diff --git a/CHANGELOG.md b/CHANGELOG.md index 485d1bd..8f8634b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # @vladmandic/face-api - Version: **1.7.10** + Version: **1.7.11** Description: **FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS** Author: **Vladimir Mandic ** @@ -9,11 +9,15 @@ ## Changelog +### **1.7.11** 2023/05/08 mandic00@live.com + + +### **origin/master** 2023/03/21 mandic00@live.com + + ### **1.7.10** 2023/03/21 mandic00@live.com - -### **origin/master** 2023/01/29 mandic00@live.com - +- change typedefs ### **1.7.9** 2023/01/29 mandic00@live.com diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js index 82d4855..2c5af16 100644 --- a/dist/face-api.esm-nobundle.js +++ b/dist/face-api.esm-nobundle.js @@ -4,4 +4,4 @@ author: ' */ -var lr=Object.defineProperty;var bo=Object.getOwnPropertyDescriptor;var 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m=a(1024,256,1,"prediction_layer/conv_0"),p=a(256,512,3,"prediction_layer/conv_1"),u=a(512,128,1,"prediction_layer/conv_2"),f=a(128,256,3,"prediction_layer/conv_3"),l=a(256,128,1,"prediction_layer/conv_4"),d=a(128,256,3,"prediction_layer/conv_5"),b=a(256,64,1,"prediction_layer/conv_6"),x=a(64,128,3,"prediction_layer/conv_7"),w=r(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),h=r(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),y=r(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),v=r(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),D=r(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),N=r(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),Y=r(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),q=r(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),O=r(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),at=r(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),st=r(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),it=r(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:m,conv_1:p,conv_2:u,conv_3:f,conv_4:l,conv_5:d,conv_6:b,conv_7:x,box_predictor_0:{box_encoding_predictor:w,class_predictor:h},box_predictor_1:{box_encoding_predictor:y,class_predictor:v},box_predictor_2:{box_encoding_predictor:D,class_predictor:N},box_predictor_3:{box_encoding_predictor:Y,class_predictor:q},box_predictor_4:{box_encoding_predictor:O,class_predictor:at},box_predictor_5:{box_encoding_predictor:st,class_predictor:it}}}return{extractMobilenetV1Params:i,extractPredictionLayerParams:c}}function 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u=`mobilenetv1/conv_${p}`,f=`MobilenetV1/Conv2d_${p}_depthwise`,l=`${u}/depthwise_conv`,d=`${u}/pointwise_conv`,b=e(`${f}/depthwise_weights`,4,`${l}/filters`),x=e(`${f}/BatchNorm/gamma`,1,`${l}/batch_norm_scale`),w=e(`${f}/BatchNorm/beta`,1,`${l}/batch_norm_offset`),h=e(`${f}/BatchNorm/moving_mean`,1,`${l}/batch_norm_mean`),y=e(`${f}/BatchNorm/moving_variance`,1,`${l}/batch_norm_variance`);return{depthwise_conv:{filters:b,batch_norm_scale:x,batch_norm_offset:w,batch_norm_mean:h,batch_norm_variance:y},pointwise_conv:r("MobilenetV1",p,d)}}function s(){return{conv_0:r("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:a(1),conv_2:a(2),conv_3:a(3),conv_4:a(4),conv_5:a(5),conv_6:a(6),conv_7:a(7),conv_8:a(8),conv_9:a(9),conv_10:a(10),conv_11:a(11),conv_12:a(12),conv_13:a(13)}}function i(p,u){let f=e(`${p}/weights`,4,`${u}/filters`),l=e(`${p}/biases`,1,`${u}/bias`);return{filters:f,bias:l}}function c(p){let u=i(`Prediction/BoxPredictor_${p}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${p}/box_encoding_predictor`),f=i(`Prediction/BoxPredictor_${p}/ClassPredictor`,`prediction_layer/box_predictor_${p}/class_predictor`);return{box_encoding_predictor:u,class_predictor:f}}function m(){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:c(0),box_predictor_1:c(1),box_predictor_2:c(2),box_predictor_3:c(3),box_predictor_4:c(4),box_predictor_5:c(5)}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:m}}function qr(o){let t=[],{extractMobilenetV1Params:e,extractPredictionLayerParams:r}=Vo(o,t),a=o["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!K(a))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${a}`);let s={mobilenetv1:e(),prediction_layer:r(),output_layer:{extra_dim:a}};return L(o,t),{params:s,paramMappings:t}}function H(o,t,e){return n.tidy(()=>{let r=n.conv2d(o,t.filters,e,"same");return r=n.add(r,t.batch_norm_offset),n.clipByValue(r,0,6)})}var Yo=.0010000000474974513;function Go(o,t,e){return n.tidy(()=>{let r=n.depthwiseConv2d(o,t.filters,e,"same");return r=n.batchNorm(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,Yo),n.clipByValue(r,0,6)})}function jo(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function Zr(o,t){return n.tidy(()=>{let e,r=H(o,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 c=i+1,m=jo(c);r=Go(r,s.depthwise_conv,m),r=H(r,s.pointwise_conv,[1,1]),c===11&&(e=r)}),e===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:r,conv11:e}})}function Uo(o,t,e){let r=o.arraySync(),a=Math.min(r[t][0],r[t][2]),s=Math.min(r[t][1],r[t][3]),i=Math.max(r[t][0],r[t][2]),c=Math.max(r[t][1],r[t][3]),m=Math.min(r[e][0],r[e][2]),p=Math.min(r[e][1],r[e][3]),u=Math.max(r[e][0],r[e][2]),f=Math.max(r[e][1],r[e][3]),l=(i-a)*(c-s),d=(u-m)*(f-p);if(l<=0||d<=0)return 0;let b=Math.max(a,m),x=Math.max(s,p),w=Math.min(i,u),h=Math.min(c,f),y=Math.max(w-b,0)*Math.max(h-x,0);return y/(l+d-y)}function Kr(o,t,e,r,a){let s=o.shape[0],i=Math.min(e,s),c=t.map((u,f)=>({score:u,boxIndex:f})).filter(u=>u.score>a).sort((u,f)=>f.score-u.score),m=u=>u<=r?1:0,p=[];return c.forEach(u=>{if(p.length>=i)return;let f=u.score;for(let l=p.length-1;l>=0;--l){let d=Uo(o,u.boxIndex,p[l]);if(d!==0&&(u.score*=m(d),u.score<=a))break}f===u.score&&p.push(u.boxIndex)}),p}function Xo(o){let t=n.unstack(n.transpose(o,[1,0])),e=[n.sub(t[2],t[0]),n.sub(t[3],t[1])],r=[n.add(t[0],n.div(e[0],2)),n.add(t[1],n.div(e[1],2))];return{sizes:e,centers:r}}function Jo(o,t){let{sizes:e,centers:r}=Xo(o),a=n.unstack(n.transpose(t,[1,0])),s=n.div(n.mul(n.exp(n.div(a[2],5)),e[0]),2),i=n.add(n.mul(n.div(a[0],10),e[0]),r[0]),c=n.div(n.mul(n.exp(n.div(a[3],5)),e[1]),2),m=n.add(n.mul(n.div(a[1],10),e[1]),r[1]);return n.transpose(n.stack([n.sub(i,s),n.sub(m,c),n.add(i,s),n.add(m,c)]),[1,0])}function Qr(o,t,e){return n.tidy(()=>{let r=o.shape[0],a=Jo(n.reshape(n.tile(e.extra_dim,[r,1,1]),[-1,4]),n.reshape(o,[-1,4]));a=n.reshape(a,[r,a.shape[0]/r,4]);let s=n.sigmoid(n.slice(t,[0,0,1],[-1,-1,-1])),i=n.slice(s,[0,0,0],[-1,-1,1]);i=n.reshape(i,[r,i.shape[1]]);let c=n.unstack(a),m=n.unstack(i);return{boxes:c,scores:m}})}function Tt(o,t){return n.tidy(()=>{let e=o.shape[0],r=n.reshape(_t(o,t.box_encoding_predictor),[e,-1,1,4]),a=n.reshape(_t(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:a}})}function to(o,t,e){return n.tidy(()=>{let r=H(o,e.conv_0,[1,1]),a=H(r,e.conv_1,[2,2]),s=H(a,e.conv_2,[1,1]),i=H(s,e.conv_3,[2,2]),c=H(i,e.conv_4,[1,1]),m=H(c,e.conv_5,[2,2]),p=H(m,e.conv_6,[1,1]),u=H(p,e.conv_7,[2,2]),f=Tt(t,e.box_predictor_0),l=Tt(o,e.box_predictor_1),d=Tt(a,e.box_predictor_2),b=Tt(i,e.box_predictor_3),x=Tt(m,e.box_predictor_4),w=Tt(u,e.box_predictor_5),h=n.concat([f.boxPredictionEncoding,l.boxPredictionEncoding,d.boxPredictionEncoding,b.boxPredictionEncoding,x.boxPredictionEncoding,w.boxPredictionEncoding],1),y=n.concat([f.classPrediction,l.classPrediction,d.classPrediction,b.classPrediction,x.classPrediction,w.classPrediction],1);return{boxPredictions:h,classPredictions:y}})}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 wt=class extends I{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return n.tidy(()=>{let r=n.cast(t.toBatchTensor(512,!1),"float32"),a=n.sub(n.div(r,127.5),1),s=Zr(a,e.mobilenetv1),{boxPredictions:i,classPredictions:c}=to(s.out,s.conv11,e.prediction_layer);return Qr(i,c,e.output_layer)})}async forward(t){return this.forwardInput(await M(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:a}=new z(e),s=await M(t),{boxes:i,scores:c}=this.forwardInput(s),m=i[0],p=c[0];for(let v=1;v{let[D,N]=[Math.max(0,h[v][0]),Math.min(1,h[v][2])].map(O=>O*w),[Y,q]=[Math.max(0,h[v][1]),Math.min(1,h[v][3])].map(O=>O*x);return new E(u[v],new St(Y,D,q-Y,N-D),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return m.dispose(),p.