOutputCoords() {
         return 2 * int(resultUV.x * ${t[1]}.0);
       }
@@ -400,7 +400,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${t[0]}, ${t[1]}));
       return 2 * (resTexRC.x * ${t[1]} + resTexRC.y);
     }
-  `}function aY(r,e){return e[0]===1?`
+  `}function pY(r,e){return e[0]===1?`
       int getOutputCoords() {
         return int(resultUV.x * ${e[1]}.0);
       }
@@ -414,7 +414,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${e[0]}, ${e[1]}));
       return resTexRC.x * ${e[1]} + resTexRC.y;
     }
-  `}function nY(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[2]/2),n=o*Math.ceil(r[1]/2);return`
+  `}function lY(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[2]/2),n=o*Math.ceil(r[1]/2);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -428,7 +428,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function lY(r,e){let t=zs(["r","c","d"],r);return`
+  `}function mY(r,e){let t=Vs(["r","c","d"],r);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${e[0]}, ${e[1]}));
@@ -436,7 +436,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${t}
       return ivec3(r, c, d);
     }
-  `}function sY(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[r.length-1]/2),n=o*Math.ceil(r[r.length-2]/2),s=n,a="",i="b, r, c";for(let l=2;l<r.length-1;l++)s*=r[r.length-l-1],a=`
+  `}function uY(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[r.length-1]/2),n=o*Math.ceil(r[r.length-2]/2),s=n,a="",i="b, r, c";for(let l=2;l<r.length-1;l++)s*=r[r.length-l-1],a=`
       int b${l} = index / ${s};
       index -= b${l} * ${s};
     `+a,i=`b${l}, `+i;return`
@@ -455,7 +455,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${r.length}(${i});
     }
-  `}function uY(r,e){let t=zs(["r","c","d","d2"],r);return`
+  `}function fY(r,e){let t=Vs(["r","c","d","d2"],r);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${e[0]}, ${e[1]}));
@@ -463,7 +463,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${t}
       return ivec4(r, c, d, d2);
     }
-  `}function cY(r,e){let t=zs(["r","c","d","d2","d3"],r);return`
+  `}function dY(r,e){let t=Vs(["r","c","d","d2","d3"],r);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},
                              ${e[1]}));
@@ -475,7 +475,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function pY(r,e){let t=zs(["r","c","d","d2","d3","d4"],r);return`
+  `}function hY(r,e){let t=Vs(["r","c","d","d2","d3","d4"],r);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${e[0]}, ${e[1]}));
@@ -486,7 +486,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function iY(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y.arraysEqual(r,e))return`
+  `}function cY(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y.arraysEqual(r,e))return`
       ivec2 getOutputCoords() {
         return 2 * ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
       }
@@ -501,7 +501,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec2(r, c);
     }
-  `}function mY(r,e){return y.arraysEqual(r,e)?`
+  `}function gY(r,e){return y.arraysEqual(r,e)?`
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
       }
@@ -528,34 +528,34 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${r[1]};
       return ivec2(r, c);
     }
-  `}function lc(r){return`offset${r}`}function Y8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=Mt();return`
+  `}function uc(r){return`offset${r}`}function eY(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=Mt();return`
     vec4 ${t}() {
       return ${o.texture2D}(${e}, halfCR);
     }
-  `}function W8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${t}() {return ${e};}`;let[o,n]=r.shapeInfo.texShape;if(o===1&&n===1)return`
+  `}function q8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${t}() {return ${e};}`;let[o,n]=r.shapeInfo.texShape;if(o===1&&n===1)return`
       float ${t}() {
         return sampleTexture(${e}, halfCR);
       }
-    `;let[s,a]=r.shapeInfo.texShape,i=lc(e);return`
+    `;let[s,a]=r.shapeInfo.texShape,i=uc(e);return`
     float ${t}() {
       vec2 uv = uvFromFlat(${s}, ${a}, ${i});
       return sampleTexture(${e}, uv);
     }
-  `}function Z8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,n=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],s=Mt();return`
+  `}function tY(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,n=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],s=Mt();return`
     vec4 ${t}(int index) {
       vec2 uv = packedUVfrom1D(
         ${n[0]}, ${n[1]}, index);
       return ${s.texture2D}(${e}, uv);
     }
-  `}function U8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
+  `}function K8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
       float ${t}(int index) {
-        ${Vp(r)}
+        ${Up(r)}
       }
     `;let o=r.shapeInfo.texShape,n=o[0],s=o[1];if(s===1&&n===1)return`
       float ${t}(int index) {
         return sampleTexture(${e}, halfCR);
       }
-    `;let a=lc(e);return s===1?`
+    `;let a=uc(e);return s===1?`
       float ${t}(int index) {
         vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${n}.0);
         return sampleTexture(${e}, uv);
@@ -570,7 +570,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${n}, ${s}, index + ${a});
       return sampleTexture(${e}, uv);
     }
-  `}function J8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=n[0],a=n[1],i=Mt();if(n!=null&&y.arraysEqual(e,n))return`
+  `}function rY(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=n[0],a=n[1],i=Mt();if(n!=null&&y.arraysEqual(e,n))return`
       vec4 ${o}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${s}.0);
 
@@ -581,22 +581,22 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
       return ${i.texture2D}(${t}, uv);
     }
-  `}function j8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape;if(n!=null&&y.arraysEqual(e,n)){let p=n[0],m=n[1];return`
+  `}function X8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape;if(n!=null&&y.arraysEqual(e,n)){let p=n[0],m=n[1];return`
     float ${o}(int row, int col) {
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${m}.0, ${p}.0);
       return sampleTexture(${t}, uv);
     }
-  `}let{newShape:s,keptDims:a}=y.squeezeShape(e),i=s;if(i.length<e.length){let p=Gp(r,i),m=["row","col"];return`
-      ${Bp(p)}
+  `}let{newShape:s,keptDims:a}=y.squeezeShape(e),i=s;if(i.length<e.length){let p=jp(r,i),m=["row","col"];return`
+      ${Wp(p)}
       float ${o}(int row, int col) {
-        return ${o}(${Wp(m,a)});
+        return ${o}(${Hp(m,a)});
       }
     `}if(r.shapeInfo.isUniform)return`
       float ${o}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
-        ${Vp(r)}
+        ${Up(r)}
       }
-    `;let l=n[0],u=n[1],c=lc(t);return u===1?`
+    `;let l=n[0],u=n[1],c=uc(t);return u===1?`
     float ${o}(int row, int col) {
       float index = dot(vec3(row, col, ${c}), vec3(${e[1]}, 1, 1));
       vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
@@ -615,10 +615,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${l}, ${u}, index);
     return sampleTexture(${t}, uv);
   }
-`}function Q8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)];if(e[0]===1){let p=e.slice(1),m=[1,2],f=Gp(r,p),d=["b","row","col"];return`
-        ${V$(f)}
+`}function oY(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)];if(e[0]===1){let p=e.slice(1),m=[1,2],f=jp(r,p),d=["b","row","col"];return`
+        ${j$(f)}
         vec4 ${o}(int b, int row, int col) {
-          return ${o}(${Wp(d,m)});
+          return ${o}(${Hp(d,m)});
         }
       `}let a=s[0],i=s[1],l=Math.ceil(e[2]/2),u=l*Math.ceil(e[1]/2),c=Mt();return`
     vec4 ${o}(int b, int row, int col) {
@@ -626,16 +626,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${a}, ${i}, ${u}, ${l}, b, row, col);
       return ${c.texture2D}(${t}, uv);
     }
-  `}function H8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[1]*e[2],s=e[2],{newShape:a,keptDims:i}=y.squeezeShape(e),l=a;if(l.length<e.length){let d=Gp(r,l),h=["row","col","depth"];return`
-        ${Bp(d)}
+  `}function Y8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[1]*e[2],s=e[2],{newShape:a,keptDims:i}=y.squeezeShape(e),l=a;if(l.length<e.length){let d=jp(r,l),h=["row","col","depth"];return`
+        ${Wp(d)}
         float ${o}(int row, int col, int depth) {
-          return ${o}(${Wp(h,i)});
+          return ${o}(${Hp(h,i)});
         }
       `}if(r.shapeInfo.isUniform)return`
       float ${o}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${n}, ${s}, 1)));
-        ${Vp(r)}
+        ${Up(r)}
       }
     `;let u=r.shapeInfo.texShape,c=u[0],p=u[1],m=r.shapeInfo.flatOffset;if(p===n&&m==null)return`
         float ${o}(int row, int col, int depth) {
@@ -652,14 +652,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${c}.0);
       return sampleTexture(${t}, uv);
     }
-  `;let f=lc(t);return`
+  `;let f=uc(t);return`
       float ${o}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
         int index = row * ${n} + col * ${s} + depth + ${f};
         vec2 uv = uvFromFlat(${c}, ${p}, index);
         return sampleTexture(${t}, uv);
       }
-  `}function eY(r){let e=r.shapeInfo.logicalShape,t=e.length,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],i=a[0],l=a[1],u=Math.ceil(e[t-1]/2),c=u*Math.ceil(e[t-2]/2),p="int b, int row, int col",m=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let d=2;d<t-1;d++)p=`int b${d}, `+p,c*=e[t-d-1],m=`b${d} * ${c} + `+m;let f=Mt();return`
+  `}function nY(r){let e=r.shapeInfo.logicalShape,t=e.length,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],i=a[0],l=a[1],u=Math.ceil(e[t-1]/2),c=u*Math.ceil(e[t-2]/2),p="int b, int row, int col",m=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let d=2;d<t-1;d++)p=`int b${d}, `+p,c*=e[t-d-1],m=`b${d} * ${c} + `+m;let f=Mt();return`
     vec4 ${n}(${p}) {
       int index = ${m};
       int texR = index / ${l};
@@ -667,16 +667,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${i});
       return ${f.texture2D}(${o}, uv);
     }
-  `}function q8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[3],s=e[2]*n,a=e[1]*s,{newShape:i,keptDims:l}=y.squeezeShape(e);if(i.length<e.length){let d=Gp(r,i),h=["row","col","depth","depth2"];return`
-      ${Bp(d)}
+  `}function Z8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[3],s=e[2]*n,a=e[1]*s,{newShape:i,keptDims:l}=y.squeezeShape(e);if(i.length<e.length){let d=jp(r,i),h=["row","col","depth","depth2"];return`
+      ${Wp(d)}
       float ${o}(int row, int col, int depth, int depth2) {
-        return ${o}(${Wp(h,l)});
+        return ${o}(${Hp(h,l)});
       }
     `}if(r.shapeInfo.isUniform)return`
       float ${o}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${a}, ${s}, ${n}, 1)));
-        ${Vp(r)}
+        ${Up(r)}
       }
     `;let u=r.shapeInfo.flatOffset,c=r.shapeInfo.texShape,p=c[0],m=c[1];if(m===a&&u==null)return`
       float ${o}(int row, int col, int depth, int depth2) {
@@ -697,7 +697,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${m}.0, ${p}.0);
         return sampleTexture(${t}, uv);
       }
-    `;let f=lc(t);return`
+    `;let f=uc(t);return`
     float ${o}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${a} + col * ${s} +
@@ -705,10 +705,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${p}, ${m}, index + ${f});
       return sampleTexture(${t}, uv);
     }
-  `}function K8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[4],s=e[3]*n,a=e[2]*s,i=e[1]*a,{newShape:l,keptDims:u}=y.squeezeShape(e);if(l.length<e.length){let h=Gp(r,l),g=["row","col","depth","depth2","depth3"];return`
-      ${Bp(h)}
+  `}function J8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[4],s=e[3]*n,a=e[2]*s,i=e[1]*a,{newShape:l,keptDims:u}=y.squeezeShape(e);if(l.length<e.length){let h=jp(r,l),g=["row","col","depth","depth2","depth3"];return`
+      ${Wp(h)}
       float ${o}(int row, int col, int depth, int depth2, int depth3) {
-        return ${o}(${Wp(g,u)});
+        return ${o}(${Hp(g,u)});
       }
     `}if(r.shapeInfo.isUniform)return`
       float ${o}(int row, int col, int depth, int depth2, int depth3) {
@@ -716,7 +716,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           vec4(row, col, depth, depth2),
           vec4(${i}, ${a}, ${s}, ${n})) +
           depth3;
-        ${Vp(r)}
+        ${Up(r)}
       }
     `;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===i&&c==null)return`
       float ${o}(int row, int col, int depth, int depth2, int depth3) {
@@ -738,7 +738,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${f}.0, ${m}.0);
         return sampleTexture(${t}, uv);
       }
-    `;let d=lc(t);return`
+    `;let d=uc(t);return`
     float ${o}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${i} + col * ${a} + depth * ${s} +
@@ -746,11 +746,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${m}, ${f}, index);
       return sampleTexture(${t}, uv);
     }
-  `}function X8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:n,keptDims:s}=y.squeezeShape(e);if(n.length<e.length){let g=Gp(r,n),x=["row","col","depth","depth2","depth3","depth4"];return`
-      ${Bp(g)}
+  `}function Q8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:n,keptDims:s}=y.squeezeShape(e);if(n.length<e.length){let g=jp(r,n),x=["row","col","depth","depth2","depth3","depth4"];return`
+      ${Wp(g)}
       float ${o}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${o}(${Wp(x,s)});
+        return ${o}(${Hp(x,s)});
       }
     `}let a=e[5],i=e[4]*a,l=e[3]*i,u=e[2]*l,c=e[1]*u;if(r.shapeInfo.isUniform)return`
       float ${o}(int row, int col, int depth,
@@ -761,7 +761,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           dot(
             vec2(depth3, depth4),
             vec2(${a}, 1)));
-        ${Vp(r)}
+        ${Up(r)}
       }
     `;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
       float ${o}(int row, int col, int depth,
@@ -787,7 +787,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${d}.0, ${f}.0);
         return sampleTexture(${t}, uv);
       }
-    `;let h=lc(t);return`
+    `;let h=uc(t);return`
     float ${o}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -796,13 +796,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${f}, ${d}, index);
       return sampleTexture(${t}, uv);
     }
-  `}function Vp(r){let e=r.name,t=y.sizeFromShape(r.shapeInfo.logicalShape);return t<2?`return ${e};`:`
+  `}function Up(r){let e=r.name,t=y.sizeFromShape(r.shapeInfo.logicalShape);return t<2?`return ${e};`:`
     for (int i = 0; i < ${t}; i++) {
       if (i == index) {
         return ${e}[i];
       }
     }
-  `}function tY(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=z$(r.shapeInfo.logicalShape,e.logicalShape),l=Le(a),u=a-s,c,p=["x","y","z","w","u","v"];s===0?c="":a<2&&i.length>=1?c="coords = 0;":c=i.map(b=>`coords.${p[b+u]} = 0;`).join(`
+  `}function sY(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=W$(r.shapeInfo.logicalShape,e.logicalShape),l=Le(a),u=a-s,c,p=["x","y","z","w","u","v"];s===0?c="":a<2&&i.length>=1?c="coords = 0;":c=i.map(b=>`coords.${p[b+u]} = 0;`).join(`
 `);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,_)=>`coords.${p[_+u]}`).join(", ");let f="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!x)f=`
       return vec4(outputValue.xy, outputValue.xy);
     `;else if(h&&!x)a===1?f=`
@@ -816,22 +816,22 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec4 outputValue = get${o}(${m});
       ${f}
     }
-  `}function rY(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,l=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===l&&r.shapeInfo.flatOffset==null&&y.arraysEqual(a,s))return`
+  `}function iY(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,l=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===l&&r.shapeInfo.flatOffset==null&&y.arraysEqual(a,s))return`
       float ${n}() {
         return sampleTexture(${t}, resultUV);
       }
-    `;let u=Le(l),c=z$(r.shapeInfo.logicalShape,e.logicalShape),p=l-i,m,f=["x","y","z","w","u","v"];i===0?m="":l<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
+    `;let u=Le(l),c=W$(r.shapeInfo.logicalShape,e.logicalShape),p=l-i,m,f=["x","y","z","w","u","v"];i===0?m="":l<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
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     float ${n}() {
       ${u} coords = getOutputCoords();
       ${m}
       return get${o}(${d});
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+  `}function Le(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function jp(r,e){let t=JSON.parse(JSON.stringify(r));return t.shapeInfo.logicalShape=e,t}function Hp(r,e){return e.map(t=>r[t]).join(", ")}function q$(r,e,t,o){let n=e.userCode,s=t.map((f,d)=>{let h={logicalShape:f.shape,texShape:f.isUniform?null:f.texData.texShape,isUniform:f.isUniform,isPacked:f.isUniform?!1:f.texData.isPacked,flatOffset:null};return f.texData!=null&&f.texData.slice!=null&&f.texData.slice.flatOffset>0&&(h.flatOffset=f.texData.slice.flatOffset),{name:e.variableNames[d],shapeInfo:h}}),a=s.map(f=>f.shapeInfo),i={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},l=U$(s,i,n,e.packedInputs),u=r.createProgram(l),c=null,p=r.getUniformLocation(u,"NAN",!1);U().getNumber("WEBGL_VERSION")===1&&(c=r.getUniformLocation(u,"INFINITY",!1));let m={};for(let f=0;f<e.variableNames.length;f++){let d=e.variableNames[f],h=!1;m[d]=r.getUniformLocation(u,d,h),m[`offset${d}`]=r.getUniformLocation(u,`offset${d}`,h)}return{program:e,source:l,webGLProgram:u,uniformLocations:m,inShapeInfos:a,outShapeInfo:i,infLoc:c,nanLoc:p}}function K$(r,e){if(r.length!==e.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} inputs`);r.forEach((t,o)=>{let n=t.logicalShape,s=e[o],a=s.shape;if(!y.arraysEqual(n,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${n} and ${a} must match`);if(t.isUniform&&s.isUniform)return;let i=t.texShape,l=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function X$(r,e,t,o,n){K$(e.inShapeInfos,t),K$([e.outShapeInfo],[o]);let s=o.texData.texture,a=o.texData.texShape;o.texData.isPacked?r.setOutputPackedMatrixTexture(s,a[0],a[1]):r.setOutputMatrixTexture(s,a[0],a[1]),r.setProgram(e.webGLProgram),U().getNumber("WEBGL_VERSION")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,Infinity),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN),t.forEach((i,l)=>{let u=e.program.variableNames[l],c=e.uniformLocations[u],p=e.uniformLocations[`offset${u}`];if(c!=null){if(i.isUniform){if(y.sizeFromShape(i.shape)<2)r.gl.uniform1f(c,i.uniformValues[0]);else{let m=i.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),r.gl.uniform1fv(c,m)}return}i.texData.slice!=null&&p!=null&&r.gl.uniform1i(p,i.texData.slice.flatOffset),r.setInputMatrixTexture(i.texData.texture,c,l)}}),n!=null&&n(r,e.webGLProgram),r.executeProgram()}function Y$(r,e,t){let o="";e.concat(t).forEach(a=>{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,l=a.isUniform?"uniform":a.texData.texShape;o+=`${a.shape}_${l}_${i}`});let n=r.userCode,s=r.constructor.name;return s+="_"+o+"_"+n,s}var{addImpl:Z$,bincountImpl:zx,bincountReduceImpl:J$,ceilImpl:Q$,concatImpl:eR,expImpl:tR,expm1Impl:rR,floorImpl:oR,gatherV2Impl:nR,greaterImpl:sR,lessImpl:iR,linSpaceImpl:aR,logImpl:lR,maxImpl:uR,maximumImpl:cR,minimumImpl:pR,multiplyImpl:mR,negImpl:fR,prodImpl:dR,rangeImpl:hR,rsqrtImpl:gR,simpleAbsImpl:Bx,sliceImpl:xR,stridedSliceImpl:yR,subImpl:bR,tileImpl:_R,topKImpl:wR,transposeImpl:qp,uniqueImpl:kR}=qk;function Nv(r,e){return["x","y","z","w","u","v"].slice(0,e).map(t=>`${r}.${t}`)}function Ut(r,e){return e===1?[r]:Nv(r,e)}function vR(r,e){if(r===1)return"rc";let t="";for(let o=0;o<r;o++)t+=e[o],o<r-1&&(t+=",");return t}var Sv=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 o=Ut("rc",t),n=Le(t),s=gY(t,e,o),a=xY(t,e[e.length-1],e[e.length-2],o),i=yY(e,o);this.userCode=`
+      `;else{let o=Ut("rc",t),n=Le(t),s=_Y(t,e,o),a=wY(t,e[e.length-1],e[e.length-2],o),i=kY(e,o);this.userCode=`
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           ${n} rc = getOutputCoords();
 