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return qr(t)}extractParams(t){return Jr(t)}};function qo(o){let t=new wt;return t.extractWeights(o),t}function bl(o){return qo(o)}var eo=class extends wt{};var ro=.4,oo=[new g(.738768,.874946),new g(2.42204,2.65704),new g(4.30971,7.04493),new g(10.246,4.59428),new g(12.6868,11.8741)],no=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],ao=[117.001,114.697,97.404],so="tiny_yolov2_model",io="tiny_yolov2_separable_conv_model";var Se=o=>typeof o=="number";function co(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(!Se(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=>Se(t.x)&&Se(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(Se)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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mo(o,t,e,r){let{extractWeights:a,getRemainingWeights:s}=A(o),i=[],{extractConvParams:c,extractConvWithBatchNormParams:m,extractSeparableConvParams:p}=Zo(a,i),u;if(t.withSeparableConvs){let[f,l,d,b,x,w,h,y,v]=r,D=t.isFirstLayerConv2d?c(f,l,3,"conv0"):p(f,l,"conv0"),N=p(l,d,"conv1"),Y=p(d,b,"conv2"),q=p(b,x,"conv3"),O=p(x,w,"conv4"),at=p(w,h,"conv5"),st=y?p(h,y,"conv6"):void 0,it=v?p(y,v,"conv7"):void 0,gt=c(v||y||h,5*e,1,"conv8");u={conv0:D,conv1:N,conv2:Y,conv3:q,conv4:O,conv5:at,conv6:st,conv7:it,conv8:gt}}else{let[f,l,d,b,x,w,h,y,v]=r,D=m(f,l,"conv0"),N=m(l,d,"conv1"),Y=m(d,b,"conv2"),q=m(b,x,"conv3"),O=m(x,w,"conv4"),at=m(w,h,"conv5"),st=m(h,y,"conv6"),it=m(y,v,"conv7"),gt=c(v,5*e,1,"conv8");u={conv0:D,conv1:N,conv2:Y,conv3:q,conv4:O,conv5:at,conv6:st,conv7:it,conv8:gt}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function Ko(o,t){let e=B(o,t);function r(c){let m=e(`${c}/sub`,1),p=e(`${c}/truediv`,1);return{sub:m,truediv:p}}function a(c){let m=e(`${c}/filters`,4),p=e(`${c}/bias`,1);return{filters:m,bias:p}}function s(c){let m=a(`${c}/conv`),p=r(`${c}/bn`);return{conv:m,bn:p}}let i=Ht(e);return{extractConvParams:a,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function po(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}=Ko(o,e),i;if(t.withSeparableConvs){let c=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?r("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:c>7?s("conv6"):void 0,conv7:c>8?s("conv7"):void 0,conv8:r("conv8")}}else i={conv0:a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:a("conv6"),conv7:a("conv7"),conv8:r("conv8")};return L(o,e),{params:i,paramMappings:e}}var J=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 cr=class extends I{constructor(e){super("TinyYolov2");co(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let a=ot(e,r.conv0);return a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv1),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv2),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv3),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv4),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv5),a=n.maxPool(a,[2,2],[1,1],"same"),a=ot(a,r.conv6),a=ot(a,r.conv7),_t(a,r.conv8,"valid",!1)}runMobilenet(e,r){let a=this.config.isFirstLayerConv2d?Xt(_t(e,r.conv0,"valid",!1)):nt(e,r.conv0);return a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv1),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv2),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv3),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv4),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv5),a=n.maxPool(a,[2,2],[1,1],"same"),a=r.conv6?nt(a,r.conv6):a,a=r.conv7?nt(a,r.conv7):a,_t(a,r.conv8,"valid",!1)}forwardInput(e,r){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return n.tidy(()=>{let s=n.cast(e.toBatchTensor(r,!1),"float32");return s=this.config.meanRgb?X(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,a):this.runTinyYolov2(s,a)})}async forward(e,r){return this.forwardInput(await M(e),r)}async detect(e,r={}){let{inputSize:a,scoreThreshold:s}=new J(r),i=await M(e),c=await this.forwardInput(i,a),m=n.tidy(()=>n.unstack(c)[0].expandDims()),p={width:i.getInputWidth(0),height:i.getInputHeight(0)},u=await this.extractBoxes(m,i.getReshapedInputDimensions(0),s);c.dispose(),m.dispose();let f=u.map(h=>h.box),l=u.map(h=>h.score),d=u.map(h=>h.classScore),b=u.map(h=>this.config.classes[h.label]);return xr(f.map(h=>h.rescale(a)),l,this.config.iouThreshold,!0).map(h=>new ct(l[h],d[h],b[h],f[h],p))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return po(e,this.config)}extractParams(e){let r=this.config.filterSizes||cr.DEFAULT_FILTER_SIZES,a=r?r.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 mo(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,a){let{width:s,height:i}=r,c=Math.max(s,i),m=c/s,p=c/i,u=e.shape[1],f=this.config.anchors.length,[l,d,b]=n.tidy(()=>{let y=e.reshape([u,u,f,this.boxEncodingSize]),v=y.slice([0,0,0,0],[u,u,f,4]),D=y.slice([0,0,0,4],[u,u,f,1]),N=this.withClassScores?n.softmax(y.slice([0,0,0,5],[u,u,f,this.config.classes.length]),3):n.scalar(0);return[v,D,N]}),x=[],w=await d.array(),h=await l.array();for(let y=0;ya){let Y=(v+fe(h[y][v][D][0]))/u*m,q=(y+fe(h[y][v][D][1]))/u*p,O=Math.exp(h[y][v][D][2])*this.config.anchors[D].x/u*m,at=Math.exp(h[y][v][D][3])*this.config.anchors[D].y/u*p,st=Y-O/2,it=q-at/2,gt={row:y,col:v,anchor:D},{classScore:pr,label:ur}=this.withClassScores?await this.extractPredictedClass(b,gt):{classScore:1,label:0};x.push({box:new Nt(st,it,st+O,it+at),score:N,classScore:N*pr,label:ur,...gt})}}return l.dispose(),d.dispose(),b.dispose(),x}async extractPredictedClass(e,r){let{row:a,col:s,anchor:i}=r,c=await e.array();return Array(this.config.classes.length).fill(0).map((m,p)=>c[a][s][i][p]).map((m,p)=>({classScore:m,label:p})).reduce((m,p)=>m.classScore>p.classScore?m:p)}},Pt=cr;Pt.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Jt=class extends Pt{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:ro,classes:["face"],...t?{anchors:no,meanRgb:ao}:{anchors:oo,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(a=>new E(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?io:so}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function id(o,t=!0){let e=new Jt(t);return e.extractWeights(o),e}var Le=class extends J{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var V=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function Ft(o,t,e,r,a=({alignedRect:s})=>s){let s=o.map(m=>Yt(m)?a(m):m.detection),i=r||(t instanceof n.Tensor?await oe(t,s):await re(t,s)),c=await e(i);return i.forEach(m=>m instanceof n.Tensor&&m.dispose()),c}async function qt(o,t,e,r,a){return Ft([o],t,async s=>e(s[0]),r,a)}var uo=.4,fo=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],lo=[117.001,114.697,97.404];var Zt=class extends Pt{constructor(){let t={withSeparableConvs:!0,iouThreshold:uo,classes:["face"],anchors:fo,meanRgb:lo,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(a=>new E(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var T={ssdMobilenetv1:new wt,tinyFaceDetector:new Zt,tinyYolov2:new Jt,faceLandmark68Net:new jt,faceLandmark68TinyNet:new Ie,faceRecognitionNet:new Ut,faceExpressionNet:new Pe,ageGenderNet:new Me},Qo=(o,t)=>T.ssdMobilenetv1.locateFaces(o,t),Bd=(o,t)=>T.tinyFaceDetector.locateFaces(o,t),Rd=(o,t)=>T.tinyYolov2.locateFaces(o,t),tn=o=>T.faceLandmark68Net.detectLandmarks(o),$d=o=>T.faceLandmark68TinyNet.detectLandmarks(o),Od=o=>T.faceRecognitionNet.computeFaceDescriptor(o),Hd=o=>T.faceExpressionNet.predictExpressions(o),zd=o=>T.ageGenderNet.predictAgeAndGender(o),en=o=>T.ssdMobilenetv1.load(o),Vd=o=>T.tinyFaceDetector.load(o),Yd=o=>T.tinyYolov2.load(o),Gd=o=>T.faceLandmark68Net.load(o),jd=o=>T.faceLandmark68TinyNet.load(o),Ud=o=>T.faceRecognitionNet.load(o),Xd=o=>T.faceExpressionNet.load(o),Jd=o=>T.ageGenderNet.load(o),qd=en,Zd=Qo,Kd=tn;var Ae=class extends V{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Dt=class extends Ae{async run(){let t=await this.parentTask,e=await Ft(t,this.input,async r=>Promise.all(r.map(a=>T.faceExpressionNet.predictExpressions(a))),this.extractedFaces);return t.map((r,a)=>tr(r,e[a]))}withAgeAndGender(){return new Mt(this,this.input)}},Et=class extends Ae{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>T.faceExpressionNet.predictExpressions(r),this.extractedFaces);return tr(t,e)}withAgeAndGender(){return new Ct(this,this.input)}},ut=class extends Dt{withAgeAndGender(){return new lt(this,this.input)}withFaceDescriptors(){return new ht(this,this.input)}},ft=class extends Et{withAgeAndGender(){return new dt(this,this.input)}withFaceDescriptor(){return new bt(this,this.input)}};var We=class extends V{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Mt=class extends We{async run(){let t=await this.parentTask,e=await Ft(t,this.input,async r=>Promise.all(r.map(a=>T.ageGenderNet.predictAgeAndGender(a))),this.extractedFaces);return t.map((r,a)=>{let{age:s,gender:i,genderProbability:c}=e[a];return sr(ir(r,i,c),s)})}withFaceExpressions(){return new Dt(this,this.input)}},Ct=class extends We{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:a}=await qt(t,this.input,s=>T.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return sr(ir(t,r,a),e)}withFaceExpressions(){return new Et(this,this.input)}},lt=class extends Mt{withFaceExpressions(){return new ut(this,this.input)}withFaceDescriptors(){return new ht(this,this.input)}},dt=class extends Ct{withFaceExpressions(){return new ft(this,this.input)}withFaceDescriptor(){return new bt(this,this.input)}};var ke=class extends V{constructor(e,r){super();this.parentTask=e;this.input=r}},ht=class extends ke{async run(){let t=await this.parentTask;return(await Ft(t,this.input,r=>Promise.all(r.map(a=>T.faceRecognitionNet.computeFaceDescriptor(a))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,a)=>ar(t[a],r))}withFaceExpressions(){return new ut(this,this.input)}withAgeAndGender(){return new lt(this,this.input)}},bt=class extends ke{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>T.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return ar(t,e)}withFaceExpressions(){return new ft(this,this.input)}withAgeAndGender(){return new dt(this,this.input)}};var Be=class extends V{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?T.faceLandmark68TinyNet:T.faceLandmark68Net}},Re=class extends Be{async run(){let t=await this.parentTask,e=t.map(i=>i.detection),r=this.input instanceof n.Tensor?await oe(this.input,e):await re(this.input,e),a=await Promise.all(r.map(i=>this.landmarkNet.detectLandmarks(i)));return r.forEach(i=>i instanceof n.Tensor&&i.dispose()),t.filter((i,c)=>a[c]).map((i,c)=>ie(i,a[c]))}withFaceExpressions(){return new ut(this,this.input)}withAgeAndGender(){return new lt(this,this.input)}withFaceDescriptors(){return new ht(this,this.input)}},$e=class extends Be{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof n.Tensor?await oe(this.input,[e]):await re(this.input,[e]),a=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof n.Tensor&&s.dispose()),ie(t,a)}withFaceExpressions(){return new ft(this,this.input)}withAgeAndGender(){return new dt(this,this.input)}withFaceDescriptor(){return new bt(this,this.input)}};var Oe=class extends V{constructor(e,r=new z){super();this.input=e;this.options=r}},me=class extends Oe{async run(){let{input:t,options:e}=this,r;if(e instanceof Le)r=T.tinyFaceDetector.locateFaces(t,e);else if(e instanceof z)r=T.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof J)r=T.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(a=>At({},a)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new Re(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Dt(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Mt(this.runAndExtendWithFaceDetections(),this.input)}},He=class extends Oe{async run(){let t=await new me(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?At({},e):void 0)})}withFaceLandmarks(t=!1){return new $e(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Et(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Ct(this.runAndExtendWithFaceDetection(),this.input)}};function Jh(o,t=new z){return new He(o,t)}function mr(o,t=new z){return new me(o,t)}async function rn(o,t){return mr(o,new z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function eb(o,t={}){return mr(o,new J(t)).withFaceLandmarks().withFaceDescriptors()}var rb=rn;function ho(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((a,s)=>a-r[s]).reduce((a,s)=>a+s*s,0))}var ze=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 a=1,s=()=>`person ${a++}`;this._labeledDescriptors=r.map(i=>{if(i instanceof Q)return i;if(i instanceof Float32Array)return new Q(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Q(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>ho(r,t)).reduce((r,a)=>r+a,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Kt(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Q.fromJSON(r));return new ze(e,t.distanceThreshold)}};function yb(o){let t=new Zt;return t.extractWeights(o),t}function on(o,t){let{width:e,height:r}=new S(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(a=>on(a,{width:e,height:r}));if(Yt(o)){let a=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(a.box.width,a.box.height);return ie(At(o,a),s)}return tt(o)?At(o,o.detection.forSize(e,r)):o instanceof $||o instanceof E?o.forSize(e,r):o}var Nb=kr;export{Me as AgeGenderNet,Nt as BoundingBox,F as Box,V as ComposableTask,ht as ComputeAllFaceDescriptorsTask,ke as ComputeFaceDescriptorsTaskBase,bt as ComputeSingleFaceDescriptorTask,Re as DetectAllFaceLandmarksTask,me as DetectAllFacesTask,Be as DetectFaceLandmarksTaskBase,Oe as DetectFacesTaskBase,$e as DetectSingleFaceLandmarksTask,He as DetectSingleFaceTask,S as Dimensions,Lr as FACE_EXPRESSION_LABELS,E as FaceDetection,eo as FaceDetectionNet,Pe as FaceExpressionNet,pt as FaceExpressions,jt as FaceLandmark68Net,Ie as FaceLandmark68TinyNet,Yr as FaceLandmarkNet,$ as FaceLandmarks,yr as FaceLandmarks5,Lt as FaceLandmarks68,Kt as FaceMatch,ze as FaceMatcher,Ut as FaceRecognitionNet,rr as Gender,Qt as LabeledBox,Q as LabeledFaceDescriptors,rt as NetInput,I as NeuralNetwork,ct as ObjectDetection,g as Point,_r as PredictedBox,St as Rect,wt as SsdMobilenetv1,z as SsdMobilenetv1Options,Zt as TinyFaceDetector,Le as TinyFaceDetectorOptions,Jt as TinyYolov2,J as TinyYolov2Options,rb as allFaces,rn as allFacesSsdMobilenetv1,eb as allFacesTinyYolov2,Tr as awaitMediaLoaded,wr as bufferToImage,Od as computeFaceDescriptor,Rt as createCanvas,be as createCanvasFromMedia,bl as createFaceDetectionNet,bf as createFaceRecognitionNet,qo as createSsdMobilenetv1,yb as createTinyFaceDetector,id as createTinyYolov2,mr as detectAllFaces,tn as detectFaceLandmarks,$d as detectFaceLandmarksTiny,Kd as detectLandmarks,Jh as detectSingleFace,Wr as draw,_ as env,ho as euclideanDistance,sr as extendWithAge,ar as extendWithFaceDescriptor,At as extendWithFaceDetection,tr as extendWithFaceExpressions,ie as extendWithFaceLandmarks,ir as extendWithGender,oe as extractFaceTensors,re as extractFaces,Fi as fetchImage,Dr as fetchJson,Ii as fetchNetWeights,mt as fetchOrThrow,ki as fetchVideo,k as getContext2dOrThrow,Bt as getMediaDimensions,Pr as imageTensorToCanvas,Fr as imageToSquare,On as inverseSigmoid,br as iou,Qe as isMediaElement,he as isMediaLoaded,yf as isWithAge,tt as isWithFaceDetection,Ar as isWithFaceExpressions,Yt as isWithFaceLandmarks,Pf as isWithGender,Jd as loadAgeGenderModel,qd as loadFaceDetectionModel,Xd as loadFaceExpressionModel,Gd as loadFaceLandmarkModel,jd as loadFaceLandmarkTinyModel,Ud as loadFaceRecognitionModel,en as loadSsdMobilenetv1Model,Vd as loadTinyFaceDetectorModel,Yd as loadTinyYolov2Model,Mr as loadWeightMap,Zd as locateFaces,Vi as matchDimensions,gr as minBbox,T as nets,xr as nonMaxSuppression,X as normalize,vr as padToSquare,zd as predictAgeAndGender,Hd as recognizeFaceExpressions,on as resizeResults,Wt as resolveInput,Rn as shuffleArray,fe as sigmoid,Qo as ssdMobilenetv1,n as tf,Bd as tinyFaceDetector,Rd as tinyYolov2,M as toNetInput,hr as utils,co as validateConfig,Nb as version}; +var lr=Object.defineProperty;var bo=Object.getOwnPropertyDescriptor;var go=Object.getOwnPropertyNames;var xo=Object.prototype.hasOwnProperty;var vo=(o=>typeof require!="undefined"?require:typeof Proxy!="undefined"?new Proxy(o,{get:(t,e)=>(typeof 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Wr={};Ve(Wr,{AnchorPosition:()=>Ke,DrawBox:()=>ee,DrawBoxOptions:()=>de,DrawFaceLandmarks:()=>De,DrawFaceLandmarksOptions:()=>Fe,DrawTextField:()=>et,DrawTextFieldOptions:()=>kt,drawContour:()=>Z,drawDetections:()=>No,drawFaceExpressions:()=>So,drawFaceLandmarks:()=>Ao});function Z(o,t,e=!1){if(o.beginPath(),t.slice(1).forEach(({x:r,y:a},s)=>{let i=t[s];o.moveTo(i.x,i.y),o.lineTo(r,a)}),e){let r=t[t.length-1],a=t[0];if(!r||!a)return;o.moveTo(r.x,r.y),o.lineTo(a.x,a.y)}o.stroke()}var hr={};Ve(hr,{computeReshapedDimensions:()=>je,getCenterPoint:()=>yt,isDimensions:()=>ue,isEven:()=>pe,isFloat:()=>Ge,isTensor:()=>xt,isTensor1D:()=>Fo,isTensor2D:()=>Ye,isTensor3D:()=>K,isTensor4D:()=>R,isValidNumber:()=>G,isValidProbablitiy:()=>It,range:()=>U,round:()=>vt});var S=class{constructor(t,e){if(!G(t)||!G(e))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width:t,height:e})}`);this._width=t,this._height=e}get width(){return this._width}get height(){return this._height}reverse(){return new S(1/this.width,1/this.height)}};function xt(o,t){return o instanceof n.Tensor&&o.shape.length===t}function Fo(o){return xt(o,1)}function Ye(o){return xt(o,2)}function K(o){return xt(o,3)}function R(o){return xt(o,4)}function Ge(o){return o%1!