@@ -843,7 +843,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             setOutput(vec4(${i}));
           }
         }
-      `}}};function bY(r,e){let t=[];for(let o=0;o<=1;o++)for(let n=0;n<=1;n++){let s=`${o===0?"r":"rp1"}, ${n===0?"c":"cp1"}`;for(let a=2;a<r;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}return t}function gY(r,e,t){if(r===1)return`rc > ${e[0]}`;let o="";for(let n=r-2;n<r;n++)o+=`${t[n]} >= ${e[n]}`,n<r-1&&(o+="||");return o}function xY(r,e,t,o){if(r===1)return"";let n=o.slice(-2);return`
+      `}}};function vY(r,e){let t=[];for(let o=0;o<=1;o++)for(let n=0;n<=1;n++){let s=`${o===0?"r":"rp1"}, ${n===0?"c":"cp1"}`;for(let a=2;a<r;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}return t}function _Y(r,e,t){if(r===1)return`rc > ${e[0]}`;let o="";for(let n=r-2;n<r;n++)o+=`${t[n]} >= ${e[n]}`,n<r-1&&(o+="||");return o}function wY(r,e,t,o){if(r===1)return"";let n=o.slice(-2);return`
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     int c = ${n[1]};
     int rp1 = r + 1;
@@ -851,12 +851,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
     bool cEdge = cp1 >= ${e};
     bool rEdge = rp1 >= ${t};
-  `}function yY(r,e){let t=r.length,o=bY(t,e);return t===1?`getA(rc),
+  `}function kY(r,e){let t=r.length,o=vY(t,e);return t===1?`getA(rc),
             rc + 1 >= ${r[0]} ? 0. : getA(rc + 1),
             0, 0`:`getA(${o[0]}),
           cEdge ? 0. : getA(${o[1]}),
           rEdge ? 0. : getA(${o[2]}),
-          rEdge || cEdge ? 0. : getA(${o[3]})`}var nh=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let o="";for(let n=0;n<4;n++){let s="thisRC = rc;";n%2==1&&(s+="thisRC.z += 1;"),n>1&&(s+="thisRC.y += 1;"),o+=`
+          rEdge || cEdge ? 0. : getA(${o[3]})`}var ah=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let o="";for(let n=0;n<4;n++){let s="thisRC = rc;";n%2==1&&(s+="thisRC.z += 1;"),n>1&&(s+="thisRC.y += 1;"),o+=`
         ${s}
         ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -868,8 +868,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
         ${n>0?"}":""}
       `}this.userCode=`
-      ${_Y(t)}
-      ${zp(e)}
+      ${CY(t)}
+      ${Gp(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -884,12 +884,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function _Y(r){return`
+    `}};function CY(r){return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${zs(["r","c","d"],r)}
+      ${Vs(["r","c","d"],r)}
       return ivec3(r, c, d);
     }
-  `}var Cv=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,o){let n=wR(t,o),s=kR(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=_R(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let l=this.freeTextures[s].shift();return this.usedTextures[s].push(l),l}let i;return n===br.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===br.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===br.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===br.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===br.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=wR(o,n),a=kR(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=_R(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),l=G().get("WEBGL_DELETE_TEXTURE_THRESHOLD");l!==-1&&this._numBytesAllocated>l?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 wY(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;throw new Error(`Unknown internal format ${e}`)}function _R(r,e,t,o,n){let s=kY(e,o),a;if(n){let[l,u]=Xi(r[0],r[1]);a=l*u}else{let[l,u]=ic(r[0],r[1]);a=l*u}let i=wY(t,s);return a*i}function kY(r,e){switch(r){case br.PACKED_2X2_FLOAT32:return bv(e);case br.PACKED_2X2_FLOAT16:return _v(e);case br.UNPACKED_FLOAT32:return gv(e);case br.UNPACKED_FLOAT16:return xv(e);case br.PACKED_4X1_UNSIGNED_BYTE:return yv(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function vY(r){return G().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?br.PACKED_2X2_FLOAT32:br.UNPACKED_FLOAT32:r?br.PACKED_2X2_FLOAT16:br.UNPACKED_FLOAT16}function wR(r,e){if(r===Er.UPLOAD)return br.PACKED_2X2_FLOAT32;if(r===Er.RENDER||r==null)return vY(e);if(r===Er.DOWNLOAD||r===Er.PIXELS)return br.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function kR(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var uo=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+  `}var Tv=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,o){let n=IR(t,o),s=NR(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=CR(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let l=this.freeTextures[s].shift();return this.usedTextures[s].push(l),l}let i;return n===br.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===br.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===br.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===br.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===br.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=IR(o,n),a=NR(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=CR(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),l=U().get("WEBGL_DELETE_TEXTURE_THRESHOLD");l!==-1&&this._numBytesAllocated>l?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 IY(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;throw new Error(`Unknown internal format ${e}`)}function CR(r,e,t,o,n){let s=NY(e,o),a;if(n){let[l,u]=Xi(r[0],r[1]);a=l*u}else{let[l,u]=ac(r[0],r[1]);a=l*u}let i=IY(t,s);return a*i}function NY(r,e){switch(r){case br.PACKED_2X2_FLOAT32:return vv(e);case br.PACKED_2X2_FLOAT16:return Cv(e);case br.UNPACKED_FLOAT32:return _v(e);case br.UNPACKED_FLOAT16:return wv(e);case br.PACKED_4X1_UNSIGNED_BYTE:return kv(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function SY(r){return U().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?br.PACKED_2X2_FLOAT32:br.UNPACKED_FLOAT32:r?br.PACKED_2X2_FLOAT16:br.UNPACKED_FLOAT16}function IR(r,e){if(r===Er.UPLOAD)return br.PACKED_2X2_FLOAT32;if(r===Er.RENDER||r==null)return SY(e);if(r===Er.DOWNLOAD||r===Er.PIXELS)return br.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function NR(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var uo=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -900,11 +900,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},gr="if (isnan(x)) return x;",vR="return x;",Iv="return abs(x);";var CR="return (x >= 0.0) ? x : (exp(x) - 1.0);",IR=gr+`
+    `}},gr="if (isnan(x)) return x;",SR="return x;",Ev="return abs(x);";var TR="return (x >= 0.0) ? x : (exp(x) - 1.0);",ER=gr+`
   return (x < 0.0) ? 0.0 : x;
-`,NR=gr+`
+`,AR=gr+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,sh="return x;";var SR="return x;",TR=`
+`,lh="return x;";var DR="return x;",$R=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -913,7 +913,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,ER=`
+`,RR=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -923,7 +923,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,AR=`
+`,FR=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -933,7 +933,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,Bs=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
+`,Gs=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
       vec4 unaryOperation(vec4 x) {
         ${t}
       }
@@ -944,17 +944,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}};var Nv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,o=Ut("rc",t),n=Le(t),s=bR(t,o),a=o.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
+    `}};var Av=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,o=Ut("rc",t),n=Le(t),s=vR(t,o),a=o.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
       void main() {
         ${n} rc = getOutputCoords();
         vec4 packedInput = getA(${s});
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}};var CY=Sr.whereImpl,IY=1e-7,NY=1e-4,Ox={};function SY(r){return r in Ox||(Ox[r]={}),Ox[r]}var TY=128,EY=600;function AY(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*EY/1024/1024}var Sv=class extends Ws{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Lo(G().getNumber("WEBGL_VERSION"));this.binaryCache=SY(G().getNumber("WEBGL_VERSION")),this.gpgpu=new wv(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 Cv(this.gpgpu),this.numMBBeforeWarning=AY(),this.texData=new Ya(this,vs())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,o){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().getBool("DEBUG"))&&this.checkNumericalProblems(e),o==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:Er.UPLOAD,refCount:1,complexParentRefCount:0}),n}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--}}decComplexRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.complexParentRefCount>0&&t.refCount--}}move(e,t,o,n){if(G().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:o,dtype:n,values:t,usage:Er.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(e){let t=e.dataId;if(this.texData.has(t)){let o=this.texData.get(t);o.refCount--,o.refCount<1&&this.disposeData(t)}}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:l}=t;if(a!=null){let m;l?m=new Bs(i,sh):m=new uo(i,sh);let f=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(o!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let p;if(n==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=N.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let d=this.pendingRead.get(e);return new Promise(h=>d.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:l}=t;if(s!=null){let d;l?d=new Bs(n,sh):d=new uo(n,sh);let h=this.runWebGLProgram(d,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(!G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&G().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let d=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(d.texture,...Il(n))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(a==="complex64"){let d=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=d[0],g=d[1];p=N.mergeRealAndImagArrays(h,g)}else if(u==null)p=this.getValuesFromTexture(e);else{let d=y.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(u,d)}c!=null&&this.disposeIntermediateTensorInfo(c);let m=this.convertAndCacheOnCPU(e,p),f=this.pendingRead.get(e);return this.pendingRead.delete(e),f.forEach(d=>d(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),m}bufferSync(e){let t=this.readSync(e.dataId),o=t;if(e.dtype==="string")try{o=t.map(n=>y.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ce(e.shape,e.dtype,o)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let o=e[t];if(!o$(o))throw G().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${o} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${o} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:o,isPacked:n}=this.texData.get(e),s=y.sizeFromShape(t);if(G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(e),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture,...Il(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let a=G().getBool("WEBGL_PACK")&&n===!0,i=a?Ax(t):t,l=a?new fv(i):new mv(i),u=this.runWebGLProgram(l,[{shape:i,dtype:o,dataId:e}],"float32"),c=this.texData.get(u.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),p}async time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=y.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);i.kernelMs=y.sum(l),i.getExtraProfileInfo=()=>l.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.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 G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;if(this.texData.get(e).complexParentRefCount>0){this.texData.get(e).refCount--;return}this.releaseGPUData(e);let{complexTensorInfos:t}=this.texData.get(e);t!=null&&(this.texData.get(t.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.real),this.texData.get(t.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.imag)),this.texData.delete(e)}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),l=i&&i.origDataId||e,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));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 G().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=vs().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=TY){let o=this.getCPUBackend();return!G().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&o==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),o!=null&&e.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return CY(e.shape,t)}packedUnaryOp(e,t,o){let n=new Bs(e.shape,t);return this.compileAndRun(n,[e],o)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let o=Fx(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,o)}if(G().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Iv,e.dtype);let t=new uo(e.shape,Iv);return this.compileAndRun(t,[e])}makeTensorInfo(e,t,o){let n;if(t==="string"&&o!=null&&o.length>0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){let{dataId:n}=this.makeTensorInfo(e,t,o);return vs().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new Nv(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new vv(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Nl(e.shape),...Sl(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Nl(t),...Sl(t)],a=new nh(s,o),i=!0,l=this.runWebGLProgram(a,[n],e.dtype,null,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:o,shape:n,dtype:s}=t,a=Ax(n),i;o?i=new pv(a):i=new cv(a);let l=!0,u=this.runWebGLProgram(i,[{shape:a,dtype:s,dataId:e}],s,null,l);return{dtype:s,shape:n,dataId:u.dataId}}runWebGLProgram(e,t,o,n,s=!1){let a=this.makeTensorInfo(e.outputShape,o),i=this.texData.get(a.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Cl.DENSE){let h=Il(e.outputShape);i.texShape=h.map(g=>g*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),y.sizeFromShape(a.shape)===0)return i.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],u=t.map(h=>{if(h.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(h.dataId);if(g.texture==null){if(!e.packedInputs&&y.sizeFromShape(h.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:h.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=h.shape)}else if(!!g.isPacked!=!!e.packedInputs)h=g.isPacked?this.unpackTensor(h):this.packTensor(h),l.push(h),g=this.texData.get(h.dataId);else if(g.isPacked&&!ac(g.shape,h.shape)){let x=h,b=h.shape;h.shape=g.shape,h=this.packedReshape(h,b),l.push(h),g=this.texData.get(h.dataId),x.shape=b}return this.uploadToGPU(h.dataId),{shape:h.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:i,isUniform:!1},p=H$(e,u,c),m=this.getAndSaveBinary(p,()=>W$(this.gpgpu,e,u,c)),f=this.activeTimers!=null,d;if(f&&(d=this.startTimer()),j$(this.gpgpu,m,u,c,n),l.forEach(h=>this.disposeIntermediateTensorInfo(h)),f&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)})),!G().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let h=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),h}return a}compileAndRun(e,t,o,n,s=!1){o=o||t[0].dtype;let a=this.runWebGLProgram(e,t,o,n,s);return vs().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(G().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=V(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=G().getBool("DEBUG");G().set("DEBUG",!1);let t=this.abs(le(1e-8)).dataSync()[0];if(G().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?IY:NY}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:l}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let p=t.texShape;if(p==null&&(p=g$(o,l),t.texShape=p),s!=null){let m=Ax(o),f,d=p[1],h=p[0],g=s instanceof Uint8Array;l?([d,h]=Xi(p[0],p[1]),f=new hv(m,[h,d],g)):f=new dv(m,[h,d],g);let x=this.makeTensorInfo([h,d],n);g?this.texData.get(x.dataId).usage=Er.PIXELS:this.texData.get(x.dataId).usage=Er.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(x.dataId),d,h,s);let b=!0,_=this.runWebGLProgram(f,[x],n,null,b),w=this.texData.get(_.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(x),this.texData.delete(_.dataId),t.values=null,u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,i,n,l);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return this.releaseGPUData(e),t!=null&&(o.values=DY(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}};function DY(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;o<t.length;++o)t[o]=Math.round(r[o]);return t}else throw new Error(`Unknown dtype ${e}`)}var DR="3.0.0";Bc.isBrowser()&&yu("webgl",()=>new Sv,2);var Px=`
+    `}};var TY=Sr.whereImpl,EY=1e-7,AY=1e-4,Vx={};function DY(r){return r in Vx||(Vx[r]={}),Vx[r]}var $Y=128,RY=600;function FY(){return U().global.screen==null?1024:U().global.screen.height*U().global.screen.width*window.devicePixelRatio*RY/1024/1024}var Kp=class extends js{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!U().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Bo(U().getNumber("WEBGL_VERSION"));this.binaryCache=DY(U().getNumber("WEBGL_VERSION")),this.gpgpu=new Iv(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 Tv(this.gpgpu),this.numMBBeforeWarning=FY(),this.texData=new Ya(this,Fo())}nextDataId(){return Kp.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,o){if((U().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||U().getBool("DEBUG"))&&this.checkNumericalProblems(e),o==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:Er.UPLOAD,refCount:1}),n}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,o,n,s){if(U().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:o,dtype:n,values:t,usage:Er.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:l}=t;if(a!=null){let m;l?m=new Gs(i,lh):m=new uo(i,lh);let f=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(o!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let p;if(n==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=N.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let d=this.pendingRead.get(e);return new Promise(h=>d.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:l}=t;if(s!=null){let d;l?d=new Gs(n,lh):d=new uo(n,lh);let h=this.runWebGLProgram(d,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(!U().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&U().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&U().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let d=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(d.texture,...Il(n))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(a==="complex64"){let d=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=d[0],g=d[1];p=N.mergeRealAndImagArrays(h,g)}else if(u==null)p=this.getValuesFromTexture(e);else{let d=y.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(u,d)}c!=null&&this.disposeIntermediateTensorInfo(c);let m=this.convertAndCacheOnCPU(e,p),f=this.pendingRead.get(e);return this.pendingRead.delete(e),f.forEach(d=>d(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Fo().removeDataId(e,this),this.pendingDeletes--),m}bufferSync(e){let t=this.readSync(e.dataId),o=t;if(e.dtype==="string")try{o=t.map(n=>y.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ce(e.shape,e.dtype,o)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let o=e[t];if(!a$(o))throw U().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${o} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${o} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:o,isPacked:n}=this.texData.get(e),s=y.sizeFromShape(t);if(U().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(e),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture,...Il(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let a=U().getBool("WEBGL_PACK")&&n===!0,i=a?Px(t):t,l=a?new xv(i):new gv(i),u=this.runWebGLProgram(l,[{shape:i,dtype:o,dataId:e}],"float32"),c=this.texData.get(u.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),p}timerAvailable(){return U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=y.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);i.kernelMs=y.sum(l),i.getExtraProfileInfo=()=>l.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),l=i&&i.origDataId||e,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return U().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Fo().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=$Y){let o=this.getCPUBackend();return!U().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&o==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),o!=null&&e.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return TY(e.shape,t)}packedUnaryOp(e,t,o){let n=new Gs(e.shape,t),s=this.compileAndRun(n,[e],o);return Fo().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=Bx(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(U().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Ev,e.dtype);let t=new uo(e.shape,Ev),o=this.compileAndRun(t,[e]);return Fo().makeTensorFromDataId(o.dataId,o.shape,o.dtype)}makeTensorInfo(e,t,o){let n;if(t==="string"&&o!=null&&o.length>0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){let{dataId:n}=this.makeTensorInfo(e,t,o);return Fo().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new Av(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Sv(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Nl(e.shape),...Sl(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Nl(t),...Sl(t)],a=new ah(s,o),i=!0,l=this.runWebGLProgram(a,[n],e.dtype,null,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:o,shape:n,dtype:s}=t,a=Px(n),i;o?i=new hv(a):i=new dv(a);let l=!0,u=this.runWebGLProgram(i,[{shape:a,dtype:s,dataId:e}],s,null,l);return{dtype:s,shape:n,dataId:u.dataId}}runWebGLProgram(e,t,o,n,s=!1){let a=this.makeTensorInfo(e.outputShape,o),i=this.texData.get(a.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Cl.DENSE){let h=Il(e.outputShape);i.texShape=h.map(g=>g*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),y.sizeFromShape(a.shape)===0)return i.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],u=t.map(h=>{if(h.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(h.dataId);if(g.texture==null){if(!e.packedInputs&&y.sizeFromShape(h.shape)<=U().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:h.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=h.shape)}else if(!!g.isPacked!=!!e.packedInputs)h=g.isPacked?this.unpackTensor(h):this.packTensor(h),l.push(h),g=this.texData.get(h.dataId);else if(g.isPacked&&!lc(g.shape,h.shape)){let x=h,b=h.shape;h.shape=g.shape,h=this.packedReshape(h,b),l.push(h),g=this.texData.get(h.dataId),x.shape=b}return this.uploadToGPU(h.dataId),{shape:h.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:i,isUniform:!1},p=Y$(e,u,c),m=this.getAndSaveBinary(p,()=>q$(this.gpgpu,e,u,c)),f=this.activeTimers!=null,d;if(f&&(d=this.startTimer()),X$(this.gpgpu,m,u,c,n),l.forEach(h=>this.disposeIntermediateTensorInfo(h)),f&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)})),!U().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let h=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),h}return a}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(U().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!U().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=U().getBool("DEBUG");U().set("DEBUG",!1);let t=this.abs(le(1e-8)).dataSync()[0];if(U().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?EY:AY}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:l}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let p=t.texShape;if(p==null&&(p=_$(o,l),t.texShape=p),s!=null){let m=Px(o),f,d=p[1],h=p[0],g=s instanceof Uint8Array;l?([d,h]=Xi(p[0],p[1]),f=new bv(m,[h,d],g)):f=new yv(m,[h,d],g);let x=this.makeTensorInfo([h,d],n);g?this.texData.get(x.dataId).usage=Er.PIXELS:this.texData.get(x.dataId).usage=Er.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(x.dataId),d,h,s);let b=!0,_=this.runWebGLProgram(f,[x],n,null,b),w=this.texData.get(_.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(x),this.texData.delete(_.dataId),t.values=null,u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,i,n,l);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return this.releaseGPUData(e),t!=null&&(o.values=OY(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}};Kp.nextDataId=0;function OY(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;o<t.length;++o)t[o]=Math.round(r[o]);return t}else throw new Error(`Unknown dtype ${e}`)}var OR="3.1.0";Vc.isBrowser()&&bu("webgl",()=>new Kp,2);var Gx=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`;var Qn=class{constructor(e,t,o){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,o),this.userCode=`
+`;var os=class{constructor(e,t,o){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,o),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -969,7 +969,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;var Vs=class{constructor(e,t,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length,a="";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`
+`;var Ws=class{constructor(e,t,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length,a="";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`
           result.y = 0.;
           result.z = 0.;
           result.w = 0.;
@@ -1001,21 +1001,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function jt(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var $R={kernelName:us,backendName:"webgl",kernelFunc:jt};function co(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.texData.get(s.dataId),i=jt({inputs:{x:o},backend:t}),l=t.texData.get(i.dataId);l.complexParentRefCount++;let u=jt({inputs:{x:n},backend:t}),c=t.texData.get(u.dataId);return c.complexParentRefCount++,a.complexTensorInfos={real:i,imag:u},s}var RR={kernelName:Hl,backendName:"webgl",kernelFunc:co};var Tv="return (a < 0.) ? b * a : a;",Ev=`
+    `}};function jt(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var PR={kernelName:Do,backendName:"webgl",kernelFunc:jt};function co(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.texData.get(s.dataId),i=jt({inputs:{x:o},backend:t}),l=jt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:l},s}var MR={kernelName:Hl,backendName:"webgl",kernelFunc:co};var Dv="return (a < 0.) ? b * a : a;",$v=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function $Y(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Vs(Ev,n.shape,a.shape):new Qn(Tv,n.shape,a.shape),l=t.runWebGLProgram(i,[n,a],n.dtype);return t.disposeIntermediateTensorInfo(a),l}var FR={kernelName:sn,backendName:"webgl",kernelFunc:$Y};var Av="return (a < 0.) ? b * a : a;",Dv=`
+`;function PY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ws($v,n.shape,a.shape):new os(Dv,n.shape,a.shape),l=t.runWebGLProgram(i,[n,a],n.dtype);return t.disposeIntermediateTensorInfo(a),l}var LR={kernelName:cn,backendName:"webgl",kernelFunc:PY};var Rv="return (a < 0.) ? b * a : a;",Fv=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function RY(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Vs(Dv,o.shape,n.shape):new Qn(Av,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)}var OR={kernelName:yn,backendName:"webgl",kernelFunc:RY};var Mx="if (isnan(x)) return x;",PR=`
+`;function MY(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ws(Fv,o.shape,n.shape):new os(Rv,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)}var zR={kernelName:kn,backendName:"webgl",kernelFunc:MY};var Wx="if (isnan(x)) return x;",BR=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,MR=`
+`,VR=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function ve({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,l=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let p=i.texData.get(a.dataId),m=t(p.values,l);return i.makeTensorInfo(a.shape,l,m)}let u=G().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Bs(a.shape,e):c=new uo(a.shape,r),i.runWebGLProgram(c,[a],l)}}function it({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:l,b:u}=a,c=i;if(o&&l.dtype==="complex64"){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(_=>{let[w,v]=_,$={dataId:w.dataId,dtype:w.dtype,shape:l.shape},A={dataId:v.dataId,dtype:v.dtype,shape:u.shape},R=new Qn(r,l.shape,u.shape);return c.runWebGLProgram(R,[$,A],mr(w.dtype,v.dtype))}),b=co({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||mr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&n!=null){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=n(l.shape,u.shape,d.values,h.values,p),b=c.makeTensorInfo(x,p),_=c.texData.get(b.dataId);return _.values=g,b}let m=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,f;return m?f=new Vs(e,l.shape,u.shape,t):f=new Qn(r,l.shape,u.shape),c.runWebGLProgram(f,[l,u],p)}}function El(r,e=!1){if(r==="linear")return e?SR:vR;if(r==="relu")return e?ER:IR;if(r==="elu")return e?TR:CR;if(r==="relu6")return e?AR:NR;if(r==="prelu")return e?Dv:Av;if(r==="leakyrelu")return e?Ev:Tv;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var ih=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,l=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o;let c=n?e[1]:e[2],p=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(l?g=`vec4 activation(vec4 a) {
+`;function ve({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,l=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let p=i.texData.get(a.dataId),m=t(p.values,l);return i.makeTensorInfo(a.shape,l,m)}let u=U().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Gs(a.shape,e):c=new uo(a.shape,r),i.runWebGLProgram(c,[a],l)}}function it({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:l,b:u}=a,c=i;if(o&&l.dtype==="complex64"){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(_=>{let[w,v]=_,$={dataId:w.dataId,dtype:w.dtype,shape:l.shape},A={dataId:v.dataId,dtype:v.dtype,shape:u.shape},R=new os(r,l.shape,u.shape);return c.runWebGLProgram(R,[$,A],mr(w.dtype,v.dtype))}),b=co({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||mr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&n!=null){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=n(l.shape,u.shape,d.values,h.values,p),b=c.makeTensorInfo(x,p),_=c.texData.get(b.dataId);return _.values=g,b}let m=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,f;return m?f=new Ws(e,l.shape,u.shape,t):f=new os(r,l.shape,u.shape),c.runWebGLProgram(f,[l,u],p)}}function El(r,e=!1){if(r==="linear")return e?DR:SR;if(r==="relu")return e?RR:ER;if(r==="elu")return e?$R:TR;if(r==="relu6")return e?FR:AR;if(r==="prelu")return e?Fv:Rv;if(r==="leakyrelu")return e?$v:Dv;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var uh=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,l=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o;let c=n?e[1]:e[2],p=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(l?g=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:u?g=`vec4 activation(vec4 a) {
@@ -1054,7 +1054,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};var $v={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Lx=class{constructor(e,t,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,o),this.userCode=`
+    `}};var Ov={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Ux=class{constructor(e,t,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,o),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1067,7 +1067,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}};var LR="return a * b;";function Rv(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=N.upcastType(o.dtype,n.dtype);if(o.dtype==="complex64"){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),u=new Lx($v.REAL,o.shape,n.shape),c=new Lx($v.IMAG,o.shape,n.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:o.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:n.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:n.shape}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=co({inputs:{real:m,imag:f},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}if(t.shouldExecuteOnCPU([o,n])){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),[u,c]=lR(o.shape,n.shape,i.values,l.values,s),p=t.makeTensorInfo(c,s),m=t.texData.get(p.dataId);return m.values=u,p}let a;return G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new Vs(LR,o.shape,n.shape):a=new Qn(LR,o.shape,n.shape),t.runWebGLProgram(a,[o,n],s)}var zR={kernelName:dn,backendName:"webgl",kernelFunc:Rv};function BR(r,e,t){let o=[Nl(r.shape),...Sl(r.shape)],n={dtype:r.dtype,shape:o,dataId:r.dataId},s=[Nl(e),...Sl(e)],a=new nh(s,o),i=!0,l=t.runWebGLProgram(a,[n],r.dtype,null,i);return{dataId:l.dataId,shape:e,dtype:l.dtype}}function ce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{shape:s}=o,a=t,i=y.sizeFromShape(n.shape),l=y.inferFromImplicitShape(s,i),u=y.sizeFromShape(l);y.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!ac(n.shape,l)&&!(c.texture!==null&&ac(c.shape,l))?BR(n,l,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:l,dtype:n.dtype})}var VR={kernelName:fs,backendName:"webgl",kernelFunc:ce};var zx=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,l=o%4,u="sumValue += dot(values, ones);";if(t!=null){let p=1/t;u=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%o>0&&(c=`
+    `}};var GR="return a * b;";function Pv(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=N.upcastType(o.dtype,n.dtype);if(o.dtype==="complex64"){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),u=new Ux(Ov.REAL,o.shape,n.shape),c=new Ux(Ov.IMAG,o.shape,n.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:o.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:n.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:n.shape}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=co({inputs:{real:m,imag:f},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}if(t.shouldExecuteOnCPU([o,n])){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),[u,c]=mR(o.shape,n.shape,i.values,l.values,s),p=t.makeTensorInfo(c,s),m=t.texData.get(p.dataId);return m.values=u,p}let a;return U().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new Ws(GR,o.shape,n.shape):a=new os(GR,o.shape,n.shape),t.runWebGLProgram(a,[o,n],s)}var WR={kernelName:yn,backendName:"webgl",kernelFunc:Pv};function UR(r,e,t){let o=[Nl(r.shape),...Sl(r.shape)],n={dtype:r.dtype,shape:o,dataId:r.dataId},s=[Nl(e),...Sl(e)],a=new ah(s,o),i=!0,l=t.runWebGLProgram(a,[n],r.dtype,null,i);return{dataId:l.dataId,shape:e,dtype:l.dtype}}function ce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{shape:s}=o,a=t,i=y.sizeFromShape(n.shape),l=y.inferFromImplicitShape(s,i),u=y.sizeFromShape(l);y.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!lc(n.shape,l)&&!(c.texture!==null&&lc(c.shape,l))?UR(n,l,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:l,dtype:n.dtype})}var jR={kernelName:gs,backendName:"webgl",kernelFunc:ce};var jx=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,l=o%4,u="sumValue += dot(values, ones);";if(t!=null){let p=1/t;u=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%o>0&&(c=`
         if (inIdx < 0 || inIdx >= ${s}) {
           return 0.0;
         }
@@ -1120,7 +1120,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(sumValue);
       }
-    `}};var Fv=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i="0.0",l="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",l="min"):t==="max"&&(i="-1.0 / 1e-20",l="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(o/4)*4,p=o%4,m=`
+    `}};var Mv=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i="0.0",l="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",l="min"):t==="max"&&(i="-1.0 / 1e-20",l="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(o/4)*4,p=o%4,m=`
       if (${t==="sum"}) {
         sumValue += dot(values, ones);
       } else if (${t==="prod"}) {
@@ -1205,12 +1205,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${u});
       }
-    `}};function FY(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=N.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Io(r,e,t,o){let n=FY(r.shape),s=r;for(let a=0;a<n.length;a++){let{inSize:i,windowSize:l,outSize:u}=n[a],c,p;t==="mean"?c=a===0?new zx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},i):new zx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u}):c=new Fv({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},t),p=s,s=o.runWebGLProgram(c,[s],e),p.dataId!==r.dataId&&o.disposeIntermediateTensorInfo(p)}return s}var Ov=class{constructor(e,t){this.variableNames=["A"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[t[a]];this.outputShape=o,this.rank=o.length;let n=Le(this.rank),s=OY(t);this.userCode=`
+    `}};function LY(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=N.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Io(r,e,t,o){let n=LY(r.shape),s=r;for(let a=0;a<n.length;a++){let{inSize:i,windowSize:l,outSize:u}=n[a],c,p;t==="mean"?c=a===0?new jx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},i):new jx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u}):c=new Mv({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},t),p=s,s=o.runWebGLProgram(c,[s],e),p.dataId!==r.dataId&&o.disposeIntermediateTensorInfo(p)}return s}var Lv=class{constructor(e,t){this.variableNames=["A"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[t[a]];this.outputShape=o,this.rank=o.length;let n=Le(this.rank),s=zY(t);this.userCode=`
     void main() {
       ${n} resRC = getOutputCoords();
       setOutput(getA(${s}));
     }
-    `}};function OY(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(e);for(let n=0;n<r.length;n++)o[r[n]]=t[n];return o.join()}var Pv=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let o=new Array(e.length);for(let c=0;c<o.length;c++)o[c]=e[t[c]];if(this.outputShape=o,this.rank=o.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Le(this.rank),s=kv("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,l=`++${s[this.rank-1]} < ${o[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
+    `}};function zY(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(e);for(let n=0;n<r.length;n++)o[r[n]]=t[n];return o.join()}var zv=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let o=new Array(e.length);for(let c=0;c<o.length;c++)o[c]=e[t[c]];if(this.outputShape=o,this.rank=o.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Le(this.rank),s=Nv("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,l=`++${s[this.rank-1]} < ${o[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
     void main() {
       ${n} rc = getOutputCoords();
       vec4 result = vec4(0.);
@@ -1227,14 +1227,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function Al(r,e,t){let o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Pv(r.shape,e):new Ov(r.shape,e);return t.runWebGLProgram(o,[r],r.dtype)}function GR(r,e,t,o){let n=e,s=r.shape.length,a=y.parseAxisParam(n,r.shape),i=a,l=N.getAxesPermutation(i,s),u=l!=null,c=r;u&&(c=Al(r,l,o),i=N.getInnerMostAxes(i.length,s)),N.assertAxesAreInnerMostDims("sum",i,s);let[p,m]=N.computeOutAndReduceShapes(c.shape,i),f=p;t&&(f=N.expandShapeToKeepDim(p,a));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=ce({inputs:{x:c},attrs:{shape:[g,d]},backend:o}),b=hu(r.dtype),_=Io(x,b,"sum",o),w=ce({inputs:{x:_},attrs:{shape:f},backend:o});return o.disposeIntermediateTensorInfo(x),o.disposeIntermediateTensorInfo(_),u&&o.disposeIntermediateTensorInfo(c),w}function ah(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return GR(n,s,a,t)}var WR={kernelName:Tn,backendName:"webgl",kernelFunc:ah};function Lt(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{perm:s}=o,a=t,i=n.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=n.shape[s[c]];let u;if(a.shouldExecuteOnCPU([n])){let p=a.texData.get(n.dataId).values,m=Up(p,n.shape,n.dtype,s,l);u=a.makeTensorInfo(l,n.dtype);let f=a.texData.get(u.dataId);f.values=m}else u=Al(n,s,a);return u}var UR={kernelName:Rn,backendName:"webgl",kernelFunc:Lt};var Mv=1e3;function uc({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:l=null}){let u=r.shape.length,c=e.shape.length,p=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],f=t?r.shape[u-1]:r.shape[u-2],d=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),_=x===b||x===1||b===1;y.assert(u>=2&&c>=2&&_,()=>`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 (${h}) and (${g}).`);let v=(x>b?r.shape.slice(0,-2):e.shape.slice(0,-2)).concat([f,d]);y.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let $=t?[x,p,f]:[x,f,p],A=o?[b,d,m]:[b,m,d],R=ce({inputs:{x:r},backend:n,attrs:{shape:$}}),M=ce({inputs:{x:e},backend:n,attrs:{shape:A}}),z=[R,M],W=Math.max(x,b),U=t?R.shape[1]:R.shape[2],q=s!=null,Z=a!=null,X=l==="leakyrelu",Y=l!=null?El(l,!0):null,te=q||Z||X||Y!=null,K;if((f===1||d===1)&&U>Mv&&te===!1){let ie=R,se=M;t&&(ie=Lt({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),z.push(ie)),o&&(se=Lt({inputs:{x:M},backend:n,attrs:{perm:[0,2,1]}}),z.push(se));let pe=d!==1,ae=d===1,xe=ie;pe&&(xe=ce({inputs:{x:ie},backend:n,attrs:{shape:[W,U,1]}}),z.push(xe));let ge=d===1?2:1,_e=se;ae&&(_e=ce({inputs:{x:se},backend:n,attrs:{shape:[W,1,U]}}),z.push(_e));let ke=Rv({inputs:{a:xe,b:_e},backend:n});K=ah({inputs:{x:ke},backend:n,attrs:{axis:ge,keepDims:!0}}),z.push(ke)}else{let ie=mr(r.dtype,e.dtype),se=new ih($,A,[W,f,d],t,o,q,Y,Z,X),pe=[R,M];if(s!=null&&pe.push(s),Z&&pe.push(a),X){let ae=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));pe.push(ae),z.push(ae)}K=n.runWebGLProgram(se,pe,ie)}let re=ce({inputs:{x:K},backend:n,attrs:{shape:v}});z.push(K);for(let ie of z)n.disposeIntermediateTensorInfo(ie);return re}function PY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=o;return uc({a:n,b:s,transposeA:l,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var jR={kernelName:bs,backendName:"webgl",kernelFunc:PY};var HR="return abs(x);";function MY(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=t.texData.get(o.dataId),a=Fx(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Bs(o.shape,HR):n=new uo(o.shape,HR),t.runWebGLProgram(n,[o],o.dtype)}var qR={kernelName:ss,backendName:"webgl",kernelFunc:MY};var LY=gr+`
+    `}};function Al(r,e,t){let o=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zv(r.shape,e):new Lv(r.shape,e);return t.runWebGLProgram(o,[r],r.dtype)}function HR(r,e,t,o){let n=e,s=r.shape.length,a=y.parseAxisParam(n,r.shape),i=a,l=N.getAxesPermutation(i,s),u=l!=null,c=r;u&&(c=Al(r,l,o),i=N.getInnerMostAxes(i.length,s)),N.assertAxesAreInnerMostDims("sum",i,s);let[p,m]=N.computeOutAndReduceShapes(c.shape,i),f=p;t&&(f=N.expandShapeToKeepDim(p,a));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=ce({inputs:{x:c},attrs:{shape:[g,d]},backend:o}),b=gu(r.dtype),_=Io(x,b,"sum",o),w=ce({inputs:{x:_},attrs:{shape:f},backend:o});return o.disposeIntermediateTensorInfo(x),o.disposeIntermediateTensorInfo(_),u&&o.disposeIntermediateTensorInfo(c),w}function ch(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return HR(n,s,a,t)}var qR={kernelName:$n,backendName:"webgl",kernelFunc:ch};function Lt(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{perm:s}=o,a=t,i=n.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=n.shape[s[c]];let u;if(a.shouldExecuteOnCPU([n])){let p=a.texData.get(n.dataId).values,m=qp(p,n.shape,n.dtype,s,l);u=a.makeTensorInfo(l,n.dtype);let f=a.texData.get(u.dataId);f.values=m}else u=Al(n,s,a);return u}var KR={kernelName:Mn,backendName:"webgl",kernelFunc:Lt};var Bv=1e3;function cc({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:l=null}){let u=r.shape.length,c=e.shape.length,p=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],f=t?r.shape[u-1]:r.shape[u-2],d=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),_=x===b||x===1||b===1;y.assert(u>=2&&c>=2&&_,()=>`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 (${h}) and (${g}).`);let v=(x>b?r.shape.slice(0,-2):e.shape.slice(0,-2)).concat([f,d]);y.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let $=t?[x,p,f]:[x,f,p],A=o?[b,d,m]:[b,m,d],R=ce({inputs:{x:r},backend:n,attrs:{shape:$}}),M=ce({inputs:{x:e},backend:n,attrs:{shape:A}}),V=[R,M],G=Math.max(x,b),W=t?R.shape[1]:R.shape[2],q=s!=null,Z=a!=null,X=l==="leakyrelu",Y=l!=null?El(l,!0):null,te=q||Z||X||Y!=null,K;if((f===1||d===1)&&W>Bv&&te===!1){let ie=R,se=M;t&&(ie=Lt({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),V.push(ie)),o&&(se=Lt({inputs:{x:M},backend:n,attrs:{perm:[0,2,1]}}),V.push(se));let pe=d!==1,ae=d===1,xe=ie;pe&&(xe=ce({inputs:{x:ie},backend:n,attrs:{shape:[G,W,1]}}),V.push(xe));let ge=d===1?2:1,_e=se;ae&&(_e=ce({inputs:{x:se},backend:n,attrs:{shape:[G,1,W]}}),V.push(_e));let ke=Pv({inputs:{a:xe,b:_e},backend:n});K=ch({inputs:{x:ke},backend:n,attrs:{axis:ge,keepDims:!0}}),V.push(ke)}else{let ie=mr(r.dtype,e.dtype),se=new uh($,A,[G,f,d],t,o,q,Y,Z,X),pe=[R,M];if(s!=null&&pe.push(s),Z&&pe.push(a),X){let ae=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));pe.push(ae),V.push(ae)}K=n.runWebGLProgram(se,pe,ie)}let re=ce({inputs:{x:K},backend:n,attrs:{shape:v}});V.push(K);for(let ie of V)n.disposeIntermediateTensorInfo(ie);return re}function BY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=o;return cc({a:n,b:s,transposeA:l,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var XR={kernelName:ks,backendName:"webgl",kernelFunc:BY};var YR="return abs(x);";function VY(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=t.texData.get(o.dataId),a=Bx(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return U().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Gs(o.shape,YR):n=new uo(o.shape,YR),t.runWebGLProgram(n,[o],o.dtype)}var ZR={kernelName:us,backendName:"webgl",kernelFunc:VY};var GY=gr+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,zY=ve({opSnippet:LY}),KR={kernelName:qs,backendName:"webgl",kernelFunc:zY};var BY=gr+`
+`,WY=ve({opSnippet:GY}),JR={kernelName:Ks,backendName:"webgl",kernelFunc:WY};var UY=gr+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,VY=ve({opSnippet:BY}),XR={kernelName:Ks,backendName:"webgl",kernelFunc:VY};var YR="return a + b;",GY=it({opSnippet:YR,packedOpSnippet:YR,supportsComplex:!0,cpuKernelImpl:q$}),ZR={kernelName:xo,backendName:"webgl",kernelFunc:GY};var Lv=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,jY=ve({opSnippet:UY}),QR={kernelName:Xs,backendName:"webgl",kernelFunc:jY};var eF="return a + b;",HY=it({opSnippet:eF,packedOpSnippet:eF,supportsComplex:!0,cpuKernelImpl:Z$}),tF={kernelName:xo,backendName:"webgl",kernelFunc:HY};var Vv=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
       void main() {
         ${o.join(`
         `)}
@@ -1242,7 +1242,7 @@ return log(x + sqrt(x * x - 1.0));`,VY=ve({opSnippet:BY}),XR={kernelName:Ks,back
         float result = ${n};
         setOutput(result);
       }
-    `}};var zv=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
+    `}};var Gv=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
       void main() {
         ${o.join(`
         `)}
@@ -1250,7 +1250,7 @@ return log(x + sqrt(x * x - 1.0));`,VY=ve({opSnippet:BY}),XR={kernelName:Ks,back
         vec4 result = ${n};
         setOutput(result);
       }
-    `}};function Bx(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return jt({inputs:{x:o[0]},backend:t});if(o.length>G().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(o.length/2),u=Bx({inputs:o.slice(0,l),backend:t}),c=Bx({inputs:o.slice(l),backend:t});return Bx({inputs:[u,c],backend:t})}let n=o.map(l=>l.dtype).reduce((l,u)=>mr(l,u)),s=o.map(l=>l.shape),i=G().getBool("WEBGL_PACK")?new zv(o[0].shape,s):new Lv(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var JR={kernelName:Uo,backendName:"webgl",kernelFunc:Bx};function WY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("all",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=ce({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Io(h,h.dtype,"all",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=ce({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=ce({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var QR={kernelName:Vl,backendName:"webgl",kernelFunc:WY};function UY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("any",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=ce({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Io(h,h.dtype,"any",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=ce({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=ce({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var eF={kernelName:Gl,backendName:"webgl",kernelFunc:UY};var Bv=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=o?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function Hx(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return jt({inputs:{x:o[0]},backend:t});if(o.length>U().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(o.length/2),u=Hx({inputs:o.slice(0,l),backend:t}),c=Hx({inputs:o.slice(l),backend:t});return Hx({inputs:[u,c],backend:t})}let n=o.map(l=>l.dtype).reduce((l,u)=>mr(l,u)),s=o.map(l=>l.shape),i=U().getBool("WEBGL_PACK")?new Gv(o[0].shape,s):new Vv(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var rF={kernelName:qo,backendName:"webgl",kernelFunc:Hx};function qY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("all",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=ce({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Io(h,h.dtype,"all",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=ce({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=ce({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var oF={kernelName:Vl,backendName:"webgl",kernelFunc:qY};function KY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("any",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=ce({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Io(h,h.dtype,"any",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=ce({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=ce({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var nF={kernelName:Gl,backendName:"webgl",kernelFunc:KY};var Wv=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=o?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1270,7 +1270,7 @@ return log(x + sqrt(x * x - 1.0));`,VY=ve({opSnippet:BY}),XR={kernelName:Ks,back
         }
         setOutput(float(bestIndex));
       }
-    `}};var Vv=class{constructor(e,t,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,l=i.length,u=Le(l),c=Ut("coords",l),p,m;if(a===1){m=l+1;let R=Le(m);p=`
+    `}};var Uv=class{constructor(e,t,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,l=i.length,u=Le(l),c=Ut("coords",l),p,m;if(a===1){m=l+1;let R=Le(m);p=`
         ${R} sourceLocR = ${R}(${c.join()}, 0);
         ++${c[l-1]};
         ${R} sourceLocG = ${R}(${c.join()}, 0);
@@ -1332,23 +1332,23 @@ return log(x + sqrt(x * x - 1.0));`,VY=ve({opSnippet:BY}),XR={kernelName:Ks,back
         }
         setOutput(bestIndex);
       }
-    `}};function tF(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=N.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},l=new Bv(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=tF(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function rF(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=N.computeOptimalWindowSize(s),i=new Vv(n,a,t,o==null),l=o==null?[e]:[e,o],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=rF(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function Vx(r,e,t,o){let n=[t];if(N.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!G().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],[a,i]=N.computeOutAndReduceShapes(e.shape,n),l=y.sizeFromShape(i),u=ce({inputs:{x:e},backend:r,attrs:{shape:[-1,l]}});s.push(u);let c=tF(r,u,o);s.push(c);let p=ce({inputs:{x:c},backend:r,attrs:{shape:a}});return s.forEach(m=>r.disposeIntermediateTensorInfo(m)),p}return rF(r,e,o)}function jY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Lt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=Vx(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var oF={kernelName:jo,backendName:"webgl",kernelFunc:jY};function HY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Lt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=Vx(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var nF={kernelName:oa,backendName:"webgl",kernelFunc:HY};var qY=gr+`
+    `}};function sF(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=N.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},l=new Wv(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=sF(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function iF(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=N.computeOptimalWindowSize(s),i=new Uv(n,a,t,o==null),l=o==null?[e]:[e,o],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=iF(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function qx(r,e,t,o){let n=[t];if(N.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!U().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],[a,i]=N.computeOutAndReduceShapes(e.shape,n),l=y.sizeFromShape(i),u=ce({inputs:{x:e},backend:r,attrs:{shape:[-1,l]}});s.push(u);let c=sF(r,u,o);s.push(c);let p=ce({inputs:{x:c},backend:r,attrs:{shape:a}});return s.forEach(m=>r.disposeIntermediateTensorInfo(m)),p}return iF(r,e,o)}function XY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Lt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=qx(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var aF={kernelName:Ko,backendName:"webgl",kernelFunc:XY};function YY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Lt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=qx(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var lF={kernelName:oa,backendName:"webgl",kernelFunc:YY};var ZY=gr+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,KY=ve({opSnippet:qY}),sF={kernelName:Xs,backendName:"webgl",kernelFunc:KY};var XY=gr+"return log(x + sqrt(x * x + 1.0));",YY=ve({opSnippet:XY}),iF={kernelName:Ys,backendName:"webgl",kernelFunc:YY};var ZY=gr+`
+`,JY=ve({opSnippet:ZY}),uF={kernelName:Ys,backendName:"webgl",kernelFunc:JY};var QY=gr+"return log(x + sqrt(x * x + 1.0));",e7=ve({opSnippet:QY}),cF={kernelName:Zs,backendName:"webgl",kernelFunc:e7};var t7=gr+`
   return atan(x);
-`,JY=ve({opSnippet:ZY}),aF={kernelName:Zs,backendName:"webgl",kernelFunc:JY};var QY=PR+`
+`,r7=ve({opSnippet:t7}),pF={kernelName:Js,backendName:"webgl",kernelFunc:r7};var o7=BR+`
   return atan(a, b);
-`,e7=`
+`,n7=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+MR+`
+  `+VR+`
   return result;
-`,t7=it({opSnippet:QY,packedOpSnippet:e7}),lF={kernelName:Qs,backendName:"webgl",kernelFunc:t7};var r7=gr+`
+`,s7=it({opSnippet:o7,packedOpSnippet:n7}),mF={kernelName:ei,backendName:"webgl",kernelFunc:s7};var i7=gr+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelName:Js,backendName:"webgl",kernelFunc:o7};var Zi=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let R=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,a7=ve({opSnippet:i7}),fF={kernelName:Qs,backendName:"webgl",kernelFunc:a7};var Zi=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let R=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${l});
         const ivec2 pads = ivec2(${f}, ${d});
 
@@ -1489,7 +1489,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         }
         setOutput(${w});
       }
-    `}},cc=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,l=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,m=e.dilationWidth,f=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let _=t==="avg",w="0.0";if(_||(w="-1.0 / 1e-20"),o){let z=">=";this.userCode=`
+    `}},pc=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,l=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,m=e.dilationWidth,f=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let _=t==="avg",w="0.0";if(_||(w="-1.0 / 1e-20"),o){let V=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${i}, ${l}, ${u});
         const ivec3 pads = ivec3(${g}, ${x}, ${b});
@@ -1540,7 +1540,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
                 // use the current value.
                 float currMinMaxValue = mix(
                     value, minMaxValue, minMaxValueFound);
-                if (value ${z} currMinMaxValue) {
+                if (value ${V} currMinMaxValue) {
                   minMaxValue = value;
                   minMaxValueFound = 1.0;
                   minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${h} +
@@ -1652,7 +1652,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
           setOutput(${$});
         }
       }
-    `}};function n7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Yi(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:n},backend:t});let p=new Zi(c,"avg",!1);return t.runWebGLProgram(p,[n],"float32")}var cF={kernelName:Ho,backendName:"webgl",kernelFunc:n7};function s7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=o,c=[1,1,1],p=N.computePool3DInfo(n.shape,s,a,c,i,l,u),m=new cc(p,"avg",!1);return t.runWebGLProgram(m,[n],"float32")}var pF={kernelName:na,backendName:"webgl",kernelFunc:s7};var Gv=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`
+    `}};function l7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Yi(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:n},backend:t});let p=new Zi(c,"avg",!1);return t.runWebGLProgram(p,[n],"float32")}var dF={kernelName:Xo,backendName:"webgl",kernelFunc:l7};function u7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=o,c=[1,1,1],p=N.computePool3DInfo(n.shape,s,a,c,i,l,u),m=new pc(p,"avg",!1);return t.runWebGLProgram(m,[n],"float32")}var hF={kernelName:na,backendName:"webgl",kernelFunc:u7};var jv=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`
       const ivec2 pads = ivec2(${c}, ${p});
       const float avgMultiplier = float(${m});
 