==0}function pe(o){return o%2===0}function vt(o,t=2){let e=10**t;return Math.floor(o*e)/e}function ue(o){return o&&o.width&&o.height}function je({width:o,height:t},e){let r=e/Math.max(t,o);return new S(Math.round(o*r),Math.round(t*r))}function yt(o){return o.reduce((t,e)=>t.add(e),new g(0,0)).div(new g(o.length,o.length))}function U(o,t,e){return Array(o).fill(0).map((r,a)=>t+a*e)}function G(o){return!!o&&o!==1/0&&o!==-1/0&&!Number.isNaN(o)||o===0}function It(o){return G(o)&&o>=0&&o<=1}var g=class{constructor(t,e){this._x=t,this._y=e}get x(){return this._x}get y(){return this._y}add(t){return new g(this.x+t.x,this.y+t.y)}sub(t){return new g(this.x-t.x,this.y-t.y)}mul(t){return new 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${JSON.stringify(r)}`);let[i,c,m,p]=s?[r.x,r.y,r.width,r.height]:[r.left,r.top,r.right-r.left,r.bottom-r.top];F.assertIsValidBox({x:i,y:c,width:m,height:p},"Box.constructor",e),this._x=i,this._y=c,this._width=m,this._height=p}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new g(this.left,this.top)}get topRight(){return new g(this.right,this.top)}get bottomLeft(){return new g(this.left,this.bottom)}get bottomRight(){return new g(this.right,this.bottom)}round(){let[t,e,r,a]=[this.x,this.y,this.width,this.height].map(s=>Math.round(s));return new F({x:t,y:e,width:r,height:a})}floor(){let[t,e,r,a]=[this.x,this.y,this.width,this.height].map(s=>Math.floor(s));return new F({x:t,y:e,width:r,height:a})}toSquare(){let{x:t,y:e,width:r,height:a}=this,s=Math.abs(r-a);return re&&(c=-f+e+r,f=e),l>t&&(m=-l+t+a,l=t),p<1&&(m=2-p,p=1),u<1&&(m=2-u,u=1),{dy:i,edy:m,dx:s,edx:c,y:u,ey:l,x:p,ex:f,w:r,h:a}}calibrate(t){return new F({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 Nt=class extends F{constructor(t,e,r,a,s=!1){super({left:t,top:e,right:r,bottom:a},s)}};var ct=class{constructor(t,e,r,a,s){this._imageDims=new S(s.width,s.height),this._score=t,this._classScore=e,this._className=r,this._box=new F(a).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 F(this._box).rescale(this.imageDims.reverse())}forSize(t,e){return new ct(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:e})}};var E=class extends ct{constructor(t,e,r){super(t,t,"",e,r)}forSize(t,e){let{score:r,relativeBox:a,imageDims:s}=super.forSize(t,e);return new E(r,a,s)}};function br(o,t,e=!0){let r=Math.max(0,Math.min(o.right,t.right)-Math.max(o.left,t.left)),a=Math.max(0,Math.min(o.bottom,t.bottom)-Math.max(o.top,t.top)),s=r*a;return e?s/(o.area+t.area-s):s/Math.min(o.area,t.area)}function gr(o){let t=o.map(c=>c.x),e=o.map(c=>c.y),r=t.reduce((c,m)=>mmcc({score:i,boxIndex:c})).sort((i,c)=>i.score-c.score).map(i=>i.boxIndex),s=[];for(;a.length>0;){let i=a.pop();s.push(i);let c=a,m=[];for(let p=0;pm[u]<=e)}return s}function X(o,t){return n.tidy(()=>{let[e,r,a]=t,s=n.fill([...o.shape.slice(0,3),1],e,"float32"),i=n.fill([...o.shape.slice(0,3),1],r,"float32"),c=n.fill([...o.shape.slice(0,3),1],a,"float32"),m=n.concat([s,i,c],3);return n.sub(o,m)})}function vr(o,t=!1){return n.tidy(()=>{let[e,r]=o.shape.slice(1);if(e===r)return o;let a=Math.abs(e-r),s=Math.round(a*(t?.5:1)),i=e>r?2:1,c=l=>{let d=o.shape.slice();return d[i]=l,n.fill(d,0,"float32")},m=c(s),p=a-m.shape[i],f=[t&&p?c(p):null,o,m].filter(l=>!!l).map(l=>n.cast(l,"float32"));return n.concat(f,i)})}function Rn(o){let t=o.slice();for(let e=t.length-1;e>0;e--){let r=Math.floor(Math.random()*(e+1)),a=t[e];t[e]=t[r],t[r]=a}return t}function fe(o){return 1/(1+Math.exp(-o))}function On(o){return Math.log(o/(1-o))}var St=class extends F{constructor(t,e,r,a,s=!1){super({x:t,y:e,width:r,height:a},s)}};var Do=.5,Eo=.43,Mo=.45,$=class{constructor(t,e,r=new g(0,0)){let{width:a,height:s}=e;this._imgDims=new S(a,s),this._shift=r,this._positions=t.map(i=>i.mul(new g(a,s)).add(r))}get shift(){return new g(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new g(this.imageWidth,this.imageHeight)))}forSize(t,e){return new this.constructor(this.relativePositions,{width:t,height:e})}shiftBy(t,e){return new this.constructor(this.relativePositions,this._imgDims,new g(t,e))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,e={}){if(t){let s=t instanceof E?t.box.floor():new F(t);return this.shiftBy(s.x,s.y).align(null,e)}let{useDlibAlignment:r,minBoxPadding:a}={useDlibAlignment:!1,minBoxPadding:.2,...e};return r?this.alignDlib():this.alignMinBbox(a)}alignDlib(){let t=this.getRefPointsForAlignment(),[e,r,a]=t,s=f=>a.sub(f).magnitude(),i=(s(e)+s(r))/2,c=Math.floor(i/Mo),m=yt(t),p=Math.floor(Math.max(0,m.x-Do*c)),u=Math.floor(Math.max(0,m.y-Eo*c));return new St(p,u,Math.min(c,this.imageWidth+p),Math.min(c,this.imageHeight+u))}alignMinBbox(t){let e=gr(this.positions);return e.pad(e.width*t,e.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var yr=class extends ${getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],yt([t[3],t[4]])]}};var Lt=class extends ${getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(yt)}};var Kt=class{constructor(t,e){this._label=t,this._distance=e}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${vt(this.distance)})`:""}`}};var Qt=class extends F{constructor(e,r){super(e);this._label=r}static assertIsValidLabeledBox(e,r){if(F.assertIsValidBox(e,r),!G(e.label))throw new Error(`${r} - expected property label (${e.label}) to be a number`)}get label(){return this._label}};var Q=class{constructor(t,e){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(e)||e.some(r=>!(r instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=e}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let e=t.descriptors.map(r=>new Float32Array(r));return new Q(t.label,e)}};var _r=class extends Qt{constructor(e,r,a,s){super(e,r);this._score=a,this._classScore=s}static assertIsValidPredictedBox(e,r){if(Qt.assertIsValidLabeledBox(e,r),!It(e.score)||!It(e.classScore))throw new Error(`${r} - expected properties score (${e.score}) and (${e.classScore}) to be a number between [0, 1]`)}get score(){return this._score}get classScore(){return this._classScore}};function tt(o){return o.detection instanceof E}function At(o,t){return{...o,...{detection:t}}}function Ue(){let o=window.fetch;if(!o)throw new Error("fetch - missing fetch implementation for browser environment");return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),createVideoElement:()=>document.createElement("video"),fetch:o,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function te(){return typeof global=="object"&&typeof process!="undefined"&&process.versions!=null&&process.versions.node!=null}function le(o){let t="";if(!o&&te())try{o=vo("fs")}catch(r){t=r.toString()}return{readFile:o?r=>new Promise((a,s)=>{o.readFile(r,(i,c)=>i?s(i):a(c))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: 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ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var C;function Co(){if(!C)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return C}function qe(o){C=o}function Ze(){return Je()?qe(Ue()):te()?qe(Xe()):null}function Io(o){if(C||Ze(),!C)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=C.Canvas,Image:e=C.Image}=o;C.Canvas=t,C.Image=e,C.createCanvasElement=o.createCanvasElement||(()=>new t),C.createImageElement=o.createImageElement||(()=>new e),C.ImageData=o.ImageData||C.ImageData,C.Video=o.Video||C.Video,C.fetch=o.fetch||C.fetch,C.readFile=o.readFile||C.readFile}var _={getEnv:Co,setEnv:qe,initialize:Ze,createBrowserEnv:Ue,createFileSystem:le,createNodejsEnv:Xe,monkeyPatch:Io,isBrowser:Je,isNodejs:te};Ze();function Wt(o){return!_.isNodejs()&&typeof o=="string"?document.getElementById(o):o}function k(o){let{Canvas:t,CanvasRenderingContext2D:e}=_.getEnv();if(o instanceof 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de=class{constructor(t={}){let{boxColor:e,lineWidth:r,label:a,drawLabelOptions:s}=t;this.boxColor=e||"rgba(0, 0, 255, 1)",this.lineWidth=r||2,this.label=a;let i={anchorPosition:"BOTTOM_LEFT",backgroundColor:this.boxColor};this.drawLabelOptions=new kt({...i,...s})}},ee=class{constructor(t,e={}){this.box=new F(t),this.options=new de(e)}draw(t){let e=k(t),{boxColor:r,lineWidth:a}=this.options,{x:s,y:i,width:c,height:m}=this.box;e.strokeStyle=r,e.lineWidth=a,e.strokeRect(s,i,c,m);let{label:p}=this.options;p&&new et([p],{x:s-a/2,y:i},this.options.drawLabelOptions).draw(t)}};function No(o,t){(Array.isArray(t)?t:[t]).forEach(r=>{let a=r instanceof E?r.score:tt(r)?r.detection.score:void 0,s=r instanceof E?r.box:tt(r)?r.detection.box:new F(r),i=a?