@@ -1694,7 +1694,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},Wv=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=p-1-e.padInfo.front,h=m-1-e.padInfo.top,g=f-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`
+    `}},Hv=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=p-1-e.padInfo.front,h=m-1-e.padInfo.top,g=f-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`
       const ivec3 pads = ivec3(${d}, ${h}, ${g});
       const float avgMultiplier = float(${x});
 
@@ -1750,7 +1750,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function i7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new Wv(m);return t.runWebGLProgram(f,[n],a.dtype)}var mF={kernelName:Ul,backendName:"webgl",kernelFunc:i7};function a7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Yi([n,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=o,c=N.computePool2DInfo(a.shape,i,l,1,u),p=new Gv(c);return t.runWebGLProgram(p,[n],a.dtype)}var fF={kernelName:Wl,backendName:"webgl",kernelFunc:a7};function l7(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return uc({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var dF={kernelName:qo,backendName:"webgl",kernelFunc:l7};var Uv=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,o);let i="0.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function c7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new Hv(m);return t.runWebGLProgram(f,[n],a.dtype)}var gF={kernelName:Ul,backendName:"webgl",kernelFunc:c7};function p7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Yi([n,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=o,c=N.computePool2DInfo(a.shape,i,l,1,u),p=new jv(c);return t.runWebGLProgram(p,[n],a.dtype)}var xF={kernelName:Wl,backendName:"webgl",kernelFunc:p7};function m7(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return cc({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var yF={kernelName:Yo,backendName:"webgl",kernelFunc:m7};var qv=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,o);let i="0.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -1760,7 +1760,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         float inv = scale * inversesqrt(variance + float(${a}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}};var jv=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,o);let i="vec4(0.0)";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};var Kv=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,o);let i="vec4(0.0)";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         vec4 offset = ${i};
         vec4 scale = ${l};
@@ -1773,7 +1773,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
 
         setOutput((x - mean) * inv + offset);
       }
-    `}};var u7=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=t;l==null&&(l=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=G().getBool("WEBGL_PACK_NORMALIZATION")?new jv(o.shape,n.shape,s.shape,c,p,l):new Uv(o.shape,n.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},hF={kernelName:on,backendName:"webgl",kernelFunc:u7};var Hv=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=`uniform int start[${this.rank}];`,n=c7(this.rank),s,a=e.map((i,l)=>`sourceLoc.${qv[l]} = start[${l}] + coords.${qv[l]};`);s=`
+    `}};var f7=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=t;l==null&&(l=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=U().getBool("WEBGL_PACK_NORMALIZATION")?new Kv(o.shape,n.shape,s.shape,c,p,l):new qv(o.shape,n.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},bF={kernelName:ln,backendName:"webgl",kernelFunc:f7};var Xv=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=`uniform int start[${this.rank}];`,n=d7(this.rank),s,a=e.map((i,l)=>`sourceLoc.${Yv[l]} = start[${l}] + coords.${Yv[l]};`);s=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${a.join(`
@@ -1784,7 +1784,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         ${s}
         setOutput(getSource(${n}));
       }
-    `}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,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},qv=["x","y","z","w","u","v"];function c7(r){if(r===1)return"sourceLoc";if(r<=6)return qv.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var Kv=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=Ut("coords",this.rank),n=Ut("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},Yv=["x","y","z","w","u","v"];function d7(r){if(r===1)return"sourceLoc";if(r<=6)return Yv.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var Zv=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=Ut("coords",this.rank),n=Ut("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`
       result.x = ${a};
       if (++${o[this.rank-1]} < ${e[this.rank-1]}) {
         ++${n[this.rank-1]};
@@ -1814,7 +1814,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         ${l}
         setOutput(result);
       }
-    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function p7(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.complexParentRefCount=0,a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=or.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let l=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,l+1),s}function Ua(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,l]=or.parseSliceParams(n,s,a);if(or.assertParamsValid(n,i,l),y.sizeFromShape(l)===0)return t.makeTensorInfo(l,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype==="string"){let p=t.texData.get(n.dataId),m=fR(p.values,i,l,n.shape,n.dtype);return t.makeTensorInfo(l,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=or.isSliceContinous(n.shape,i,l);if(u||!c){let p=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Kv(l):new Hv(l),m=p.getCustomSetupFunc(i);return t.runWebGLProgram(p,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),p7(n,i,l,t)}var gF={kernelName:hs,backendName:"webgl",kernelFunc:Ua};var m7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,_)=>b*_),l=N.getReshaped(n.shape,s,i),u=N.getPermuted(l.length,s.length),c=N.getReshapedPermuted(n.shape,s,i),p=N.getSliceBeginCoords(a,s.length),m=N.getSliceSize(c,a,s.length),f=[],d=ce({inputs:{x:n},backend:t,attrs:{shape:l}}),h=Lt({inputs:{x:d},backend:t,attrs:{perm:u}}),g=ce({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Ua({inputs:{x:g},backend:t,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},xF={kernelName:sa,backendName:"webgl",kernelFunc:m7};function f7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),l=t.readSync(s.dataId),u=Rx(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var yF={kernelName:jl,backendName:"webgl",kernelFunc:f7};var d7="return float(a != b);",Xv=it({opSnippet:d7,dtype:"bool"}),bF={kernelName:bi,backendName:"webgl",kernelFunc:Xv};function ja(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return jt({inputs:{x:n.complexTensorInfos.real},backend:t})}var _F={kernelName:cu,backendName:"webgl",kernelFunc:ja};var h7="return float(int(x));";function wF(r,e){let t=new uo(r.shape,h7),o=e.runWebGLProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Yv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return jt({inputs:{x:n},backend:t});let a=gt(n.shape),i=Yv({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),l=co({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),l}if(n.dtype==="complex64"){let a=ja({inputs:{input:n},backend:t}),i=Yv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=jt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32")return wF(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),l=Xv({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),l}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var kF={kernelName:Eo,backendName:"webgl",kernelFunc:Yv};var vF="return ceil(x);",g7=ve({opSnippet:vF,packedOpSnippet:vF,cpuKernelImpl:X$}),CF={kernelName:ei,backendName:"webgl",kernelFunc:g7};var Zv=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,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function h7(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=or.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let l=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,l+1),s}function Ua(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,l]=or.parseSliceParams(n,s,a);if(or.assertParamsValid(n,i,l),y.sizeFromShape(l)===0)return t.makeTensorInfo(l,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype==="string"){let p=t.texData.get(n.dataId),m=xR(p.values,i,l,n.shape,n.dtype);return t.makeTensorInfo(l,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=or.isSliceContinous(n.shape,i,l);if(u||!c){let p=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Zv(l):new Xv(l),m=p.getCustomSetupFunc(i);return t.runWebGLProgram(p,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),h7(n,i,l,t)}var _F={kernelName:ys,backendName:"webgl",kernelFunc:Ua};var g7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,_)=>b*_),l=N.getReshaped(n.shape,s,i),u=N.getPermuted(l.length,s.length),c=N.getReshapedPermuted(n.shape,s,i),p=N.getSliceBeginCoords(a,s.length),m=N.getSliceSize(c,a,s.length),f=[],d=ce({inputs:{x:n},backend:t,attrs:{shape:l}}),h=Lt({inputs:{x:d},backend:t,attrs:{perm:u}}),g=ce({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Ua({inputs:{x:g},backend:t,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},wF={kernelName:sa,backendName:"webgl",kernelFunc:g7};function x7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),l=t.readSync(s.dataId),u=zx(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var kF={kernelName:jl,backendName:"webgl",kernelFunc:x7};var y7="return float(a != b);",Jv=it({opSnippet:y7,dtype:"bool"}),vF={kernelName:bi,backendName:"webgl",kernelFunc:Jv};function ja(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return jt({inputs:{x:n.complexTensorInfos.real},backend:t})}var CF={kernelName:cu,backendName:"webgl",kernelFunc:ja};var b7="return float(int(x));";function IF(r,e){let t=new uo(r.shape,b7),o=e.runWebGLProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Qv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return jt({inputs:{x:n},backend:t});let a=gt(n.shape),i=Qv({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),l=co({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),l}if(n.dtype==="complex64"){let a=ja({inputs:{input:n},backend:t}),i=Qv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=jt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32")return IF(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),l=Jv({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),l}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var NF={kernelName:Eo,backendName:"webgl",kernelFunc:Qv};var SF="return ceil(x);",_7=ve({opSnippet:SF,packedOpSnippet:SF,cpuKernelImpl:Q$}),TF={kernelName:Zo,backendName:"webgl",kernelFunc:_7};var eC=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1827,7 +1827,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}getCustomSetupFunc(e,t){return(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};var Jv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};var tC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1841,7 +1841,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}getCustomSetupFunc(e,t){return(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};function x7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;G().getBool("WEBGL_PACK_CLIP")?i=new Jv(n.shape):i=new Zv(n.shape);let l=i.getCustomSetupFunc(s,a);return t.runWebGLProgram(i,[n],n.dtype,l)}var IF={kernelName:Ao,backendName:"webgl",kernelFunc:x7};var Qv=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};function w7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;U().getBool("WEBGL_PACK_CLIP")?i=new tC(n.shape):i=new eC(n.shape);let l=i.getCustomSetupFunc(s,a);return t.runWebGLProgram(i,[n],n.dtype,l)}var EF={kernelName:Ao,backendName:"webgl",kernelFunc:w7};var rC=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -1854,7 +1854,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function NF(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function y7(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new Qv(o.shape),a=[NF(o,n.complexTensorInfos.real),NF(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var SF={kernelName:ia,backendName:"webgl",kernelFunc:y7};var eC=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let o=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];o.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let n=t.length,s=t[t.length-1];o.push(`else setOutput(getT${n}(yR, yC-${s}));`),this.userCode=`
+    `}};function AF(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function k7(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new rC(o.shape),a=[AF(o,n.complexTensorInfos.real),AF(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var DF={kernelName:ia,backendName:"webgl",kernelFunc:k7};var oC=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let o=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];o.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let n=t.length,s=t[t.length-1];o.push(`else setOutput(getT${n}(yR, yC-${s}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -1863,18 +1863,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         ${o.join(`
         `)}
       }
-    `}};var tC=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let o=this.outputShape,n=o.length,s=Le(n),a=Ut("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((h,g)=>`T${g}`);let l=new Array(e.length-1);l[0]=e[0][t];for(let h=1;h<l.length;h++)l[h]=l[h-1]+e[h][t];let u=i[t],c=i.slice(-2),p=i.join(),m=`if (${u} < ${l[0]}) {
+    `}};var nC=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let o=this.outputShape,n=o.length,s=Le(n),a=Ut("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((h,g)=>`T${g}`);let l=new Array(e.length-1);l[0]=e[0][t];for(let h=1;h<l.length;h++)l[h]=l[h-1]+e[h][t];let u=i[t],c=i.slice(-2),p=i.join(),m=`if (${u} < ${l[0]}) {
         return getChannel(
             getT0(${p}), vec2(${c.join()}));
         }`;for(let h=1;h<l.length;h++){let g=l[h-1];m+=`
         if (${u} < ${l[h]}  && ${u} >= ${l[h-1]}) {
           return getChannel(
-            getT${h}(${Gx(i,u,g)}),
-            vec2(${Gx(c,u,g)}));
+            getT${h}(${Kx(i,u,g)}),
+            vec2(${Kx(c,u,g)}));
         }`}let f=l.length,d=l[l.length-1];m+=`
         return getChannel(
-          getT${f}(${Gx(i,u,d)}),
-          vec2(${Gx(c,u,d)}));`,this.userCode=`
+          getT${f}(${Kx(i,u,d)}),
+          vec2(${Kx(c,u,d)}));`,this.userCode=`
       float getValue(${i.map(h=>"int "+h)}) {
         ${m}
       }
@@ -1900,7 +1900,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         }
         setOutput(result);
       }
-    `}};function Gx(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function pc(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return jt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var TF={kernelName:ou,backendName:"webgl",kernelFunc:pc};function mc(r,e,t){let o=r[0].dtype;if(o==="complex64"){let u=r.map(d=>ja({inputs:{input:d},backend:t})),c=r.map(d=>pc({inputs:{input:d},backend:t})),p=mc(u,e,t),m=mc(c,e,t),f=co({inputs:{real:p,imag:m},backend:t});return u.forEach(d=>t.disposeIntermediateTensorInfo(d)),c.forEach(d=>t.disposeIntermediateTensorInfo(d)),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),f}if(o==="string"){let{tensors2D:u,outShape:c}=EF(r,e,t),p=u.map(g=>({vals:t.readSync(g.dataId),shape:g.shape})),m=u[0].shape[0]===1,f=Y$(p,c,o,m),d=N.computeOutShape(r.map(g=>g.shape),e),h=t.makeTensorInfo(d,o,f);return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}if(r.length>G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(r.length/2),c=mc(r.slice(0,u),e,t),p=mc(r.slice(u),e,t),m=mc([c,p],e,t);return t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),m}if(G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let u=new tC(r.map(c=>c.shape),e);return t.runWebGLProgram(u,r,o)}let{tensors2D:n,outShape:s}=EF(r,e,t),a=new eC(n.map(u=>u.shape)),i=t.runWebGLProgram(a,n,o);n.forEach(u=>t.disposeIntermediateTensorInfo(u));let l=ce({inputs:{x:i},attrs:{shape:s},backend:t});return t.disposeIntermediateTensorInfo(i),l}function EF(r,e,t){let o=N.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>ce({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function rC(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=N.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(u=>y.sizeFromShape(u.shape)>0);if(i.length===1)return jt({inputs:{x:i[0]},backend:t});let l=i.map(u=>u.shape);return N.assertParamsConsistent(l,s),mc(i,s,t)}var AF={kernelName:is,backendName:"webgl",kernelFunc:rC};var lh=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,l=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,_=g?3:1,w="",v="";o&&(n?w=`float activation(float a) {
+    `}};function Kx(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function mc(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return jt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var $F={kernelName:ou,backendName:"webgl",kernelFunc:mc};function fc(r,e,t){let o=r[0].dtype;if(o==="complex64"){let u=r.map(d=>ja({inputs:{input:d},backend:t})),c=r.map(d=>mc({inputs:{input:d},backend:t})),p=fc(u,e,t),m=fc(c,e,t),f=co({inputs:{real:p,imag:m},backend:t});return u.forEach(d=>t.disposeIntermediateTensorInfo(d)),c.forEach(d=>t.disposeIntermediateTensorInfo(d)),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),f}if(o==="string"){let{tensors2D:u,outShape:c}=RF(r,e,t),p=u.map(g=>({vals:t.readSync(g.dataId),shape:g.shape})),m=u[0].shape[0]===1,f=eR(p,c,o,m),d=N.computeOutShape(r.map(g=>g.shape),e),h=t.makeTensorInfo(d,o,f);return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}if(r.length>U().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(r.length/2),c=fc(r.slice(0,u),e,t),p=fc(r.slice(u),e,t),m=fc([c,p],e,t);return t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),m}if(U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let u=new nC(r.map(c=>c.shape),e);return t.runWebGLProgram(u,r,o)}let{tensors2D:n,outShape:s}=RF(r,e,t),a=new oC(n.map(u=>u.shape)),i=t.runWebGLProgram(a,n,o);n.forEach(u=>t.disposeIntermediateTensorInfo(u));let l=ce({inputs:{x:i},attrs:{shape:s},backend:t});return t.disposeIntermediateTensorInfo(i),l}function RF(r,e,t){let o=N.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>ce({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function sC(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=N.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(u=>y.sizeFromShape(u.shape)>0);if(i.length===1)return jt({inputs:{x:i[0]},backend:t});let l=i.map(u=>u.shape);return N.assertParamsConsistent(l,s),fc(i,s,t)}var FF={kernelName:cs,backendName:"webgl",kernelFunc:sC};var ph=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,l=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,_=g?3:1,w="",v="";o&&(n?w=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${o}
         }`:s?w=`float activation(float a) {
@@ -2034,7 +2034,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         ${v}
         setOutput(result);
       }
-    `}},oC=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`
+    `}},iC=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`
       const ivec3 strides = ivec3(${s}, ${a}, ${i});
       const ivec3 pads = ivec3(${t}, ${o}, ${n});
 
@@ -2122,7 +2122,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};var nC=class{constructor(e,t,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:n,inChannels:s,strideWidth:a,strideHeight:i,padInfo:l,outWidth:u,dilationWidth:c,dilationHeight:p,dataFormat:m}=o,{left:f,top:d}=l,h=s*n,g=Mt(),x=m==="channelsLast",b=x?0:1,_=x?1:2,w="";for(let v=0;v<=1;v++)for(let $=0;$<=1;$++)w+=`
+    `}};var aC=class{constructor(e,t,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:n,inChannels:s,strideWidth:a,strideHeight:i,padInfo:l,outWidth:u,dilationWidth:c,dilationHeight:p,dataFormat:m}=o,{left:f,top:d}=l,h=s*n,g=Mt(),x=m==="channelsLast",b=x?0:1,_=x?1:2,w="";for(let v=0;v<=1;v++)for(let $=0;$<=1;$++)w+=`
           blockIndex = rc.y + ${$};
           pos = rc.x + ${v};
 
@@ -2166,7 +2166,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
 
         ${g.output} = result;
       }
-    `}};function Wx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,f=t.dataFormat==="channelsLast",d=!1,h=!1,g,x=[],b=(p===1||m===1)&&c>Mv,_=l[2]%2!=0&&!!u.isPacked;if(b||!G().getBool("WEBGL_LAZILY_UNPACK")||!G().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!_){let w=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],v=ce({inputs:{x:r},backend:o,attrs:{shape:[1,w,t.inChannels]}}),$=ce({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),A=uc({a:v,b:$,transposeA:d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=ce({inputs:{x:A},backend:o,attrs:{shape:t.outShape}}),x.push(v),x.push($),x.push(A)}else{let w=f?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),v={dataId:r.dataId,shape:[1,w,t.inChannels],dtype:r.dtype},$=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(ac(u.shape,v.shape),()=>`packed reshape ${u.shape} to ${v.shape} isn't free`);let A=ce({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(A);let R=uc({a:v,b:A,backend:o,transposeA:d,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),M=o.texData.get(R.dataId);y.assert(M.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=$,M.shape=t.outShape,g=jt({inputs:{x:R},backend:o}),g.shape=t.outShape,x.push(R)}for(let w of x)o.disposeIntermediateTensorInfo(w);return g}function Ux({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=t,d=f==="channelsLast",h=l*u*c,g=m*p,x=[h,g],b=!0,_=!1,w=[],v=ce({inputs:{x:r},backend:o,attrs:{shape:r.shape.slice(1)}}),$=ce({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});w.push(v),w.push($);let A=new nC(x,v.shape,t),R=o.runWebGLProgram(A,[v],"float32"),M=ce({inputs:{x:R},backend:o,attrs:{shape:[1,x[0],x[1]]}});w.push(R),w.push(M);let z=n!=null,W=s!=null,U=i==="leakyrelu",q=i?El(i,!0):null,Z=new ih(M.shape,$.shape,[1,g,t.outChannels],b,_,z,q,W,U),X=[M,$];if(n&&X.push(n),W&&X.push(s),U){let re=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));X.push(re),w.push(re)}let Y=o.runWebGLProgram(Z,X,"float32"),te=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],K=ce({inputs:{x:Y},backend:o,attrs:{shape:te}});w.push(Y);for(let re of w)o.disposeIntermediateTensorInfo(re);return K}function b7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=o,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=Wx({x:n,filter:s,convInfo:m,backend:t});else if(G().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)f=Ux({x:n,filter:s,convInfo:m,backend:t});else{let h=new lh(m);f=t.runWebGLProgram(h,[n,s],"float32")}let d=ce({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var DF={kernelName:Ko,backendName:"webgl",kernelFunc:b7};var sC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function Xx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,f=t.dataFormat==="channelsLast",d=!1,h=!1,g,x=[],b=(p===1||m===1)&&c>Bv,_=l[2]%2!=0&&!!u.isPacked;if(b||!U().getBool("WEBGL_LAZILY_UNPACK")||!U().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!_){let w=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],v=ce({inputs:{x:r},backend:o,attrs:{shape:[1,w,t.inChannels]}}),$=ce({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),A=cc({a:v,b:$,transposeA:d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=ce({inputs:{x:A},backend:o,attrs:{shape:t.outShape}}),x.push(v),x.push($),x.push(A)}else{let w=f?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),v={dataId:r.dataId,shape:[1,w,t.inChannels],dtype:r.dtype},$=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(lc(u.shape,v.shape),()=>`packed reshape ${u.shape} to ${v.shape} isn't free`);let A=ce({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(A);let R=cc({a:v,b:A,backend:o,transposeA:d,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),M=o.texData.get(R.dataId);y.assert(M.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=$,M.shape=t.outShape,g=jt({inputs:{x:R},backend:o}),g.shape=t.outShape,x.push(R)}for(let w of x)o.disposeIntermediateTensorInfo(w);return g}function Yx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=t,d=f==="channelsLast",h=l*u*c,g=m*p,x=[h,g],b=!0,_=!1,w=[],v=ce({inputs:{x:r},backend:o,attrs:{shape:r.shape.slice(1)}}),$=ce({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});w.push(v),w.push($);let A=new aC(x,v.shape,t),R=o.runWebGLProgram(A,[v],"float32"),M=ce({inputs:{x:R},backend:o,attrs:{shape:[1,x[0],x[1]]}});w.push(R),w.push(M);let V=n!=null,G=s!=null,W=i==="leakyrelu",q=i?El(i,!0):null,Z=new uh(M.shape,$.shape,[1,g,t.outChannels],b,_,V,q,G,W),X=[M,$];if(n&&X.push(n),G&&X.push(s),W){let re=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));X.push(re),w.push(re)}let Y=o.runWebGLProgram(Z,X,"float32"),te=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],K=ce({inputs:{x:Y},backend:o,attrs:{shape:te}});w.push(Y);for(let re of w)o.disposeIntermediateTensorInfo(re);return K}function v7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=o,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=Xx({x:n,filter:s,convInfo:m,backend:t});else if(U().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)f=Yx({x:n,filter:s,convInfo:m,backend:t});else{let h=new ph(m);f=t.runWebGLProgram(h,[n,s],"float32")}let d=ce({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var OF={kernelName:Jo,backendName:"webgl",kernelFunc:v7};var lC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2208,7 +2208,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},iC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,l=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,p=a?3:1;this.userCode=`
+    `}},uC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,l=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,p=a?3:1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${l});
 
       void main() {
@@ -2261,7 +2261,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},aC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`
+    `}},cC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -2303,7 +2303,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},lC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`
+    `}},pC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`
       const ivec3 pads = ivec3(${l}, ${u}, ${c});
 
       void main() {
@@ -2360,12 +2360,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function _7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=o,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,c,a,1,i,u,!1,p),f=new sC(m);return t.runWebGLProgram(f,[n,s],"float32")}var $F={kernelName:ql,backendName:"webgl",kernelFunc:_7};function w7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=o,p=N.convertConv2DDataFormat(u),m=N.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new iC(m);return t.runWebGLProgram(f,[n,s],"float32")}var RF={kernelName:Xo,backendName:"webgl",kernelFunc:w7};function k7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=N.computeConv3DInfo(n.shape,s.shape,a,l,i),c=new oC(u);return t.runWebGLProgram(c,[n,s],"float32")}var FF={kernelName:aa,backendName:"webgl",kernelFunc:k7};function v7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:l}=o,u=N.computeConv3DInfo(n.shape,l,a,1,i),c=new aC(u);return t.runWebGLProgram(c,[n,s],"float32")}var OF={kernelName:Kl,backendName:"webgl",kernelFunc:v7};function C7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:l}=o,u=N.computeConv3DInfo(l,s.shape,i,1,a),c=new lC(u);return t.runWebGLProgram(c,[n,s],"float32")}var PF={kernelName:Xl,backendName:"webgl",kernelFunc:C7};var I7=Mx+`
+    `}};function C7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=o,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,c,a,1,i,u,!1,p),f=new lC(m);return t.runWebGLProgram(f,[n,s],"float32")}var PF={kernelName:ql,backendName:"webgl",kernelFunc:C7};function I7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=o,p=N.convertConv2DDataFormat(u),m=N.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new uC(m);return t.runWebGLProgram(f,[n,s],"float32")}var MF={kernelName:Qo,backendName:"webgl",kernelFunc:I7};function N7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=N.computeConv3DInfo(n.shape,s.shape,a,l,i),c=new iC(u);return t.runWebGLProgram(c,[n,s],"float32")}var LF={kernelName:aa,backendName:"webgl",kernelFunc:N7};function S7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:l}=o,u=N.computeConv3DInfo(n.shape,l,a,1,i),c=new cC(u);return t.runWebGLProgram(c,[n,s],"float32")}var zF={kernelName:Kl,backendName:"webgl",kernelFunc:S7};function T7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:l}=o,u=N.computeConv3DInfo(l,s.shape,i,1,a),c=new pC(u);return t.runWebGLProgram(c,[n,s],"float32")}var BF={kernelName:Xl,backendName:"webgl",kernelFunc:T7};var E7=Wx+`
   return cos(x);
-`,N7=ve({opSnippet:I7}),MF={kernelName:Yo,backendName:"webgl",kernelFunc:N7};var S7=`
+`,A7=ve({opSnippet:E7}),VF={kernelName:en,backendName:"webgl",kernelFunc:A7};var D7=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,T7=ve({opSnippet:S7}),LF={kernelName:ti,backendName:"webgl",kernelFunc:T7};var uC=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=o;this.outputShape=[c,p,m,u];let f=n==="bilinear"?1:0,[d,h]=[`${i-1}.0`,`${l-1}.0`],[g,x,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[_,w,v]=m>1?[`${(l-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=`
+`,$7=ve({opSnippet:D7}),GF={kernelName:ti,backendName:"webgl",kernelFunc:$7};var mC=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=o;this.outputShape=[c,p,m,u];let f=n==="bilinear"?1:0,[d,h]=[`${i-1}.0`,`${l-1}.0`],[g,x,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[_,w,v]=m>1?[`${(l-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=`
       const float height_ratio = float(${g});
       const float width_ratio = float(${_});
       void main() {
@@ -2426,21 +2426,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
           setOutput(newValue);
         }
       }
-    `}};var E7=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=o,c=new uC(n.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[n,s,a],"float32")},zF={kernelName:ri,backendName:"webgl",kernelFunc:E7};var jx=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=e;let n=e.length,s=t?"0.0":`getX(${BF(n,"coords")})`,a=e[e.length-1],i="",l="";t?(i=o?`end != ${a-1}`:"end != 0",l=o?"end + 1":"end - 1"):(i=o?`end + pow2 < ${a}`:"end >= pow2",l=o?"end + pow2":"end - pow2"),this.userCode=`
+    `}};var R7=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=o,c=new mC(n.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[n,s,a],"float32")},WF={kernelName:ri,backendName:"webgl",kernelFunc:R7};var Zx=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=e;let n=e.length,s=t?"0.0":`getX(${UF(n,"coords")})`,a=e[e.length-1],i="",l="";t?(i=o?`end != ${a-1}`:"end != 0",l=o?"end + 1":"end - 1"):(i=o?`end + pow2 < ${a}`:"end >= pow2",l=o?"end + pow2":"end - pow2"),this.userCode=`
       uniform float index;
       void main() {
         ${Le(n)} coords = getOutputCoords();
-        int end = ${VF(n,"coords")};
+        int end = ${jF(n,"coords")};
         float val = ${s};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${l};
-          ${VF(n,"coords")} = idx;
-          val += getX(${BF(n,"coords")});
+          ${jF(n,"coords")} = idx;
+          val += getX(${UF(n,"coords")});
         }
         setOutput(val);
       }
-    `}getCustomSetupFunc(e){return(t,o)=>{this.index==null&&(this.index=t.getUniformLocation(o,"index")),t.gl.uniform1f(this.index,e)}}};function BF(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function VF(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function A7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o,l=n.shape.length,u=N.getAxesPermutation([s],l),c=n;u!=null&&(c=Lt({inputs:{x:n},backend:t,attrs:{perm:u}}));let p=N.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${n.shape.length-1} but got axis=${s}`);let m=n.shape[p],f=jt({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new jx(c.shape,!1,i),g=h.getCustomSetupFunc(d),x=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(x)}if(a){let d=new jx(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=N.getUndoAxesPermutation(u),h=Lt({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var GF={kernelName:Zo,backendName:"webgl",kernelFunc:A7};function D7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let l=t.readSync(n.dataId),u=t.readSync(s.dataId),c=Rx(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let l=t.bufferSync(n),u=t.bufferSync(s),c=K$(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var WF={kernelName:Yl,backendName:"webgl",kernelFunc:D7};var cC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=`
+    `}getCustomSetupFunc(e){return(t,o)=>{this.index==null&&(this.index=t.getUniformLocation(o,"index")),t.gl.uniform1f(this.index,e)}}};function UF(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function jF(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function F7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o,l=n.shape.length,u=N.getAxesPermutation([s],l),c=n;u!=null&&(c=Lt({inputs:{x:n},backend:t,attrs:{perm:u}}));let p=N.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${n.shape.length-1} but got axis=${s}`);let m=c.shape[p],f=jt({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new Zx(c.shape,!1,i),g=h.getCustomSetupFunc(d),x=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(x)}if(a){let d=new Zx(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=N.getUndoAxesPermutation(u),h=Lt({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var HF={kernelName:tn,backendName:"webgl",kernelFunc:F7};function O7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let l=t.readSync(n.dataId),u=t.readSync(s.dataId),c=zx(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let l=t.bufferSync(n),u=t.bufferSync(s),c=J$(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var qF={kernelName:Yl,backendName:"webgl",kernelFunc:O7};var fC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2459,7 +2459,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={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 $7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o;y.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=n.shape[0],l=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=new cC(d,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var UF={kernelName:oi,backendName:"webgl",kernelFunc:$7};var uh=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=e.outChannels/e.inChannels,x="",b="";o&&(n?x=`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 P7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o;y.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=n.shape[0],l=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=new fC(d,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var KF={kernelName:oi,backendName:"webgl",kernelFunc:P7};var mh=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=e.outChannels/e.inChannels,x="",b="";o&&(n?x=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${o}
         }`:s?x=`float activation(float a) {
@@ -2515,7 +2515,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         ${b}
         setOutput(result);
       }
-    `}};var ch=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=h,x="int xR; int xC; int xCOffset;";for(let v=0;v<d;v++)for(let $=0;$<h;$++)x+=`
+    `}};var fh=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=h,x="int xR; int xC; int xCOffset;";for(let v=0;v<d;v++)for(let $=0;$<h;$++)x+=`
           vec4 xTexelR${v}C${$*2} = vec4(0.);
           vec4 wR${v}C${$} = vec4(0.);
           vec4 xR${v}C${$} = vec4(0.);`;for(let v=0;v<d;v++)for(let $=0;$<g;$++){let A=$*2;if(x+=`
@@ -2668,7 +2668,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         ${_}
         setOutput(result);
       }
-    `}};function R7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l,dimRoundingMode:u}=o,c=l;c==null&&(c=[1,1]),y.assert(N.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let p=N.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;return G().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?m=new ch(p):m=new uh(p),t.runWebGLProgram(m,[n,s],"float32")}var jF={kernelName:Jo,backendName:"webgl",kernelFunc:R7};var pC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
+    `}};function M7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l,dimRoundingMode:u}=o,c=l;c==null&&(c=[1,1]),y.assert(N.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let p=N.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;return U().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?m=new fh(p):m=new mh(p),t.runWebGLProgram(m,[n,s],"float32")}var XF={kernelName:rn,backendName:"webgl",kernelFunc:M7};var dC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2703,7 +2703,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},mC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,l=e.outChannels/e.inChannels;this.userCode=`
+    `}},hC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,l=e.outChannels/e.inChannels;this.userCode=`
       const ivec2 pads = ivec2(${a}, ${i});
 