`${vt(a)}`:void 0;new ee(s,{label:i}).draw(o)})}function he(o){let{Image:t,Video:e}=_.getEnv();return o instanceof t&&o.complete||o instanceof e&&o.readyState>=3}function Tr(o){return new Promise((t,e)=>{(o instanceof _.getEnv().Canvas||he(o))&&t(null);function r(s){s.currentTarget&&(s.currentTarget.removeEventListener("load",a),s.currentTarget.removeEventListener("error",r),e(s))}function a(s){s.currentTarget&&(s.currentTarget.removeEventListener("load",a),s.currentTarget.removeEventListener("error",r),t(s))}o.addEventListener("load",a),o.addEventListener("error",r)})}function wr(o){return new Promise((t,e)=>{o instanceof Blob||e(new Error("bufferToImage - expected buf to be of type: Blob"));let r=new FileReader;r.onload=()=>{typeof r.result!="string"&&e(new Error("bufferToImage - expected reader.result to be a string, in onload"));let a=_.getEnv().createImageElement();a.onload=()=>t(a),a.onerror=e,a.src=r.result},r.onerror=e,r.readAsDataURL(o)})}function Bt(o){let{Image:t,Video:e}=_.getEnv();return o instanceof t?new S(o.naturalWidth,o.naturalHeight):o instanceof e?new S(o.videoWidth,o.videoHeight):new S(o.width,o.height)}function Rt({width:o,height:t}){let{createCanvasElement:e}=_.getEnv(),r=e();return r.width=o,r.height=t,r}function be(o,t){let{ImageData:e}=_.getEnv();if(!(o instanceof e)&&!he(o))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:r,height:a}=t||Bt(o),s=Rt({width:r,height:a});return o instanceof e?k(s).putImageData(o,0,0):k(s).drawImage(o,0,0,r,a),s}async function Pr(o,t){let e=t||_.getEnv().createCanvasElement(),[r,a,s]=o.shape.slice(R(o)?1:0),i=n.tidy(()=>o.as3D(r,a,s).toInt());return await n.browser.toPixels(i,e),i.dispose(),e}function Qe(o){let{Image:t,Canvas:e,Video:r}=_.getEnv();return o instanceof t||o instanceof e||o instanceof r}function Fr(o,t,e=!1){let{Image:r,Canvas:a}=_.getEnv();if(!(o instanceof r||o instanceof a))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return Rt({width:1,height:1});let s=Bt(o),i=t/Math.max(s.height,s.width),c=i*s.width,m=i*s.height,p=Rt({width:t,height:t}),u=o instanceof a?o:be(o),f=Math.abs(c-m)/2,l=e&&c0&&u.height>0&&k(p).drawImage(u,l,d,c,m),p}var rt=class{constructor(t,e=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=e,this._batchSize=t.length,t.forEach((r,a)=>{if(K(r)){this._imageTensors[a]=r,this._inputDimensions[a]=r.shape;return}if(R(r)){let i=r.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[a]=r,this._inputDimensions[a]=r.shape.slice(1);return}let s=r instanceof _.getEnv().Canvas?r:be(r);this._canvases[a]=s,this._inputDimensions[a]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return U(this.batchSize,0,1).map((t,e)=>this.getReshapedInputDimensions(e))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let e=this.getInputWidth(t),r=this.getInputHeight(t);return je({width:e,height:r},this.inputSize)}toBatchTensor(t,e=!0){return this._inputSize=t,n.tidy(()=>{let r=U(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof n.Tensor){let c=R(i)?i:n.expandDims(i);return c=vr(c,e),(c.shape[1]!==t||c.shape[2]!==t)&&(c=n.image.resizeBilinear(c,[t,t],!1,!1)),c.as3D(t,t,3)}if(i instanceof _.getEnv().Canvas)return n.browser.fromPixels(Fr(i,t,e));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return n.stack(r.map(s=>n.cast(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function M(o){if(o instanceof rt)return o;let t=Array.isArray(o)?o:[o];if(!t.length)throw new Error("toNetInput - empty array passed as input");let e=a=>Array.isArray(o)?` at input index ${a}:`:"",r=t.map(Wt);return r.forEach((a,s)=>{if(!Qe(a)&&!K(a)&&!R(a))throw typeof t[s]=="string"?new Error(`toNetInput -${e(s)} string passed, but could not resolve HTMLElement for element id ${t[s]}`):new Error(`toNetInput -${e(s)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(R(a)){let i=a.shape[0];if(i!==1)throw new Error(`toNetInput -${e(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(r.map(a=>Qe(a)&&Tr(a))),new rt(r,Array.isArray(o))}async function re(o,t){let{Canvas:e}=_.getEnv(),r=o;if(!(o instanceof e)){let i=await M(o);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let c=i.getInput(0);r=c instanceof e?c:await Pr(c)}let a=k(r);return t.map(i=>i instanceof E?i.forSize(r.width,r.height).box.floor():i).map(i=>i.clipAtImageBorders(r.width,r.height)).map(({x:i,y:c,width:m,height:p})=>{let u=Rt({width:m,height:p});return m>0&&p>0&&k(u).putImageData(a.getImageData(i,c,m,p),0,0),u})}async function oe(o,t){if(!K(o)&&!R(o))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(R(o)&&o.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return n.tidy(()=>{let[e,r,a]=o.shape.slice(R(o)?1:0);return t.map(c=>c instanceof E?c.forSize(r,e).box:c).map(c=>c.clipAtImageBorders(r,e)).filter(c=>c.width>0&&c.height>0).map(({x:c,y:m,width:p,height:u})=>n.slice3d(o.as3D(e,r,a),[m,c,0],[u,p,a]))})}async function mt(o,t){let{fetch:e}=_.getEnv(),r=await e(o,t);if(!(r.status<400))throw new Error(`failed to fetch: (${r.status}) ${r.statusText}, from url: ${r.url}`);return r}async function Fi(o){let t=await mt(o),e=await t.blob();if(!e.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${e.type}, for url: ${t.url}`);return wr(e)}async function Dr(o){return(await mt(o)).json()}async function Ii(o){return new Float32Array(await(await mt(o)).arrayBuffer())}function Er(o){return new Promise((t,e)=>{o instanceof Blob||e(new Error("bufferToVideo - expected buf to be of type: Blob"));let r=_.getEnv().createVideoElement();r.oncanplay=()=>t(r),r.onerror=e,r.playsInline=!0,r.muted=!0,r.src=URL.createObjectURL(o),r.play()})}async function ki(o){let t=await 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Me=class extends I{constructor(e=new Ee(2)){super("AgeGenderNet");this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){let{params:r}=this;if(!r)throw new Error(`${this._name} - load model before inference`);return n.tidy(()=>{let a=e instanceof rt?this.faceFeatureExtractor.forwardInput(e):e,s=n.avgPool(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),i=se(s,r.fc.age).as1D(),c=se(s,r.fc.gender);return{age:i,gender:c}})}forwardInput(e){return n.tidy(()=>{let{age:r,gender:a}=this.runNet(e);return{age:r,gender:n.softmax(a)}})}async forward(e){return this.forwardInput(await M(e))}async predictAgeAndGender(e){let r=await M(e),a=await this.forwardInput(r),s=n.unstack(a.age),i=n.unstack(a.gender),c=s.map((p,u)=>({ageTensor:p,genderTensor:i[u]})),m=await Promise.all(c.map(async({ageTensor:p,genderTensor:u})=>{let f=p.dataSync()[0],l=u.dataSync()[0],d=l>.5,b=d?"male":"female",x=d?l:1-l;return 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i=(f,l)=>n.stack([n.fill([68],f,"float32"),n.fill([68],l,"float32")],1).as2D(1,136).as1D(),c=(f,l)=>{let{width:d,height:b}=a[f];return l(d,b)?Math.abs(d-b)/2:0},m=f=>c(f,(l,d)=>lc(f,(l,d)=>di(m(l),p(l))))).div(n.stack(Array.from(Array(s),(f,l)=>i(a[l].width,a[l].height))))})}forwardInput(t){return n.tidy(()=>{let e=this.runNet(t);return this.postProcess(e,t.inputSize,t.inputDimensions.map(([r,a])=>({height:r,width:a})))})}async forward(t){return this.forwardInput(await M(t))}async detectLandmarks(t){let e=await M(t),r=n.tidy(()=>n.unstack(this.forwardInput(e))),a=await Promise.all(r.map(async(s,i)=>{let c=Array.from(s.dataSync()),m=c.filter((u,f)=>pe(f)),p=c.filter((u,f)=>!pe(f));return new Lt(Array(68).fill(0).map((u,f)=>new g(m[f],p[f])),{height:e.getInputHeight(i),width:e.getInputWidth(i)})}));return r.forEach(s=>s.dispose()),e.isBatchInput?a:a[0]}getClassifierChannelsOut(){return 136}};var jt=class extends Gt{constructor(t=new zt){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function zr(o){let t=[],{extractDenseBlock3Params:e}=Te(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2")};return L(o,t),{params:r,paramMappings:t}}function Vr(o){let t=[],{extractWeights:e,getRemainingWeights:r}=A(o),{extractDenseBlock3Params:a}=ye(e,t),s=a(3,32,"dense0",!0),i=a(32,64,"dense1"),c=a(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:c}}}var Ce=class extends I{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyFaceFeatureExtractor - load model before inference");return n.tidy(()=>{let r=n.cast(t.toBatchTensor(112,!0),"float32"),s=X(r,[122.782,117.001,104.298]).div(255),i=xe(s,e.dense0,!0);return i=xe(i,e.dense1),i=xe(i,e.dense2),i=n.avgPool(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await