       void main() {
@@ -2748,13 +2748,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function F7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=o,p=N.computeConv2DInfo(n.shape,c,a,i,l,u,!0),m=new pC(p);return t.runWebGLProgram(m,[n,s],"float32")}var HF={kernelName:Zl,backendName:"webgl",kernelFunc:F7};function O7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=o,p=N.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new mC(p);return t.runWebGLProgram(m,[n,s],"float32")}var qF={kernelName:Jl,backendName:"webgl",kernelFunc:O7};var fC=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function L7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=o,p=N.computeConv2DInfo(n.shape,c,a,i,l,u,!0),m=new dC(p);return t.runWebGLProgram(m,[n,s],"float32")}var YF={kernelName:Zl,backendName:"webgl",kernelFunc:L7};function z7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=o,p=N.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new hC(p);return t.runWebGLProgram(m,[n,s],"float32")}var ZF={kernelName:Jl,backendName:"webgl",kernelFunc:z7};var gC=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 P7(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=ce({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new fC(s),l=t.runWebGLProgram(i,[a],a.dtype),u=ce({inputs:{x:l},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var KF={kernelName:Ql,backendName:"webgl",kernelFunc:P7};var dC=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=n;this.userCode=`
+    `}};function B7(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=ce({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new gC(s),l=t.runWebGLProgram(i,[a],a.dtype),u=ce({inputs:{x:l},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var JF={kernelName:Ql,backendName:"webgl",kernelFunc:B7};var xC=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=n;this.userCode=`
       const ivec2 strides = ivec2(${s}, ${a});
       const ivec2 pads = ivec2(${p}, ${m});
       const float neg_infinity = -3.4e38;
@@ -2792,7 +2792,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         float result = curVal;
         setOutput(result);
       }
-    `}};function M7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=N.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",l),c,p=new dC(u);c=t.runWebGLProgram(p,[n,s],"float32");let m=ce({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var XF={kernelName:la,backendName:"webgl",kernelFunc:M7};var L7="return (x >= 0.0) ? x : (exp(x) - 1.0);",z7=`
+    `}};function V7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=N.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",l),c,p=new xC(u);c=t.runWebGLProgram(p,[n,s],"float32");let m=ce({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var QF={kernelName:la,backendName:"webgl",kernelFunc:V7};var G7="return (x >= 0.0) ? x : (exp(x) - 1.0);",W7=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -2801,12 +2801,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,B7=ve({opSnippet:L7,packedOpSnippet:z7}),YF={kernelName:ni,backendName:"webgl",kernelFunc:B7};var V7="return (b >= 1.0) ? a : a * (b + 1.0);",G7=`
+`,U7=ve({opSnippet:G7,packedOpSnippet:W7}),eO={kernelName:ni,backendName:"webgl",kernelFunc:U7};var j7="return (b >= 1.0) ? a : a * (b + 1.0);",H7=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,W7=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Vs(G7,o.shape,n.shape):new Qn(V7,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},ZF={kernelName:eu,backendName:"webgl",kernelFunc:W7};var U7=`
+`,q7=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ws(H7,o.shape,n.shape):new os(j7,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},tO={kernelName:eu,backendName:"webgl",kernelFunc:q7};var K7=`
   return vec4(equal(a, b));
-`,j7="return float(a == b);",H7=it({opSnippet:j7,packedOpSnippet:U7,dtype:"bool"}),JF={kernelName:ii,backendName:"webgl",kernelFunc:H7};var q7=`
+`,X7="return float(a == b);",Y7=it({opSnippet:X7,packedOpSnippet:K7,dtype:"bool"}),rO={kernelName:ii,backendName:"webgl",kernelFunc:Y7};var Z7=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -2821,7 +2821,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={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));
-`,K7=ve({opSnippet:q7}),QF={kernelName:si,backendName:"webgl",kernelFunc:K7};var eO="return exp(x);",hC=ve({opSnippet:eO,packedOpSnippet:eO,cpuKernelImpl:Z$}),tO={kernelName:en,backendName:"webgl",kernelFunc:hC};function Hx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+n+1),i.splice(l,0,1),ce({inputs:{x:s},backend:o,attrs:{shape:i}})}var rO={kernelName:as,backendName:"webgl",kernelFunc:Hx};var oO="return exp(x) - 1.0;",X7=ve({opSnippet:oO,packedOpSnippet:oO,cpuKernelImpl:J$}),nO={kernelName:ai,backendName:"webgl",kernelFunc:X7};var qx=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.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 ${e}.`);this.userCode=`
+`,J7=ve({opSnippet:Z7}),oO={kernelName:si,backendName:"webgl",kernelFunc:J7};var nO="return exp(x);",yC=ve({opSnippet:nO,packedOpSnippet:nO,cpuKernelImpl:tR}),sO={kernelName:nn,backendName:"webgl",kernelFunc:yC};function Jx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+n+1),i.splice(l,0,1),ce({inputs:{x:s},backend:o,attrs:{shape:i}})}var iO={kernelName:ps,backendName:"webgl",kernelFunc:Jx};var aO="return exp(x) - 1.0;",Q7=ve({opSnippet:aO,packedOpSnippet:aO,cpuKernelImpl:rR}),lO={kernelName:ai,backendName:"webgl",kernelFunc:Q7};var Qx=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.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 = ${s};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -2854,13 +2854,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function Kx(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=ce({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),l=i.shape,u=new qx("real",l,e),c=new qx("imag",l,e),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:l},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=co({inputs:{real:m,imag:f},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f);let h=ce({inputs:{x:d},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(d),h}function Y7(r){let{inputs:e,backend:t}=r,{input:o}=e;return Kx(o,!1,t)}var sO={kernelName:tu,backendName:"webgl",kernelFunc:Y7};var gC=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function ey(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=ce({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),l=i.shape,u=new Qx("real",l,e),c=new Qx("imag",l,e),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:l},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=co({inputs:{real:m,imag:f},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f);let h=ce({inputs:{x:d},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(d),h}function eZ(r){let{inputs:e,backend:t}=r,{input:o}=e;return ey(o,!1,t)}var uO={kernelName:tu,backendName:"webgl",kernelFunc:eZ};var bC=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,o)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(o,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function ph(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new gC(o,n),i=a.getCustomSetupFunc(n);return e.runWebGLProgram(a,[],s,i)}}var iO={kernelName:ua,backendName:"webgl",kernelFunc:ph};var xC=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e){return(t,o)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(o,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function dh(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new bC(o,n),i=a.getCustomSetupFunc(n);return e.runWebGLProgram(a,[],s,i)}}var cO={kernelName:ua,backendName:"webgl",kernelFunc:dh};var _C=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];
@@ -2874,7 +2874,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
           }
           setOutput(outputValue);
         }
-    `}};var aO={kernelName:li,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new xC(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var lO="return floor(x);",Z7=ve({opSnippet:lO,packedOpSnippet:lO,cpuKernelImpl:Q$}),uO={kernelName:tn,backendName:"webgl",kernelFunc:Z7};var J7=`
+    `}};var pO={kernelName:li,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new _C(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var mO="return floor(x);",tZ=ve({opSnippet:mO,packedOpSnippet:mO,cpuKernelImpl:oR}),fO={kernelName:sn,backendName:"webgl",kernelFunc:tZ};var rZ=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -2884,7 +2884,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
   } else {
     return NAN;
   }
-`,Q7=`
+`,oZ=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -2905,7 +2905,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,eZ=it({opSnippet:J7,packedOpSnippet:Q7,dtype:"int32"}),cO={kernelName:rn,backendName:"webgl",kernelFunc:eZ};var yC=class{constructor(e){this.variableNames=["A"];let t=Mt(),[o,n]=e;this.outputShape=e,this.userCode=`
+`,nZ=it({opSnippet:rZ,packedOpSnippet:oZ,dtype:"int32"}),dO={kernelName:an,backendName:"webgl",kernelFunc:nZ};var wC=class{constructor(e){this.variableNames=["A"];let t=Mt(),[o,n]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2927,7 +2927,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}};var bC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Mt(),[o,n]=e;this.outputShape=e,this.userCode=`
+    `}};var kC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Mt(),[o,n]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2961,7 +2961,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
 
         ${t.output} = result;
       }
-    `}};var pO={kernelName:Fc,backendName:"webgl",kernelFunc:tZ},jp;function tZ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&n instanceof ImageBitmap,[u,c]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],p=[c,u],m=[c,u,s];(i||a||l)&&(jp==null&&(jp=document.createElement("canvas").getContext("2d")),jp.canvas.width=u,jp.canvas.height=c,jp.drawImage(n,0,0,u,c),n=jp.canvas);let f=t.makeTensorInfo(p,"int32");t.texData.get(f.dataId).usage=Er.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(f.dataId),n);let d=G().getBool("WEBGL_PACK")?new bC(m):new yC(m),h=t.runWebGLProgram(d,[f],"int32");return t.disposeData(f.dataId),h}function rZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=o,h=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(n.shape,s.shape,l,p,u,m,!1,h),x,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))x=Wx({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(G().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)x=Ux({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let w=a!=null,v=i!=null,$=f==="leakyrelu",A=f?El(f,!1):null,R=new lh(g,w,A,v,$),M=[n,s];if(a&&M.push(a),i&&M.push(i),$){let z=t.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));M.push(z),b.push(z)}x=t.runWebGLProgram(R,M,"float32")}let _=ce({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(w=>t.disposeIntermediateTensorInfo(w)),_}var mO={kernelName:_s,backendName:"webgl",kernelFunc:rZ};function oZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=o,d=[],h=c;h==null&&(h=[1,1]),y.assert(N.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let g=N.computeConv2DInfo(n.shape,s.shape,l,h,u,p,!0),x=G().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=m?El(m,x):null,_=[n,s],w=a!=null,v=i!=null,$=m==="leakyrelu";if(w&&_.push(a),v&&_.push(i),$){let M=t.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));_.push(M),d.push(M)}let A;x?A=new ch(g,w,b,v,$):A=new uh(g,w,b,v,$);let R=t.runWebGLProgram(A,_,"float32");return d.forEach(M=>t.disposeIntermediateTensorInfo(M)),R}var fO={kernelName:ws,backendName:"webgl",kernelFunc:oZ};var _C=class{constructor(e,t,o){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=o;let n=Le(t.length),s=Le(o.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}};var hO={kernelName:Oc,backendName:"webgl",kernelFunc:sZ},Xp;function sZ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&n instanceof ImageBitmap,[u,c]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],p=[c,u],m=[c,u,s];(i||a||l)&&(Xp==null&&(Xp=document.createElement("canvas").getContext("2d")),Xp.canvas.width=u,Xp.canvas.height=c,Xp.drawImage(n,0,0,u,c),n=Xp.canvas);let f=t.makeTensorInfo(p,"int32");t.texData.get(f.dataId).usage=Er.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(f.dataId),n);let d=U().getBool("WEBGL_PACK")?new kC(m):new wC(m),h=t.runWebGLProgram(d,[f],"int32");return t.disposeData(f.dataId),h}function iZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=o,h=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(n.shape,s.shape,l,p,u,m,!1,h),x,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))x=Xx({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(U().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)x=Yx({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let w=a!=null,v=i!=null,$=f==="leakyrelu",A=f?El(f,!1):null,R=new ph(g,w,A,v,$),M=[n,s];if(a&&M.push(a),i&&M.push(i),$){let V=t.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));M.push(V),b.push(V)}x=t.runWebGLProgram(R,M,"float32")}let _=ce({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(w=>t.disposeIntermediateTensorInfo(w)),_}var gO={kernelName:vs,backendName:"webgl",kernelFunc:iZ};function aZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=o,d=[],h=c;h==null&&(h=[1,1]),y.assert(N.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let g=N.computeConv2DInfo(n.shape,s.shape,l,h,u,p,!0),x=U().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=m?El(m,x):null,_=[n,s],w=a!=null,v=i!=null,$=m==="leakyrelu";if(w&&_.push(a),v&&_.push(i),$){let M=t.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));_.push(M),d.push(M)}let A;x?A=new fh(g,w,b,v,$):A=new mh(g,w,b,v,$);let R=t.runWebGLProgram(A,_,"float32");return d.forEach(M=>t.disposeIntermediateTensorInfo(M)),R}var xO={kernelName:Cs,backendName:"webgl",kernelFunc:aZ};var vC=class{constructor(e,t,o){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=o;let n=Le(t.length),s=Le(o.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${n} strides = ${n}(${this.strides});
          void main() {
           ${s} coords = getOutputCoords();
@@ -2972,21 +2972,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}};function nZ(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],[i,l,u,c]=N.prepareAndValidate(o,n),p=ce({inputs:{x:n},backend:t,attrs:{shape:[l,a]}}),m=ce({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/u,u]}}),f=new _C(a,c,[l,u]),d=t.runWebGLProgram(f,[m,p],m.dtype),h=ce({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),h}var dO={kernelName:ui,backendName:"webgl",kernelFunc:nZ};var wC=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let o=Le(this.rank),n=sZ(e,2);this.userCode=`
+      `}};function lZ(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],[i,l,u,c]=N.prepareAndValidate(o,n),p=ce({inputs:{x:n},backend:t,attrs:{shape:[l,a]}}),m=ce({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/u,u]}}),f=new vC(a,c,[l,u]),d=t.runWebGLProgram(f,[m,p],m.dtype),h=ce({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),h}var yO={kernelName:ui,backendName:"webgl",kernelFunc:lZ};var CC=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let o=Le(this.rank),n=uZ(e,2);this.userCode=`
       void main() {
         ${o} resRC = getOutputCoords();
         setOutput(getA(${n}));
       }
-    `}};function sZ(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n<r.length;n++)n===2?o.push("int(getIndices(resRC.x, resRC.z))"):o.push(`${t[n]}`);return o.join()}function iZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,l=y.parseAxisParam(a,n.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(n,s,l,i),c=y.sizeFromShape(s.shape),p=[],m=ce({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),f=ce({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(m),p.push(f);let d=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype==="string"){let b=t.bufferSync(f),_=t.bufferSync(m),w=eR(_,b,d);return p.forEach(v=>t.disposeIntermediateTensorInfo(v)),t.makeTensorInfo(u.outputShape,w.dtype,w.values)}let h=new wC(m.shape,d),g=t.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=ce({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return p.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var hO={kernelName:ls,backendName:"webgl",kernelFunc:iZ};var aZ="return float(a > b);",lZ=`
+    `}};function uZ(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n<r.length;n++)n===2?o.push("int(getIndices(resRC.x, resRC.z))"):o.push(`${t[n]}`);return o.join()}function cZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,l=y.parseAxisParam(a,n.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(n,s,l,i),c=y.sizeFromShape(s.shape),p=[],m=ce({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),f=ce({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(m),p.push(f);let d=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype==="string"){let b=t.bufferSync(f),_=t.bufferSync(m),w=nR(_,b,d);return p.forEach(v=>t.disposeIntermediateTensorInfo(v)),t.makeTensorInfo(u.outputShape,w.dtype,w.values)}let h=new CC(m.shape,d),g=t.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=ce({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return p.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var bO={kernelName:ms,backendName:"webgl",kernelFunc:cZ};var pZ="return float(a > b);",mZ=`
   return vec4(greaterThan(a, b));
-`,uZ=it({opSnippet:aZ,packedOpSnippet:lZ,cpuKernelImpl:tR,dtype:"bool"}),gO={kernelName:ci,backendName:"webgl",kernelFunc:uZ};var cZ="return float(a >= b);",pZ=`
+`,fZ=it({opSnippet:pZ,packedOpSnippet:mZ,cpuKernelImpl:sR,dtype:"bool"}),_O={kernelName:ci,backendName:"webgl",kernelFunc:fZ};var dZ="return float(a >= b);",hZ=`
   return vec4(greaterThanEqual(a, b));
-`,mZ=it({opSnippet:cZ,packedOpSnippet:pZ,dtype:"bool"}),xO={kernelName:nn,backendName:"webgl",kernelFunc:mZ};function fZ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Kx(o,!0,t)}var yO={kernelName:ru,backendName:"webgl",kernelFunc:fZ};var dZ="return float(!isnan(x) && !isinf(x));",hZ=ve({opSnippet:dZ,dtype:"bool"}),bO={kernelName:pi,backendName:"webgl",kernelFunc:hZ};var gZ="return float(isinf(x));",xZ=ve({opSnippet:gZ,dtype:"bool"}),_O={kernelName:mi,backendName:"webgl",kernelFunc:xZ};var yZ="return float(isnan(x));",bZ=ve({opSnippet:yZ,dtype:"bool"}),wO={kernelName:fi,backendName:"webgl",kernelFunc:bZ};var _Z="return float(a < b);",wZ=`
+`,gZ=it({opSnippet:dZ,packedOpSnippet:hZ,dtype:"bool"}),wO={kernelName:un,backendName:"webgl",kernelFunc:gZ};function xZ(r){let{inputs:e,backend:t}=r,{input:o}=e;return ey(o,!0,t)}var kO={kernelName:ru,backendName:"webgl",kernelFunc:xZ};var yZ="return float(!isnan(x) && !isinf(x));",bZ=ve({opSnippet:yZ,dtype:"bool"}),vO={kernelName:pi,backendName:"webgl",kernelFunc:bZ};var _Z="return float(isinf(x));",wZ=ve({opSnippet:_Z,dtype:"bool"}),CO={kernelName:mi,backendName:"webgl",kernelFunc:wZ};var kZ="return float(isnan(x));",vZ=ve({opSnippet:kZ,dtype:"bool"}),IO={kernelName:fi,backendName:"webgl",kernelFunc:vZ};var CZ="return float(a < b);",IZ=`
   return vec4(lessThan(a, b));
-`,kZ=it({opSnippet:_Z,packedOpSnippet:wZ,cpuKernelImpl:rR,dtype:"bool"}),kO={kernelName:di,backendName:"webgl",kernelFunc:kZ};var vZ="return float(a <= b);",CZ=`
+`,NZ=it({opSnippet:CZ,packedOpSnippet:IZ,cpuKernelImpl:iR,dtype:"bool"}),NO={kernelName:di,backendName:"webgl",kernelFunc:NZ};var SZ="return float(a <= b);",TZ=`
   return vec4(lessThanEqual(a, b));
-`,IZ=it({opSnippet:vZ,packedOpSnippet:CZ,dtype:"bool"}),vO={kernelName:hi,backendName:"webgl",kernelFunc:IZ};function NZ(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=oR(o,n,s);return e.makeTensorInfo([a.length],"float32",a)}var CO={kernelName:nu,backendName:"webgl",kernelFunc:NZ};var SZ=`if (x < 0.0) return NAN;
-  return log(x);`,TZ=`
+`,EZ=it({opSnippet:SZ,packedOpSnippet:TZ,dtype:"bool"}),SO={kernelName:hi,backendName:"webgl",kernelFunc:EZ};function AZ(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=aR(o,n,s);return e.makeTensorInfo([a.length],"float32",a)}var TO={kernelName:nu,backendName:"webgl",kernelFunc:AZ};var DZ=`if (x < 0.0) return NAN;
+  return log(x);`,$Z=`
   vec4 result = log(x);
   vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
   result.r = isNaN.r == 1.0 ? NAN : result.r;
@@ -2995,16 +2995,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
   result.a = isNaN.a == 1.0 ? NAN : result.a;
 
   return result;
-`,EZ=ve({opSnippet:SZ,packedOpSnippet:TZ,cpuKernelImpl:nR}),IO={kernelName:an,backendName:"webgl",kernelFunc:EZ};var AZ="return log(1.0 + x);",DZ=ve({opSnippet:AZ}),NO={kernelName:gi,backendName:"webgl",kernelFunc:DZ};var $Z="return float(a >= 1.0 && b >= 1.0);",RZ=`
+`,RZ=ve({opSnippet:DZ,packedOpSnippet:$Z,cpuKernelImpl:lR}),EO={kernelName:pn,backendName:"webgl",kernelFunc:RZ};var FZ="return log(1.0 + x);",OZ=ve({opSnippet:FZ}),AO={kernelName:gi,backendName:"webgl",kernelFunc:OZ};var PZ="return float(a >= 1.0 && b >= 1.0);",MZ=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,FZ=it({opSnippet:$Z,packedOpSnippet:RZ,dtype:"bool"}),SO={kernelName:xi,backendName:"webgl",kernelFunc:FZ};var OZ="return float(!(x >= 1.0));",PZ=ve({opSnippet:OZ}),TO={kernelName:Za,backendName:"webgl",kernelFunc:PZ};var MZ="return float(a >= 1.0 || b >= 1.0);",LZ=`
+`,LZ=it({opSnippet:PZ,packedOpSnippet:MZ,dtype:"bool"}),DO={kernelName:xi,backendName:"webgl",kernelFunc:LZ};var zZ="return float(!(x >= 1.0));",BZ=ve({opSnippet:zZ}),$O={kernelName:Za,backendName:"webgl",kernelFunc:BZ};var VZ="return float(a >= 1.0 || b >= 1.0);",GZ=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,zZ=it({opSnippet:MZ,packedOpSnippet:LZ,dtype:"bool"}),EO={kernelName:Ja,backendName:"webgl",kernelFunc:zZ};var kC=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
+`,WZ=it({opSnippet:VZ,packedOpSnippet:GZ,dtype:"bool"}),RO={kernelName:Ja,backendName:"webgl",kernelFunc:WZ};var IC=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3023,7 +3023,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         float val = x * ${l};
         setOutput(val);
       }
-    `}};var vC=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
+    `}};var NC=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -3085,7 +3085,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         vec4 result = xAtOutputCoords * ${l};
         setOutput(result);
       }
-    `}};var BZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=o,u=G().getBool("WEBGL_PACK_NORMALIZATION")?new vC(n.shape,s,a,i,l):new kC(n.shape,s,a,i,l);return t.runWebGLProgram(u,[n],n.dtype)},AO={kernelName:ca,backendName:"webgl",kernelFunc:BZ};var CC=class{constructor(e,t,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`
+    `}};var UZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=o,u=U().getBool("WEBGL_PACK_NORMALIZATION")?new NC(n.shape,s,a,i,l):new IC(n.shape,s,a,i,l);return t.runWebGLProgram(u,[n],n.dtype)},FO={kernelName:ca,backendName:"webgl",kernelFunc:UZ};var SC=class{constructor(e,t,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3140,14 +3140,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
       }
       setOutput(result);
       }
-    `}};var VZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=o,p=new CC(n.shape,i,l,u,c);return t.runWebGLProgram(p,[n,s,a],n.dtype)},DO={kernelName:su,backendName:"webgl",kernelFunc:VZ};function $O(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=ce({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=Io(i,r.dtype,"max",o),u=ce({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}function IC(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([n]),f=n;if(p){if(m){let _=t.texData.get(f.dataId).values,w=new Array(i);for(let A=0;A<w.length;A++)w[A]=n.shape[c[A]];let v=Up(_,n.shape,n.dtype,c,w);f=t.makeTensorInfo(w,n.dtype);let $=t.texData.get(f.dataId);$.values=v}else f=Al(n,c,t);u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("max",u,i);let[d,h]=N.computeOutAndReduceShapes(f.shape,u),g=d;a&&(g=N.expandShapeToKeepDim(d,l));let x;if(m){let _=t.texData.get(f.dataId).values,w=sR(_,y.sizeFromShape(h),g,n.dtype);x=t.makeTensorInfo(g,n.dtype);let v=t.texData.get(x.dataId);v.values=w}else x=$O(f,h,g,t);return p&&t.disposeIntermediateTensorInfo(f),x}var RO={kernelName:ln,backendName:"webgl",kernelFunc:IC};var GZ=Px+`
+    `}};var jZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=o,p=new SC(n.shape,i,l,u,c);return t.runWebGLProgram(p,[n,s,a],n.dtype)},OO={kernelName:su,backendName:"webgl",kernelFunc:jZ};function PO(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=ce({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=Io(i,r.dtype,"max",o),u=ce({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}function TC(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([n]),f=n;if(p){if(m){let _=t.texData.get(f.dataId).values,w=new Array(i);for(let A=0;A<w.length;A++)w[A]=n.shape[c[A]];let v=qp(_,n.shape,n.dtype,c,w);f=t.makeTensorInfo(w,n.dtype);let $=t.texData.get(f.dataId);$.values=v}else f=Al(n,c,t);u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("max",u,i);let[d,h]=N.computeOutAndReduceShapes(f.shape,u),g=d;a&&(g=N.expandShapeToKeepDim(d,l));let x;if(m){let _=t.texData.get(f.dataId).values,w=uR(_,y.sizeFromShape(h),g,n.dtype);x=t.makeTensorInfo(g,n.dtype);let v=t.texData.get(x.dataId);v.values=w}else x=PO(f,h,g,t);return p&&t.disposeIntermediateTensorInfo(f),x}var MO={kernelName:mn,backendName:"webgl",kernelFunc:TC};var HZ=Gx+`
   return max(a, b);
-`,WZ=`
+`,qZ=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+Tl+`
   return result;
-`,UZ=it({opSnippet:GZ,packedOpSnippet:WZ,cpuKernelImpl:iR}),FO={kernelName:un,backendName:"webgl",kernelFunc:UZ};function jZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Yi(n,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:n},backend:t});let p=new Zi(c,"max",!1);return t.runWebGLProgram(p,[n],n.dtype)}var OO={kernelName:cn,backendName:"webgl",kernelFunc:jZ};function HZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=o,c=[1,1,1],p=N.computePool3DInfo(n.shape,s,a,c,i,u,l),m=new cc(p,"max",!1);return t.runWebGLProgram(m,[n],n.dtype)}var PO={kernelName:pa,backendName:"webgl",kernelFunc:HZ};var NC=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,l=a-1-e.padInfo.left,u=s*a-1;this.userCode=`
+`,KZ=it({opSnippet:HZ,packedOpSnippet:qZ,cpuKernelImpl:cR}),LO={kernelName:fn,backendName:"webgl",kernelFunc:KZ};function XZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Yi(n,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:n},backend:t});let p=new Zi(c,"max",!1);return t.runWebGLProgram(p,[n],n.dtype)}var zO={kernelName:dn,backendName:"webgl",kernelFunc:XZ};function YZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=o,c=[1,1,1],p=N.computePool3DInfo(n.shape,s,a,c,i,u,l),m=new pc(p,"max",!1);return t.runWebGLProgram(m,[n],n.dtype)}var BO={kernelName:pa,backendName:"webgl",kernelFunc:YZ};var EC=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,l=a-1-e.padInfo.left,u=s*a-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${l});
 
       void main() {
@@ -3193,7 +3193,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},SC=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=u-1-e.padInfo.top,f=c-1-e.padInfo.left,d=l*u*c-1;this.userCode=`
+    `}},AC=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=u-1-e.padInfo.top,f=c-1-e.padInfo.left,d=l*u*c-1;this.userCode=`
       const ivec3 pads = ivec3(${p}, ${m}, ${f});
 
       void main() {
@@ -3257,14 +3257,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function qZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new cc(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new SC(m),g=t.runWebGLProgram(h,[n,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var MO={kernelName:au,backendName:"webgl",kernelFunc:qZ};function KZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Yi([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=o,m=N.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new Zi(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new NC(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var LO={kernelName:iu,backendName:"webgl",kernelFunc:KZ};function zO(r,e,t,o){let n=new Zi(t,"max",!1),s=o.runWebGLProgram(n,[r],"float32");n=new Zi(t,"max",!0,!0,e);let a=o.runWebGLProgram(n,[r],"float32");return[s,a]}var BO={kernelName:lu,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=N.computePool2DInfo(o.shape,n,s,u,a),[p,m]=zO(o,i,c,l);return[p,m]}};function VO(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=ce({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=Io(i,"float32","mean",o),u=ce({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}var GO={kernelName:pn,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{keepDims:n,axis:s}=e,a=t,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([o]),f=[],d=o;if(p){if(m){let w=a.texData.get(d.dataId).values,v=new Array(i);for(let R=0;R<v.length;R++)v[R]=o.shape[c[R]];let $=Up(w,o.shape,o.dtype,c,v);d=a.makeTensorInfo(v,o.dtype);let A=a.texData.get(d.dataId);A.values=$}else d=Al(o,c,a);f.push(d),u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("sum",u,i);let[h,g]=N.computeOutAndReduceShapes(d.shape,u),x=h;n&&(x=N.expandShapeToKeepDim(h,l));let b=VO(d,g,x,a);for(let _ of f)a.disposeIntermediateTensorInfo(_);return b}};function XZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,n.shape.length)),N.assertAxesAreInnerMostDims("min",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=ce({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Io(h,h.dtype,"min",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=ce({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=ce({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var WO={kernelName:mn,backendName:"webgl",kernelFunc:XZ};var YZ=Px+`
+    `}};function ZZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new pc(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new AC(m),g=t.runWebGLProgram(h,[n,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var VO={kernelName:au,backendName:"webgl",kernelFunc:ZZ};function JZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Yi([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=o,m=N.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new Zi(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new EC(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var GO={kernelName:iu,backendName:"webgl",kernelFunc:JZ};function WO(r,e,t,o){let n=new Zi(t,"max",!1),s=o.runWebGLProgram(n,[r],"float32");n=new Zi(t,"max",!0,!0,e);let a=o.runWebGLProgram(n,[r],"float32");return[s,a]}var UO={kernelName:lu,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=N.computePool2DInfo(o.shape,n,s,u,a),[p,m]=WO(o,i,c,l);return[p,m]}};function jO(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=ce({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=Io(i,"float32","mean",o),u=ce({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}var HO={kernelName:hn,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{keepDims:n,axis:s}=e,a=t,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([o]),f=[],d=o;if(p){if(m){let w=a.texData.get(d.dataId).values,v=new Array(i);for(let R=0;R<v.length;R++)v[R]=o.shape[c[R]];let $=qp(w,o.shape,o.dtype,c,v);d=a.makeTensorInfo(v,o.dtype);let A=a.texData.get(d.dataId);A.values=$}else d=Al(o,c,a);f.push(d),u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("sum",u,i);let[h,g]=N.computeOutAndReduceShapes(d.shape,u),x=h;n&&(x=N.expandShapeToKeepDim(h,l));let b=jO(d,g,x,a);for(let _ of f)a.disposeIntermediateTensorInfo(_);return b}};function QZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,n.shape.length)),N.assertAxesAreInnerMostDims("min",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=ce({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Io(h,h.dtype,"min",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=ce({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=ce({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var qO={kernelName:gn,backendName:"webgl",kernelFunc:QZ};var eJ=Gx+`
   return min(a, b);
-`,ZZ=`
+`,tJ=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+Tl+`
   return result;
-`,JZ=it({opSnippet:YZ,packedOpSnippet:ZZ,cpuKernelImpl:aR}),UO={kernelName:fn,backendName:"webgl",kernelFunc:JZ};var TC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((c,p)=>c[0]+e[p]+c[1]);let n=e.length,s=Le(n),a=t.map(c=>c[0]).join(","),i=t.map((c,p)=>c[0]+e[p]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),u=o==="reflect"?0:1;if(n===1){this.userCode=`
+`,rJ=it({opSnippet:eJ,packedOpSnippet:tJ,cpuKernelImpl:pR}),KO={kernelName:xn,backendName:"webgl",kernelFunc:rJ};var DC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((c,p)=>c[0]+e[p]+c[1]);let n=e.length,s=Le(n),a=t.map(c=>c[0]).join(","),i=t.map((c,p)=>c[0]+e[p]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),u=o==="reflect"?0:1;if(n===1){this.userCode=`
         int start = ${a};
         int end = ${i};
 
@@ -3293,7 +3293,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         ${s} coords = outC - start;
         setOutput(getX(${l}));
       }
-    `}};var EC=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let n=e.length,s=Le(n),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=Ut("rc",n),u=Ut("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,f="";if(n===1){let d=`
+    `}};var $C=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let n=e.length,s=Le(n),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=Ut("rc",n),u=Ut("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,f="";if(n===1){let d=`
         ${s} source = rc;
         if (source < start) {
           source = start * 2 - source - ${m};
@@ -3349,13 +3349,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         ${f}
         setOutput(result);
       }
-    `}};var QZ=({inputs:r,backend:e,attrs:t})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new EC(o.shape,n,s):new TC(o.shape,n,s);return e.runWebGLProgram(a,[o],o.dtype)},jO={kernelName:ma,backendName:"webgl",kernelFunc:QZ};var e9=`if (b == 0.0) return NAN;
-  return mod(a, b);`,t9=`
+    `}};var oJ=({inputs:r,backend:e,attrs:t})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $C(o.shape,n,s):new DC(o.shape,n,s);return e.runWebGLProgram(a,[o],o.dtype)},XO={kernelName:ma,backendName:"webgl",kernelFunc:oJ};var nJ=`if (b == 0.0) return NAN;
+  return mod(a, b);`,sJ=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
   `+Tl+`
   return result;
-`,r9=it({opSnippet:e9,packedOpSnippet:t9}),HO={kernelName:yi,backendName:"webgl",kernelFunc:r9};var AC=class{constructor(e,t,o){this.variableNames=["probs"],this.outputShape=[e,o],this.userCode=`
+`,iJ=it({opSnippet:nJ,packedOpSnippet:sJ}),YO={kernelName:yi,backendName:"webgl",kernelFunc:iJ};var RC=class{constructor(e,t,o){this.variableNames=["probs"],this.outputShape=[e,o],this.userCode=`
       uniform float seed;
 
       void main() {
@@ -3377,11 +3377,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),uF={kernelNam
         // If no other event happened, last event happened.
         setOutput(float(${t-1}));
       }
-    `}getCustomSetupFunc(e){return(t,o)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(o,"seed")),t.gl.uniform1f(this.seedLoc,e)}}};var o9=`
+    `}getCustomSetupFunc(e){return(t,o)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(o,"seed")),t.gl.uniform1f(this.seedLoc,e)}}};var aJ=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,n9=`
+return a / b;`,lJ=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3399,14 +3399,14 @@ return a / b;`,n9=`
   }
 
   return result;
-`,DC=it({opSnippet:o9,packedOpSnippet:n9,checkOutOfBounds:!0}),qO={kernelName:Qo,backendName:"webgl",kernelFunc:DC};var KO="return a - b;",$C=it({opSnippet:KO,packedOpSnippet:KO,supportsComplex:!0,cpuKernelImpl:hR}),XO={kernelName:Dn,backendName:"webgl",kernelFunc:$C};function RC(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=IC({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,a),u=ce({inputs:{x:i},backend:t,attrs:{shape:l}}),c=$C({inputs:{a:n,b:u},backend:t}),p=hC({inputs:{x:c},backend:t}),m=ah({inputs:{x:p},backend:t,attrs:{axis:a,keepDims:!1}}),f=ce({inputs:{x:m},backend:t,attrs:{shape:l}}),d=DC({inputs:{a:p,b:f},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}var YO={kernelName:En,backendName:"webgl",kernelFunc:RC};function s9(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,l=i?n:RC({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new AC(u,c,s),m=p.getCustomSetupFunc(a),f=t.runWebGLProgram(p,[l],"int32",m);return i||t.disposeIntermediateTensorInfo(l),f}var ZO={kernelName:uu,backendName:"webgl",kernelFunc:s9};var JO="return -x;";function i9(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.texData.get(o.dataId),[a,i]=uR(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Bs(o.shape,JO):n=new uo(o.shape,JO),t.runWebGLProgram(n,[o],o.dtype)}var QO={kernelName:cs,backendName:"webgl",kernelFunc:i9};var a9=Sr.nonMaxSuppressionV3Impl;function l9(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:p}=a9(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var eP={kernelName:_i,backendName:"webgl",kernelFunc:l9};var u9=Sr.nonMaxSuppressionV4Impl;function c9(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=u9(c,p,a,i,l,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([f]))]}var tP={kernelName:wi,backendName:"webgl",kernelFunc:c9};var p9=Sr.nonMaxSuppressionV5Impl;function m9(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),m=a,f=i,d=l,h=u,{selectedIndices:g,selectedScores:x}=p9(c,p,m,f,d,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var rP={kernelName:ki,backendName:"webgl",kernelFunc:m9};var FC=class{constructor(e,t,o,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`,FC=it({opSnippet:aJ,packedOpSnippet:lJ,checkOutOfBounds:!0}),ZO={kernelName:on,backendName:"webgl",kernelFunc:FC};var JO="return a - b;",OC=it({opSnippet:JO,packedOpSnippet:JO,supportsComplex:!0,cpuKernelImpl:bR}),QO={kernelName:On,backendName:"webgl",kernelFunc:OC};function PC(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=TC({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,a),u=ce({inputs:{x:i},backend:t,attrs:{shape:l}}),c=OC({inputs:{a:n,b:u},backend:t}),p=yC({inputs:{x:c},backend:t}),m=ch({inputs:{x:p},backend:t,attrs:{axis:a,keepDims:!1}}),f=ce({inputs:{x:m},backend:t,attrs:{shape:l}}),d=FC({inputs:{a:p,b:f},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}var eP={kernelName:Rn,backendName:"webgl",kernelFunc:PC};function uJ(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,l=i?n:PC({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new RC(u,c,s),m=p.getCustomSetupFunc(a),f=t.runWebGLProgram(p,[l],"int32",m);return i||t.disposeIntermediateTensorInfo(l),f}var tP={kernelName:uu,backendName:"webgl",kernelFunc:uJ};var rP="return -x;";function cJ(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.texData.get(o.dataId),[a,i]=fR(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n;return U().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Gs(o.shape,rP):n=new uo(o.shape,rP),t.runWebGLProgram(n,[o],o.dtype)}var oP={kernelName:fs,backendName:"webgl",kernelFunc:cJ};var pJ=Sr.nonMaxSuppressionV3Impl;function mJ(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:p}=pJ(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var nP={kernelName:_i,backendName:"webgl",kernelFunc:mJ};var fJ=Sr.nonMaxSuppressionV4Impl;function dJ(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=fJ(c,p,a,i,l,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([f]))]}var sP={kernelName:wi,backendName:"webgl",kernelFunc:dJ};var hJ=Sr.nonMaxSuppressionV5Impl;function gJ(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),m=a,f=i,d=l,h=u,{selectedIndices:g,selectedScores:x}=hJ(c,p,m,f,d,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var iP={kernelName:ki,backendName:"webgl",kernelFunc:gJ};var MC=class{constructor(e,t,o,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${n}), float(${o}),
                       float(index == coords.y)));
       }
-    `}};var f9=r=>{let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{depth:s,onValue:a,offValue:i}=o,l=y.sizeFromShape(n.shape),u=new FC(l,s,a,i),c=ce({inputs:{x:n},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],n.dtype);t.disposeIntermediateTensorInfo(c);let m=[...n.shape,s],f=ce({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},oP={kernelName:hn,backendName:"webgl",kernelFunc:f9};function mh(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=ja({inputs:{input:o},backend:t}),s=mh({inputs:{x:n},backend:t}),a=pc({inputs:{input:o},backend:t}),i=mh({inputs:{x:a},backend:t}),l=co({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return ph({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var nP={kernelName:ys,backendName:"webgl",kernelFunc:mh};function sP(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=ja({inputs:{input:o},backend:t}),s=sP({inputs:{x:n},backend:t}),a=pc({inputs:{input:o},backend:t}),i=mh({inputs:{x:a},backend:t}),l=co({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return ph({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var iP={kernelName:ps,backendName:"webgl",kernelFunc:sP};function d9(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Hx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=Hx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(p),p}),u=rC({inputs:l,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var aP={kernelName:ms,backendName:"webgl",kernelFunc:d9};var OC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=Le(n),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
+    `}};var xJ=r=>{let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{depth:s,onValue:a,offValue:i}=o,l=y.sizeFromShape(n.shape),u=new MC(l,s,a,i),c=ce({inputs:{x:n},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],n.dtype);t.disposeIntermediateTensorInfo(c);let m=[...n.shape,s],f=ce({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},aP={kernelName:bn,backendName:"webgl",kernelFunc:xJ};function hh(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=ja({inputs:{input:o},backend:t}),s=hh({inputs:{x:n},backend:t}),a=mc({inputs:{input:o},backend:t}),i=hh({inputs:{x:a},backend:t}),l=co({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return dh({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var lP={kernelName:ws,backendName:"webgl",kernelFunc:hh};function uP(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=ja({inputs:{input:o},backend:t}),s=uP({inputs:{x:n},backend:t}),a=mc({inputs:{input:o},backend:t}),i=hh({inputs:{x:a},backend:t}),l=co({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return dh({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var cP={kernelName:ds,backendName:"webgl",kernelFunc:uP};function yJ(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Jx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=Jx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(p),p}),u=sC({inputs:l,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var pP={kernelName:hs,backendName:"webgl",kernelFunc:yJ};var LC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=Le(n),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
         int start = ${a};
         int end = ${i};
 
@@ -3431,7 +3431,7 @@ return a / b;`,n9=`
           setOutput(getX(${l}));
         }
       }
-    `}};var PC=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Le(n),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=Ut("rc",n),u=Ut("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[n-1]} += 1;
+    `}};var zC=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Le(n),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=Ut("rc",n),u=Ut("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[n-1]} += 1;
        if(${c}) {
       `,n===1?"":`}
        rc = outputLoc;
@@ -3455,7 +3455,7 @@ return a / b;`,n9=`
         ${d}
         setOutput(result);
       }
-    `}};var MC=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o,i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new PC(n.shape,s,a):new OC(n.shape,s,a);return t.runWebGLProgram(i,[n],n.dtype)},lP={kernelName:gn,backendName:"webgl",kernelFunc:MC};var h9=`
+    `}};var BC=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o,i=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zC(n.shape,s,a):new LC(n.shape,s,a);return t.runWebGLProgram(i,[n],n.dtype)},mP={kernelName:_n,backendName:"webgl",kernelFunc:BC};var bJ=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3464,7 +3464,7 @@ return a / b;`,n9=`
   }
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,g9=`
+`,_J=`
   // 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);
@@ -3480,9 +3480,9 @@ return a / b;`,n9=`
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
   `+Tl+`
   return result;
-`,x9=it({opSnippet:h9,packedOpSnippet:g9}),uP={kernelName:xn,backendName:"webgl",kernelFunc:x9};function y9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=[],u=y.parseAxisParam(s,n.shape),c=u,p=N.getAxesPermutation(c,i),m=n;p!=null&&(m=Lt({inputs:{x:n},backend:t,attrs:{perm:p}}),c=N.getInnerMostAxes(c.length,i),l.push(m)),N.assertAxesAreInnerMostDims("prod",c,i);let f;if(t.shouldExecuteOnCPU([m])){let d=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=cR(m.shape,m.dtype,d,c);f=t.makeTensorInfo(g,x,h)}else{let[d,h]=N.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=ce({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=hu(n.dtype),_=Io(x,b,"prod",t);f=ce({inputs:{x:_},backend:t,attrs:{shape:d}}),l.push(x),l.push(_)}if(a){l.push(f);let d=N.expandShapeToKeepDim(f.shape,u);f=ce({inputs:{x:f},backend:t,attrs:{shape:d}})}return l.forEach(d=>t.disposeIntermediateTensorInfo(d)),f}var cP={kernelName:vi,backendName:"webgl",kernelFunc:y9};var LC=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=pR(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},pP={kernelName:fa,backendName:"webgl",kernelFunc:LC};var b9="return 1.0 / x;",_9=ve({opSnippet:b9}),mP={kernelName:Ci,backendName:"webgl",kernelFunc:_9};var w9=gr+`
+`,wJ=it({opSnippet:bJ,packedOpSnippet:_J}),fP={kernelName:wn,backendName:"webgl",kernelFunc:wJ};function kJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=[],u=y.parseAxisParam(s,n.shape),c=u,p=N.getAxesPermutation(c,i),m=n;p!=null&&(m=Lt({inputs:{x:n},backend:t,attrs:{perm:p}}),c=N.getInnerMostAxes(c.length,i),l.push(m)),N.assertAxesAreInnerMostDims("prod",c,i);let f;if(t.shouldExecuteOnCPU([m])){let d=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=dR(m.shape,m.dtype,d,c);f=t.makeTensorInfo(g,x,h)}else{let[d,h]=N.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=ce({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=gu(n.dtype),_=Io(x,b,"prod",t);f=ce({inputs:{x:_},backend:t,attrs:{shape:d}}),l.push(x),l.push(_)}if(a){l.push(f);let d=N.expandShapeToKeepDim(f.shape,u);f=ce({inputs:{x:f},backend:t,attrs:{shape:d}})}return l.forEach(d=>t.disposeIntermediateTensorInfo(d)),f}var dP={kernelName:vi,backendName:"webgl",kernelFunc:kJ};var VC=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=hR(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},hP={kernelName:fa,backendName:"webgl",kernelFunc:VC};var vJ="return 1.0 / x;",CJ=ve({opSnippet:vJ}),gP={kernelName:Ci,backendName:"webgl",kernelFunc:CJ};var IJ=gr+`
   return (x < 0.0) ? 0.0 : x;
-`,k9=`
+`,NJ=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3492,9 +3492,9 @@ return a / b;`,n9=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,v9=ve({opSnippet:w9,packedOpSnippet:k9}),fP={kernelName:bn,backendName:"webgl",kernelFunc:v9};var C9=gr+`
+`,SJ=ve({opSnippet:IJ,packedOpSnippet:NJ}),xP={kernelName:vn,backendName:"webgl",kernelFunc:SJ};var TJ=gr+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,I9=`
+`,EJ=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3504,7 +3504,7 @@ return a / b;`,n9=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,N9=ve({opSnippet:C9,packedOpSnippet:I9}),dP={kernelName:wn,backendName:"webgl",kernelFunc:N9};var zC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+`,AJ=ve({opSnippet:TJ,packedOpSnippet:EJ}),yP={kernelName:In,backendName:"webgl",kernelFunc:AJ};var GC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${c[0]/p[0]},
           ${c[1]/p[1]});
@@ -3537,7 +3537,7 @@ return a / b;`,n9=`
 