M(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return zr(t)}extractParams(t){return Vr(t)}};var Ie=class extends Gt{constructor(t=new Ce){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var Yr=class extends jt{};function Gr(o,t){return n.add(n.mul(o,t.weights),t.biases)}function or(o,t,e,r,a="same"){let{filters:s,bias:i}=t.conv,c=n.conv2d(o,s,e,a);return c=n.add(c,i),c=Gr(c,t.scale),r?n.relu(c):c}function jr(o,t){return or(o,t,[1,1],!0)}function nr(o,t){return or(o,t,[1,1],!1)}function Ne(o,t){return or(o,t,[2,2],!0,"valid")}function Oo(o,t){function e(c,m,p){let u=o(c),f=u.length/(m*p*p);if(Ge(f))throw new Error(`depth has to be an integer: ${f}, weights.length: ${u.length}, numFilters: ${m}, filterSize: ${p}`);return n.tidy(()=>n.transpose(n.tensor4d(u,[m,f,p,p]),[2,3,1,0]))}function r(c,m,p,u){let f=e(c,m,p),l=n.tensor1d(o(m));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/bias`}),{filters:f,bias:l}}function a(c,m){let p=n.tensor1d(o(c)),u=n.tensor1d(o(c));return t.push({paramPath:`${m}/weights`},{paramPath:`${m}/biases`}),{weights:p,biases:u}}function s(c,m,p,u){let f=r(c,m,p,`${u}/conv`),l=a(m,`${u}/scale`);return{conv:f,scale:l}}function i(c,m,p,u,f=!1){let l=s((f?.5:1)*c,m,p,`${u}/conv1`),d=s(c,m,p,`${u}/conv2`);return{conv1:l,conv2:d}}return{extractConvLayerParams:s,extractResidualLayerParams:i}}function 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r=n.cast(t.toBatchTensor(150,!0),"float32"),s=X(r,[122.782,117.001,104.298]).div(255),i=Ne(s,e.conv32_down);i=n.maxPool(i,3,2,"valid"),i=j(i,e.conv32_1),i=j(i,e.conv32_2),i=j(i,e.conv32_3),i=ce(i,e.conv64_down),i=j(i,e.conv64_1),i=j(i,e.conv64_2),i=j(i,e.conv64_3),i=ce(i,e.conv128_down),i=j(i,e.conv128_1),i=j(i,e.conv128_2),i=ce(i,e.conv256_down),i=j(i,e.conv256_1),i=j(i,e.conv256_2),i=ce(i,e.conv256_down_out);let c=i.mean([1,2]);return n.matMul(c,e.fc)})}async forward(t){return this.forwardInput(await M(t))}async computeFaceDescriptor(t){var s;if((s=t==null?void 0:t.shape)!=null&&s.some(i=>i<=0))return new Float32Array(128);let e=await M(t),r=n.tidy(()=>n.unstack(this.forwardInput(e))),a=await Promise.all(r.map(i=>i.data()));return r.forEach(i=>i.dispose()),e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return Xr(t)}extractParams(t){return Ur(t)}};function bf(o){let t=new Ut;return t.extractWeights(o),t}function 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s(m,p,u){let f=e(m,`${u}/depthwise_conv`),l=a(m,p,1,`${u}/pointwise_conv`);return{depthwise_conv:f,pointwise_conv:l}}function i(){let m=a(3,32,3,"mobilenetv1/conv_0"),p=s(32,64,"mobilenetv1/conv_1"),u=s(64,128,"mobilenetv1/conv_2"),f=s(128,128,"mobilenetv1/conv_3"),l=s(128,256,"mobilenetv1/conv_4"),d=s(256,256,"mobilenetv1/conv_5"),b=s(256,512,"mobilenetv1/conv_6"),x=s(512,512,"mobilenetv1/conv_7"),w=s(512,512,"mobilenetv1/conv_8"),h=s(512,512,"mobilenetv1/conv_9"),y=s(512,512,"mobilenetv1/conv_10"),v=s(512,512,"mobilenetv1/conv_11"),D=s(512,1024,"mobilenetv1/conv_12"),N=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:m,conv_1:p,conv_2:u,conv_3:f,conv_4:l,conv_5:d,conv_6:b,conv_7:x,conv_8:w,conv_9:h,conv_10:y,conv_11:v,conv_12:D,conv_13:N}}function c(){let 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f=u.score;for(let l=p.length-1;l>=0;--l){let d=Uo(o,u.boxIndex,p[l]);if(d!==0&&(u.score*=m(d),u.score<=a))break}f===u.score&&p.push(u.boxIndex)}),p}function Xo(o){let t=n.unstack(n.transpose(o,[1,0])),e=[n.sub(t[2],t[0]),n.sub(t[3],t[1])],r=[n.add(t[0],n.div(e[0],2)),n.add(t[1],n.div(e[1],2))];return{sizes:e,centers:r}}function Jo(o,t){let{sizes:e,centers:r}=Xo(o),a=n.unstack(n.transpose(t,[1,0])),s=n.div(n.mul(n.exp(n.div(a[2],5)),e[0]),2),i=n.add(n.mul(n.div(a[0],10),e[0]),r[0]),c=n.div(n.mul(n.exp(n.div(a[3],5)),e[1]),2),m=n.add(n.mul(n.div(a[1],10),e[1]),r[1]);return n.transpose(n.stack([n.sub(i,s),n.sub(m,c),n.add(i,s),n.add(m,c)]),[1,0])}function Qr(o,t,e){return n.tidy(()=>{let r=o.shape[0],a=Jo(n.reshape(n.tile(e.extra_dim,[r,1,1]),[-1,4]),n.reshape(o,[-1,4]));a=n.reshape(a,[r,a.shape[0]/r,4]);let s=n.sigmoid(n.slice(t,[0,0,1],[-1,-1,-1])),i=n.slice(s,[0,0,0],[-1,-1,1]);i=n.reshape(i,[r,i.shape[1]]);let c=n.unstack(a),m=n.unstack(i);return{boxes:c,scores:m}})}function Tt(o,t){return n.tidy(()=>{let e=o.shape[0],r=n.reshape(_t(o,t.box_encoding_predictor),[e,-1,1,4]),a=n.reshape(_t(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:a}})}function to(o,t,e){return n.tidy(()=>{let r=H(o,e.conv_0,[1,1]),a=H(r,e.conv_1,[2,2]),s=H(a,e.conv_2,[1,1]),i=H(s,e.conv_3,[2,2]),c=H(i,e.conv_4,[1,1]),m=H(c,e.conv_5,[2,2]),p=H(m,e.conv_6,[1,1]),u=H(p,e.conv_7,[2,2]),f=Tt(t,e.box_predictor_0),l=Tt(o,e.box_predictor_1),d=Tt(a,e.box_predictor_2),b=Tt(i,e.box_predictor_3),x=Tt(m,e.box_predictor_4),w=Tt(u,e.box_predictor_5),h=n.concat([f.boxPredictionEncoding,l.boxPredictionEncoding,d.boxPredictionEncoding,b.boxPredictionEncoding,x.boxPredictionEncoding,w.boxPredictionEncoding],1),y=n.concat([f.classPrediction,l.classPrediction,d.classPrediction,b.classPrediction,x.classPrediction,w.classPrediction],1);return{boxPredictions:h,classPredictions:y}})}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 wt=class extends I{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return n.tidy(()=>{let r=n.cast(t.toBatchTensor(512,!1),"float32"),a=n.sub(n.div(r,127.5),1),s=Zr(a,e.mobilenetv1),{boxPredictions:i,classPredictions:c}=to(s.out,s.conv11,e.prediction_layer);return Qr(i,c,e.output_layer)})}async forward(t){return this.forwardInput(await M(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:a}=new z(e),s=await M(t),{boxes:i,scores:c}=this.forwardInput(s),m=i[0],p=c[0];for(let v=1;v{let[D,N]=[Math.max(0,h[v][0]),Math.min(1,h[v][2])].map(O=>O*w),[Y,q]=[Math.max(0,h[v][1]),Math.min(1,h[v][3])].map(O=>O*x);return new E(u[v],new St(Y,D,q-Y,N-D),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return m.dispose(),p.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return qr(t)}extractParams(t){return Jr(t)}};function qo(o){let t=new wt;return t.extractWeights(o),t}function bl(o){return qo(o)}var eo=class extends wt{};var ro=.4,oo=[new g(.738768,.874946),new g(2.42204,2.65704),new g(4.30971,7.04493),new g(10.246,4.59428),new g(12.6868,11.8741)],no=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],ao=[117.001,114.697,97.404],so="tiny_yolov2_model",io="tiny_yolov2_separable_conv_model";var Se=o=>typeof o=="number";function co(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(!Se(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=>Se(t.x)&&Se(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(Se)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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mo(o,t,e,r){let{extractWeights:a,getRemainingWeights:s}=A(o),i=[],{extractConvParams:c,extractConvWithBatchNormParams:m,extractSeparableConvParams:p}=Zo(a,i),u;if(t.withSeparableConvs){let[f,l,d,b,x,w,h,y,v]=r,D=t.isFirstLayerConv2d?c(f,l,3,"conv0"):p(f,l,"conv0"),N=p(l,d,"conv1"),Y=p(d,b,"conv2"),q=p(b,x,"conv3"),O=p(x,w,"conv4"),at=p(w,h,"conv5"),st=y?p(h,y,"conv6"):void 0,it=v?p(y,v,"conv7"):void 0,gt=c(v||y||h,5*e,1,"conv8");u={conv0:D,conv1:N,conv2:Y,conv3:q,conv4:O,conv5:at,conv6:st,conv7:it,conv8:gt}}else{let[f,l,d,b,x,w,h,y,v]=r,D=m(f,l,"conv0"),N=m(l,d,"conv1"),Y=m(d,b,"conv2"),q=m(b,x,"conv3"),O=m(x,w,"conv4"),at=m(w,h,"conv5"),st=m(h,y,"conv6"),it=m(y,v,"conv7"),gt=c(v,5*e,1,"conv8");u={conv0:D,conv1:N,conv2:Y,conv3:q,conv4:O,conv5:at,conv6:st,conv7:it,conv8:gt}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function Ko(o,t){let e=B(o,t);function r(c){let m=e(`${c}/sub`,1),p=e(`${c}/truediv`,1);return{sub:m,truediv:p}}function a(c){let m=e(`${c}/filters`,4),p=e(`${c}/bias`,1);return{filters:m,bias:p}}function s(c){let m=a(`${c}/conv`),p=r(`${c}/bn`);return{conv:m,bn:p}}let i=Ht(e);return{extractConvParams:a,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function po(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}=Ko(o,e),i;if(t.withSeparableConvs){let c=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?r("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:c>7?s("conv6"):void 0,conv7:c>8?s("conv7"):void 0,conv8:r("conv8")}}else i={conv0:a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:a("conv6"),conv7:a("conv7"),conv8:r("conv8")};return L(o,e),{params:i,paramMappings:e}}var J=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 cr=class extends I{constructor(e){super("TinyYolov2");co(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let a=ot(e,r.conv0);return a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv1),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv2),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv3),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv4),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv5),a=n.maxPool(a,[2,2],[1,1],"same"),a=ot(a,r.conv6),a=ot(a,r.conv7),_t(a,r.conv8,"valid",!1)}runMobilenet(e,r){let a=this.config.isFirstLayerConv2d?Xt(_t(e,r.conv0,"valid",!1)):nt(e,r.conv0);return a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv1),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv2),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv3),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv4),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv5),a=n.maxPool(a,[2,2],[1,1],"same"),a=r.conv6?nt(a,r.conv6):a,a=r.conv7?nt(a,r.conv7):a,_t(a,r.conv8,"valid",!1)}forwardInput(e,r){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return n.tidy(()=>{let s=n.cast(e.toBatchTensor(r,!1),"float32");return s=this.config.meanRgb?X(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,a):this.runTinyYolov2(s,a)})}async forward(e,r){return this.forwardInput(await M(e),r)}async detect(e,r={}){let{inputSize:a,scoreThreshold:s}=new J(r),i=await M(e),c=await this.forwardInput(i,a),m=n.tidy(()=>n.unstack(c)[0].expandDims()),p={width:i.getInputWidth(0),height:i.getInputHeight(0)},u=await this.extractBoxes(m,i.getReshapedInputDimensions(0),s);c.dispose(),m.dispose();let f=u.map(h=>h.box),l=u.map(h=>h.score),d=u.map(h=>h.classScore),b=u.map(h=>this.config.classes[h.label]);return xr(f.map(h=>h.rescale(a)),l,this.config.iouThreshold,!0).map(h=>new ct(l[h],d[h],b[h],f[h],p))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return po(e,this.config)}extractParams(e){let r=this.config.filterSizes||cr.DEFAULT_FILTER_SIZES,a=r?r.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 mo(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,a){let{width:s,height:i}=r,c=Math.max(s,i),m=c/s,p=c/i,u=e.shape[1],f=this.config.anchors.length,[l,d,b]=n.tidy(()=>{let y=e.reshape([u,u,f,this.boxEncodingSize]),v=y.slice([0,0,0,0],[u,u,f,4]),D=y.slice([0,0,0,4],[u,u,f,1]),N=this.withClassScores?n.softmax(y.slice([0,0,0,5],[u,u,f,this.config.classes.length]),3):n.scalar(0);return[v,D,N]}),x=[],w=await d.array(),h=await l.array();for(let y=0;ya){let Y=(v+fe(h[y][v][D][0]))/u*m,q=(y+fe(h[y][v][D][1]))/u*p,O=Math.exp(h[y][v][D][2])*this.config.anchors[D].x/u*m,at=Math.exp(h[y][v][D][3])*this.config.anchors[D].y/u*p,st=Y-O/2,it=q-at/2,gt={row:y,col:v,anchor:D},{classScore:pr,label:ur}=this.withClassScores?await this.extractPredictedClass(b,gt):{classScore:1,label:0};x.push({box:new Nt(st,it,st+O,it+at),score:N,classScore:N*pr,label:ur,...gt})}}return l.dispose(),d.dispose(),b.dispose(),x}async extractPredictedClass(e,r){let{row:a,col:s,anchor:i}=r,c=await e.array();return Array(this.config.classes.length).fill(0).map((m,p)=>c[a][s][i][p]).map((m,p)=>({classScore:m,label:p})).reduce((m,p)=>m.classScore>p.classScore?m:p)}},Pt=cr;Pt.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Jt=class extends Pt{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:ro,classes:["face"],...t?{anchors:no,meanRgb:ao}:{anchors:oo,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(a=>new E(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?io:so}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function id(o,t=!0){let e=new Jt(t);return e.extractWeights(o),e}var Le=class extends J{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var V=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function Ft(o,t,e,r,a=({alignedRect:s})=>s){let s=o.map(m=>Yt(m)?a(m):m.detection),i=r||(t instanceof n.Tensor?await oe(t,s):await re(t,s)),c=await e(i);return i.forEach(m=>m instanceof n.Tensor&&m.dispose()),c}async function qt(o,t,e,r,a){return Ft([o],t,async s=>e(s[0]),r,a)}var uo=.4,fo=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],lo=[117.001,114.697,97.404];var Zt=class extends Pt{constructor(){let t={withSeparableConvs:!0,iouThreshold:uo,classes:["face"],anchors:fo,meanRgb:lo,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(a=>new E(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var T={ssdMobilenetv1:new wt,tinyFaceDetector:new Zt,tinyYolov2:new Jt,faceLandmark68Net:new jt,faceLandmark68TinyNet:new Ie,faceRecognitionNet:new Ut,faceExpressionNet:new Pe,ageGenderNet:new Me},Qo=(o,t)=>T.ssdMobilenetv1.locateFaces(o,t),Bd=(o,t)=>T.tinyFaceDetector.locateFaces(o,t),Rd=(o,t)=>T.tinyYolov2.locateFaces(o,t),tn=o=>T.faceLandmark68Net.detectLandmarks(o),$d=o=>T.faceLandmark68TinyNet.detectLandmarks(o),Od=o=>T.faceRecognitionNet.computeFaceDescriptor(o),Hd=o=>T.faceExpressionNet.predictExpressions(o),zd=o=>T.ageGenderNet.predictAgeAndGender(o),en=o=>T.ssdMobilenetv1.load(o),Vd=o=>T.tinyFaceDetector.load(o),Yd=o=>T.tinyYolov2.load(o),Gd=o=>T.faceLandmark68Net.load(o),jd=o=>T.faceLandmark68TinyNet.load(o),Ud=o=>T.faceRecognitionNet.load(o),Xd=o=>T.faceExpressionNet.load(o),Jd=o=>T.ageGenderNet.load(o),qd=en,Zd=Qo,Kd=tn;var Ae=class extends V{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Dt=class extends Ae{async run(){let t=await this.parentTask,e=await Ft(t,this.input,async r=>Promise.all(r.map(a=>T.faceExpressionNet.predictExpressions(a))),this.extractedFaces);return t.map((r,a)=>tr(r,e[a]))}withAgeAndGender(){return new Mt(this,this.input)}},Et=class extends Ae{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>T.faceExpressionNet.predictExpressions(r),this.extractedFaces);return tr(t,e)}withAgeAndGender(){return new Ct(this,this.input)}},ut=class extends Dt{withAgeAndGender(){return new lt(this,this.input)}withFaceDescriptors(){return new ht(this,this.input)}},ft=class extends Et{withAgeAndGender(){return new dt(this,this.input)}withFaceDescriptor(){return new bt(this,this.input)}};var We=class extends V{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Mt=class extends We{async run(){let t=await this.parentTask,e=await Ft(t,this.input,async r=>Promise.all(r.map(a=>T.ageGenderNet.predictAgeAndGender(a))),this.extractedFaces);return t.map((r,a)=>{let{age:s,gender:i,genderProbability:c}=e[a];return sr(ir(r,i,c),s)})}withFaceExpressions(){return new Dt(this,this.input)}},Ct=class extends We{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:a}=await qt(t,this.input,s=>T.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return sr(ir(t,r,a),e)}withFaceExpressions(){return new Et(this,this.input)}},lt=class extends Mt{withFaceExpressions(){return new ut(this,this.input)}withFaceDescriptors(){return new ht(this,this.input)}},dt=class extends Ct{withFaceExpressions(){return new ft(this,this.input)}withFaceDescriptor(){return new bt(this,this.input)}};var ke=class extends V{constructor(e,r){super();this.parentTask=e;this.input=r}},ht=class extends ke{async run(){let t=await this.parentTask;return(await Ft(t,this.input,r=>Promise.all(r.map(a=>T.faceRecognitionNet.computeFaceDescriptor(a))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,a)=>ar(t[a],r))}withFaceExpressions(){return new ut(this,this.input)}withAgeAndGender(){return new lt(this,this.input)}},bt=class extends ke{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>T.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return ar(t,e)}withFaceExpressions(){return new ft(this,this.input)}withAgeAndGender(){return new dt(this,this.input)}};var Be=class extends V{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?T.faceLandmark68TinyNet:T.faceLandmark68Net}},Re=class extends Be{async run(){let t=await this.parentTask,e=t.map(i=>i.detection),r=this.input instanceof n.Tensor?await oe(this.input,e):await re(this.input,e),a=await Promise.all(r.map(i=>this.landmarkNet.detectLandmarks(i)));return r.forEach(i=>i instanceof n.Tensor&&i.dispose()),t.filter((i,c)=>a[c]).map((i,c)=>ie(i,a[c]))}withFaceExpressions(){return new ut(this,this.input)}withAgeAndGender(){return new lt(this,this.input)}withFaceDescriptors(){return new ht(this,this.input)}},$e=class extends Be{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof n.Tensor?await oe(this.input,[e]):await re(this.input,[e]),a=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof n.Tensor&&s.dispose()),ie(t,a)}withFaceExpressions(){return new ft(this,this.input)}withAgeAndGender(){return new dt(this,this.input)}withFaceDescriptor(){return new bt(this,this.input)}};var Oe=class extends V{constructor(e,r=new z){super();this.input=e;this.options=r}},me=class extends Oe{async run(){let{input:t,options:e}=this,r;if(e instanceof Le)r=T.tinyFaceDetector.locateFaces(t,e);else if(e instanceof z)r=T.