         setOutput(newValue);
       }
-    `}};var BC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};var WC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${c[0]/p[0]},
           ${c[1]/p[1]},
@@ -3614,7 +3614,7 @@ return a / b;`,n9=`
 
         setOutput(newValue);
       }
-    `}};function S9(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new BC(n.shape,l,u,s,a):new zC(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],"float32")}var hP={kernelName:_n,backendName:"webgl",kernelFunc:S9};var VC=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
+    `}};function DJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=U().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new WC(n.shape,l,u,s,a):new GC(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],"float32")}var bP={kernelName:Cn,backendName:"webgl",kernelFunc:DJ};var UC=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3695,7 +3695,7 @@ return a / b;`,n9=`
 
         setOutput(accumulator);
       }
-    `}};function T9(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new VC(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var gP={kernelName:mu,backendName:"webgl",kernelFunc:T9};var GC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function $J(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new UC(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var _P={kernelName:mu,backendName:"webgl",kernelFunc:$J};var jC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${c[0]/p[0]},
           ${c[1]/p[1]});
@@ -3717,7 +3717,7 @@ return a / b;`,n9=`
 
         setOutput(newValue);
       }
-    `}};function E9(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=new GC(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var xP={kernelName:da,backendName:"webgl",kernelFunc:E9};var WC=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
+    `}};function RJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=new jC(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var wP={kernelName:da,backendName:"webgl",kernelFunc:RJ};var HC=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3787,7 +3787,7 @@ return a / b;`,n9=`
 
         setOutput(accumulator);
       }
-    `}};function A9(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new WC(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var yP={kernelName:pu,backendName:"webgl",kernelFunc:A9};var UC=class{constructor(e,t){this.variableNames=["x"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`
+    `}};function FJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new HC(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var kP={kernelName:pu,backendName:"webgl",kernelFunc:FJ};var qC=class{constructor(e,t){this.variableNames=["x"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
           setOutput(getX(${e[0]} - coord - 1));
@@ -3797,7 +3797,7 @@ return a / b;`,n9=`
         ${a} coords = getOutputCoords();
         setOutput(getX(${s}));
       }
-    `}};var jC=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Ut("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Le(o);o===1?this.userCode=`
+    `}};var KC=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Ut("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Le(o);o===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
@@ -3825,7 +3825,7 @@ return a / b;`,n9=`
           }
           setOutput(result);
         }
-    `;function l(d){return m(d)}function u(d){return d[o-1]="("+d[o-1]+" + 1)",m(d)}function c(d){return d[o-2]="("+d[o-2]+" + 1)",m(d)}function p(d){return d[o-1]="("+d[o-1]+" + 1)",d[o-2]="("+d[o-2]+" + 1)",m(d)}function m(d){let h=e.map((b,_)=>f(_,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${h[d]} - 1`:`${h[d]}`}}};function D9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return jt({inputs:{x:n},backend:t});let l=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new jC(n.shape,i):new UC(n.shape,i);return t.runWebGLProgram(l,[n],n.dtype)}var bP={kernelName:kn,backendName:"webgl",kernelFunc:D9};var HC=class{constructor(e,t,o,n){this.variableNames=["Image"],this.outputShape=[];let s=e[1],a=e[2],i=Math.sin(t).toFixed(3),l=Math.cos(t).toFixed(3);this.outputShape=e;let[u,c]=N.getImageCenter(n,s,a),p=u.toFixed(3),m=c.toFixed(3),f="";typeof o=="number"?f=`float outputValue = ${o.toFixed(2)};`:f=`
+    `;function l(d){return m(d)}function u(d){return d[o-1]="("+d[o-1]+" + 1)",m(d)}function c(d){return d[o-2]="("+d[o-2]+" + 1)",m(d)}function p(d){return d[o-1]="("+d[o-1]+" + 1)",d[o-2]="("+d[o-2]+" + 1)",m(d)}function m(d){let h=e.map((b,_)=>f(_,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${h[d]} - 1`:`${h[d]}`}}};function OJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return jt({inputs:{x:n},backend:t});let l=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new KC(n.shape,i):new qC(n.shape,i);return t.runWebGLProgram(l,[n],n.dtype)}var vP={kernelName:Nn,backendName:"webgl",kernelFunc:OJ};var XC=class{constructor(e,t,o,n){this.variableNames=["Image"],this.outputShape=[];let s=e[1],a=e[2],i=Math.sin(t).toFixed(3),l=Math.cos(t).toFixed(3);this.outputShape=e;let[u,c]=N.getImageCenter(n,s,a),p=u.toFixed(3),m=c.toFixed(3),f="";typeof o=="number"?f=`float outputValue = ${o.toFixed(2)};`:f=`
         vec3 fill = vec3(${o.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         void main() {
@@ -3842,7 +3842,7 @@ return a / b;`,n9=`
           }
           setOutput(outputValue);
         }
-    `}};var _P={kernelName:Ri,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,l=new HC(o.shape,n,s,a);return i.runWebGLProgram(l,[o],o.dtype)}};var $9=`
+    `}};var CP={kernelName:Ri,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,l=new XC(o.shape,n,s,a);return i.runWebGLProgram(l,[o],o.dtype)}};var PJ=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -3857,7 +3857,7 @@ return a / b;`,n9=`
       return base + 1.0;
     }
   }
-`,R9=ve({opSnippet:$9}),wP={kernelName:vn,backendName:"webgl",kernelFunc:R9};var F9="return inversesqrt(x);",O9=ve({opSnippet:F9,cpuKernelImpl:mR}),kP={kernelName:Cn,backendName:"webgl",kernelFunc:O9};var fh=class{constructor(e,t,o,n,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let l=Le(s.length),u=Le(a.length),c="";o===1?c="i":o===2&&(c="i, j");let p=`getIndices(${c})`,m="";n===1?m="i":n===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=t>1?"strides[j]":"strides";this.userCode=`
+`,MJ=ve({opSnippet:PJ}),IP={kernelName:Sn,backendName:"webgl",kernelFunc:MJ};var LJ="return inversesqrt(x);",zJ=ve({opSnippet:LJ,cpuKernelImpl:gR}),NP={kernelName:Tn,backendName:"webgl",kernelFunc:zJ};var gh=class{constructor(e,t,o,n,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let l=Le(s.length),u=Le(a.length),c="";o===1?c="i":o===2&&(c="i, j");let p=`getIndices(${c})`,m="";n===1?m="i":n===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=t>1?"strides[j]":"strides";this.userCode=`
         ${l} strides = ${l}(${s});
 
         void main() {
@@ -3877,7 +3877,7 @@ return a / b;`,n9=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function P9(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=N.calculateShapes(s,n,a),m=[p/u,u];if(p===0)return t.makeTensorInfo(a,n.dtype);let f=ce({inputs:{x:n},backend:t,attrs:{shape:[l,i]}}),d=ce({inputs:{x:s},backend:t,attrs:{shape:[l,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g=new fh(l,i,f.shape.length,d.shape.length,c,m),x=t.runWebGLProgram(g,[d,f,h],d.dtype),b=ce({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var vP={kernelName:Ii,backendName:"webgl",kernelFunc:P9};var qC=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],l=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&l.push(`${i[c]}`);n=l.join(),s=u.join()}let a=Le(o);this.userCode=`
+      `}};function BJ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=N.calculateShapes(s,n,a),m=[p/u,u];if(p===0)return t.makeTensorInfo(a,n.dtype);let f=ce({inputs:{x:n},backend:t,attrs:{shape:[l,i]}}),d=ce({inputs:{x:s},backend:t,attrs:{shape:[l,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g=new gh(l,i,f.shape.length,d.shape.length,c,m),x=t.runWebGLProgram(g,[d,f,h],d.dtype),b=ce({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var SP={kernelName:Ii,backendName:"webgl",kernelFunc:BJ};var YC=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],l=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&l.push(`${i[c]}`);n=l.join(),s=u.join()}let a=Le(o);this.userCode=`
       void main() {
         ${a} resRC = getOutputCoords();
         float cVal = getC(${n});
@@ -3887,21 +3887,21 @@ return a / b;`,n9=`
           setOutput(getB(${s}));
         }
       }
-    `}};function M9(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new qC(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],mr(n.dtype,s.dtype))}var CP={kernelName:ds,backendName:"webgl",kernelFunc:M9};var L9=`
+    `}};function VJ(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new YC(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],mr(n.dtype,s.dtype))}var TP={kernelName:xs,backendName:"webgl",kernelFunc:VJ};var GJ=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
   float scaleAlpha = ${N.SELU_SCALEALPHA};
   float scale = ${N.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,z9=ve({opSnippet:L9}),IP={kernelName:Ni,backendName:"webgl",kernelFunc:z9};var B9="return 1.0 / (1.0 + exp(-1.0 * x));",V9=ve({opSnippet:B9}),NP={kernelName:Nn,backendName:"webgl",kernelFunc:V9};var G9=`
+`,WJ=ve({opSnippet:GJ}),EP={kernelName:Ni,backendName:"webgl",kernelFunc:WJ};var UJ="return 1.0 / (1.0 + exp(-1.0 * x));",jJ=ve({opSnippet:UJ}),AP={kernelName:An,backendName:"webgl",kernelFunc:jJ};var HJ=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,W9=ve({opSnippet:G9}),SP={kernelName:Ti,backendName:"webgl",kernelFunc:W9};var U9=Mx+`
+`,qJ=ve({opSnippet:HJ}),DP={kernelName:Ti,backendName:"webgl",kernelFunc:qJ};var KJ=Wx+`
   return sin(x);
-`,j9=ve({opSnippet:U9}),TP={kernelName:In,backendName:"webgl",kernelFunc:j9};var H9=`
+`,XJ=ve({opSnippet:KJ}),$P={kernelName:En,backendName:"webgl",kernelFunc:XJ};var YJ=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,q9=ve({opSnippet:H9}),EP={kernelName:Si,backendName:"webgl",kernelFunc:q9};var K9=`
+`,ZJ=ve({opSnippet:YJ}),RP={kernelName:Si,backendName:"webgl",kernelFunc:ZJ};var JJ=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -3921,9 +3921,9 @@ return a / b;`,n9=`
     result = log(exp_x + 1.0);
   }
   return result;
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zL={kernelName:Cn,backendName:"wasm",setupFunc:KQ,kernelFunc:XQ};var BL;function YQ(r){BL=r.wasm.cwrap(Nn,null,["number","array","number","array","number","number"])}function ZQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=y.parseAxisParam(s,n.shape);if(n.shape.length===0)return dc({inputs:{x:n},backend:t});let i=t.makeOutput(n.shape,n.dtype),l=t.dataIdMap.get(n.dataId).id,u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),p=new Uint8Array(new Int32Array(n.shape).buffer);BL(l,c,a.length,p,n.shape.length,u);let m=zr({inputs:{x:i},attrs:{shape:n.shape},backend:t});return t.disposeData(i.dataId),m}var VL={kernelName:Nn,backendName:"wasm",kernelFunc:ZQ,setupFunc:YQ};var GL;function JQ(r){GL=r.wasm.cwrap(Ri,null,["number","number","number","number","number","number","number","number","array","number","number"])}function 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tidy,Po as tile,AV as time,of as topk,ul as train,Ue as transpose,Gu as truncatedNormal,ep as unique,y3 as unregisterGradient,x3 as unregisterKernel,nf as unsortedSegmentSum,ur as unstack,mr as upcastType,y as util,_W as valueAndGrad,wW as valueAndGrads,V_ as variable,pg as variableGrads,_9 as version,_x as version_converter,n_ as version_core,hl as version_layers,$ee as version_wasm,$t as where,sf as whereAsync,gt as zeros,Ie as zerosLike};
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/tfjs.esm.js.map b/dist/tfjs.esm.js.map
index 56bc3db..0477e8e 100644
--- a/dist/tfjs.esm.js.map
+++ b/dist/tfjs.esm.js.map
@@ -1,7 +1,7 @@
 {
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-  "sourcesContent": ["", "// 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 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\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}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}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(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 shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){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\"){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;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":169,\"maximum\":169+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;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)}var WASM_PAGE_SIZE=65536;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 STACK_BASE=5256480,STACKTOP=STACK_BASE,STACK_MAX=13600,DYNAMIC_BASE=5256480,DYNAMICTOP_PTR=12672;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_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_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"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_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}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){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;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 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 Math_ceil=Math.ceil;var Math_floor=Math.floor;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+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}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(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}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)&&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()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;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\"){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\");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();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13584;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(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>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 __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";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 __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";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={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12832;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13072;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___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+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(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+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>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){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},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\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(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.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}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){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},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){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;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);__ATEXIT__.unshift({func:func,arg: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_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){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{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}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];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}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){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;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(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,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)}},inEventHandler:0,currentEventHandler:null,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_async_queue_on_thread_(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_async_queue_on_thread_(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){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}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}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},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},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],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\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){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 contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};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.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() 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,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_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_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;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;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>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+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}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 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79: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: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();else PThread.initWorker();var GLctx;GL.init();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,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=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 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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)}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_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;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){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;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 exitRuntime(){runtimeExited=true}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 Math_ceil=Math.ceil;var Math_floor=Math.floor;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+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}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(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}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)&&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()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);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\"){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\");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();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};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}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Floor\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"LeakyRelu\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"MaxPool\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Maximum\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"Mean\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"Min\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Minimum\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Multiply\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"Neg\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"NotEqual\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"OneHot\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"PadV2\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"Pow\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Prelu\"]).apply(null,arguments)};var _Prod=Module[\"_Prod\"]=function(){return(_Prod=Module[\"_Prod\"]=Module[\"asm\"][\"Prod\"]).apply(null,arguments)};var _RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"RealDiv\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Relu\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Relu6\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"ResizeBilinear\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Reverse\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"RotateWithOffset\"]).apply(null,arguments)};var _Round=Module[\"_Round\"]=function(){return(_Round=Module[\"_Round\"]=Module[\"asm\"][\"Round\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Rsqrt\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"ScatterNd\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"SelectV2\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"Sigmoid\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Sin\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Softmax\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Sqrt\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Square\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"SquaredDifference\"]).apply(null,arguments)};var _Step=Module[\"_Step\"]=function(){return(_Step=Module[\"_Step\"]=Module[\"asm\"][\"Step\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"StridedSlice\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sub\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Sum\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Tanh\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Tile\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"TopK\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Transpose\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"_FusedMatMul\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"malloc\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"free\"]).apply(null,arguments)};var __start=Module[\"__start\"]=function(){return(__start=Module[\"__start\"]=Module[\"asm\"][\"_start\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"stackSave\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"stackAlloc\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"stackRestore\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}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();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);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(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}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()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n  return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModule;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModule; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModule\"] = WasmBackendModule;\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, 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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, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;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:selfThreadId})}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};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;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)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}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;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;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[\"dynCall_ii\"](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\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){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.stack)err(ex.stack);throw ex}};if(typeof 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+  "sourcesContent": ["", "// 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.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){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\"){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;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":171,\"maximum\":171+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;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)}var WASM_PAGE_SIZE=65536;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 STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_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_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"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_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}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){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;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 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 Math_ceil=Math.ceil;var Math_floor=Math.floor;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+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}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(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}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)&&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()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;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\"){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\");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();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13568;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(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>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 __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";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 __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";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={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12816;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13056;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___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+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(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+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>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){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},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\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(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.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}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){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},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){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;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);__ATEXIT__.unshift({func:func,arg: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_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){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{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}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];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}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){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;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(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,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)}},inEventHandler:0,currentEventHandler:null,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_async_queue_on_thread_(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_async_queue_on_thread_(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){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}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}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},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},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],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\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){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 contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};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.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() 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,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_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_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;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;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>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+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}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 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79: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: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();else PThread.initWorker();var GLctx;GL.init();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,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"_\"]).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\"][\"aa\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"ca\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"da\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"ea\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"fa\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"ga\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"ha\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"ia\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"ja\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"ka\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"la\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"ma\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"na\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"oa\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"qa\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"ra\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"sa\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"ta\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"ua\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"va\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"wa\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"xa\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"ya\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"za\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Aa\"]).apply(null,arguments)};var _Prod=Module[\"_Prod\"]=function(){return(_Prod=Module[\"_Prod\"]=Module[\"asm\"][\"Ba\"]).apply(null,arguments)};var _RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"Ca\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _Round=Module[\"_Round\"]=function(){return(_Round=Module[\"_Round\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var 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_Step=Module[\"_Step\"]=function(){return(_Step=Module[\"_Step\"]=Module[\"asm\"][\"Sa\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"Ta\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Ua\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Va\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Wa\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Xa\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ya\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Za\"]).apply(null,arguments)};var 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_emscripten_async_queue_on_thread_=Module[\"_emscripten_async_queue_on_thread_\"]=function(){return(_emscripten_async_queue_on_thread_=Module[\"_emscripten_async_queue_on_thread_\"]=Module[\"asm\"][\"wb\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"xb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"yb\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"zb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ab\"]).apply(null,arguments)};var dynCall_vi=Module[\"dynCall_vi\"]=function(){return(dynCall_vi=Module[\"dynCall_vi\"]=Module[\"asm\"][\"Bb\"]).apply(null,arguments)};var 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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();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()()}}if(!ENVIRONMENT_IS_PTHREAD)noExitRuntime=true;if(!ENVIRONMENT_IS_PTHREAD)run();\n\n\n  return WasmBackendModuleThreadedSimd\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModuleThreadedSimd;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModuleThreadedSimd; });\n    else 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 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 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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 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INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;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){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;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 exitRuntime(){runtimeExited=true}function 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callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). 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diff --git a/package-lock.json b/package-lock.json
index c30de7e..9ef5350 100644
--- a/package-lock.json
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+      "resolved": "https://registry.npmjs.org/@typescript-eslint/typescript-estree/-/typescript-estree-4.15.1.tgz",
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-        "@typescript-eslint/visitor-keys": "4.15.0",
+        "@typescript-eslint/types": "4.15.1",
+        "@typescript-eslint/visitor-keys": "4.15.1",
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@@ -500,12 +498,12 @@
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+      "resolved": "https://registry.npmjs.org/@typescript-eslint/visitor-keys/-/visitor-keys-4.15.1.tgz",
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+        "@typescript-eslint/types": "4.15.1",
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@@ -963,9 +961,9 @@
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+      "resolved": "https://registry.npmjs.org/esbuild/-/esbuild-0.8.46.tgz",
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@@ -1413,9 +1411,9 @@
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+      "resolved": "https://registry.npmjs.org/form-data/-/form-data-3.0.1.tgz",
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@@ -2646,9 +2644,9 @@
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+          "version": "7.1.1",
+          "resolved": "https://registry.npmjs.org/ajv/-/ajv-7.1.1.tgz",
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@@ -2921,9 +2919,9 @@
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+      "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.5.tgz",
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diff --git a/package.json b/package.json
index 37d8316..225a104 100644
--- a/package.json
+++ b/package.json
@@ -41,15 +41,15 @@
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   },
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-    "@types/node": "^14.14.27",
-    "@typescript-eslint/eslint-plugin": "^4.15.0",
-    "@typescript-eslint/parser": "^4.15.0",
+    "@tensorflow/tfjs": "^3.1.0",
+    "@tensorflow/tfjs-backend-wasm": "^3.1.0",
+    "@tensorflow/tfjs-node": "^3.1.0",
+    "@tensorflow/tfjs-node-gpu": "^3.1.0",
+    "@types/node": "^14.14.28",
+    "@typescript-eslint/eslint-plugin": "^4.15.1",
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     "chokidar": "^3.5.1",
-    "esbuild": "^0.8.45",
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     "eslint": "^7.20.0",
     "eslint-config-airbnb-base": "^14.2.1",
     "eslint-plugin-import": "^2.22.1",