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof J)r=T.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(a=>At({},a)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new Re(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Dt(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Mt(this.runAndExtendWithFaceDetections(),this.input)}},He=class extends Oe{async run(){let t=await new me(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?At({},e):void 0)})}withFaceLandmarks(t=!1){return new $e(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Et(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Ct(this.runAndExtendWithFaceDetection(),this.input)}};function Jh(o,t=new z){return new He(o,t)}function mr(o,t=new z){return new me(o,t)}async function rn(o,t){return mr(o,new z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function eb(o,t={}){return mr(o,new J(t)).withFaceLandmarks().withFaceDescriptors()}var rb=rn;function ho(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((a,s)=>a-r[s]).reduce((a,s)=>a+s*s,0))}var ze=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 a=1,s=()=>`person ${a++}`;this._labeledDescriptors=r.map(i=>{if(i instanceof Q)return i;if(i instanceof Float32Array)return new Q(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Q(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>ho(r,t)).reduce((r,a)=>r+a,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Kt(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Q.fromJSON(r));return new ze(e,t.distanceThreshold)}};function yb(o){let t=new Zt;return t.extractWeights(o),t}function on(o,t){let{width:e,height:r}=new S(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(a=>on(a,{width:e,height:r}));if(Yt(o)){let a=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(a.box.width,a.box.height);return ie(At(o,a),s)}return tt(o)?At(o,o.detection.forSize(e,r)):o instanceof $||o instanceof E?o.forSize(e,r):o}var Nb=kr;export{Me as AgeGenderNet,Nt as BoundingBox,F as Box,V as ComposableTask,ht as ComputeAllFaceDescriptorsTask,ke as ComputeFaceDescriptorsTaskBase,bt as ComputeSingleFaceDescriptorTask,Re as DetectAllFaceLandmarksTask,me as DetectAllFacesTask,Be as DetectFaceLandmarksTaskBase,Oe as DetectFacesTaskBase,$e as DetectSingleFaceLandmarksTask,He as DetectSingleFaceTask,S as Dimensions,Lr as FACE_EXPRESSION_LABELS,E as FaceDetection,eo as FaceDetectionNet,Pe as FaceExpressionNet,pt as FaceExpressions,jt as FaceLandmark68Net,Ie as FaceLandmark68TinyNet,Yr as FaceLandmarkNet,$ as FaceLandmarks,yr as FaceLandmarks5,Lt as FaceLandmarks68,Kt as FaceMatch,ze as FaceMatcher,Ut as FaceRecognitionNet,rr as Gender,Qt as LabeledBox,Q as LabeledFaceDescriptors,rt as NetInput,I as NeuralNetwork,ct as ObjectDetection,g as Point,_r as PredictedBox,St as Rect,wt as SsdMobilenetv1,z as SsdMobilenetv1Options,Zt as TinyFaceDetector,Le as TinyFaceDetectorOptions,Jt as TinyYolov2,J as TinyYolov2Options,rb as allFaces,rn as allFacesSsdMobilenetv1,eb as allFacesTinyYolov2,Tr as awaitMediaLoaded,wr as bufferToImage,Od as computeFaceDescriptor,Rt as createCanvas,be as createCanvasFromMedia,bl as createFaceDetectionNet,bf as createFaceRecognitionNet,qo as createSsdMobilenetv1,yb as createTinyFaceDetector,id as createTinyYolov2,mr as detectAllFaces,tn as detectFaceLandmarks,$d as detectFaceLandmarksTiny,Kd as detectLandmarks,Jh as detectSingleFace,Wr as draw,_ as env,ho as euclideanDistance,sr as extendWithAge,ar as extendWithFaceDescriptor,At as extendWithFaceDetection,tr as extendWithFaceExpressions,ie as extendWithFaceLandmarks,ir as extendWithGender,oe as extractFaceTensors,re as extractFaces,Fi as fetchImage,Dr as fetchJson,Ii as fetchNetWeights,mt as fetchOrThrow,ki as fetchVideo,k as getContext2dOrThrow,Bt as getMediaDimensions,Pr as imageTensorToCanvas,Fr as imageToSquare,On as inverseSigmoid,br as iou,Qe as isMediaElement,he as isMediaLoaded,yf as isWithAge,tt as isWithFaceDetection,Ar as isWithFaceExpressions,Yt as isWithFaceLandmarks,Pf as isWithGender,Jd as loadAgeGenderModel,qd as loadFaceDetectionModel,Xd as loadFaceExpressionModel,Gd as loadFaceLandmarkModel,jd as loadFaceLandmarkTinyModel,Ud as loadFaceRecognitionModel,en as loadSsdMobilenetv1Model,Vd as loadTinyFaceDetectorModel,Yd as loadTinyYolov2Model,Mr as loadWeightMap,Zd as locateFaces,Vi as matchDimensions,gr as minBbox,T as nets,xr as nonMaxSuppression,X as normalize,vr as padToSquare,zd as predictAgeAndGender,Hd as recognizeFaceExpressions,on as resizeResults,Wt as resolveInput,Rn as shuffleArray,fe as sigmoid,Qo as ssdMobilenetv1,n as tf,Bd as tinyFaceDetector,Rd as tinyYolov2,M as toNetInput,hr as utils,co as validateConfig,Nb as version}; diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js index ffde92f..fe84c51 100644 --- a/dist/face-api.esm.js +++ b/dist/face-api.esm.js @@ -4,65 +4,69 @@ author: ' */ -var xD=Object.defineProperty;var vD=(e=>typeof require!="undefined"?require:typeof Proxy!="undefined"?new 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t=[];return LS(e,t,new Set),t}function LS(e,t,n){if(e==null)return;if(e instanceof Ce){t.push(e);return}if(!ZR(e))return;let a=e;for(let r in a){let s=a[r];n.has(s)||(n.add(s),LS(s,t,n))}}function ZR(e){return Array.isArray(e)||typeof e=="object"}function Yy(e){return e.kernelName!=null}var Mk=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()}},cc=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Mk}async 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i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;return a!=null&&(p=_(a,"offset","batchNorm")),A(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),A(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),A(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${p.rank}.`),ks(i,o,l,p,u,s)}var $v=L({batchNorm2d_:FP});function $P(e,t,n,a,r,s){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;return a!=null&&(p=_(a,"offset","batchNorm")),A(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),A(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),A(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${p.rank}.`),ks(i,o,l,p,u,s)}var Dv=L({batchNorm3d_:$P});function DP(e,t,n,a,r,s){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;return a!=null&&(p=_(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}.`),u!=null&&A(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${p.rank}.`),ks(i,o,l,p,u,s)}var Rv=L({batchNorm4d_:DP});function RP(e,t,n){let a=_(e,"x","bincount"),r=_(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 ${n}.`),A(r.size===a.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${a.shape}, weights shape: ${r.shape}.`);let s={x:a,weights:r},i={size:n};return O.runKernel(Ql,s,i)}var Mv=L({bincount_:RP});function MP(e,t){let n=_(e,"x","bitwiseAnd"),a=_(t,"y","bitwiseAnd");if(!$r(n.shape,a.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${n.shape}, y: 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Has rank ${n.rank}`);if(a.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). 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jL(e,t,n,a,r,s="NHWC",i){let o=e;e.rank===3&&(o=W(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=W(t,[1,t.shape[0],t.shape[1],t.shape[2]])),A(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),A(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),A(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let u=s==="NHWC"?o.shape[3]:o.shape[1],p=s==="NHWC"?l.shape[3]:l.shape[1];A(u===n[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${n[2]}.`),A(p===n[3],()=>`Error in conv2dDerFilter: depth of dy (${p}) must match output depth for filter (${n[3]}).`),Tn("conv2dDerFilter",r,i);let d={x:o,dy:l},c={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,filterShape:n};return O.runKernel(vm,d,c)}var Nw=L({conv2DBackpropFilter_:jL});function pf(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return z(e,Bo(t));throw new 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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)}},_w=class extends Rr{static get className(){return"Adagrad"}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=O.registeredVariables[t];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:P(()=>xn(a.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;P(()=>{let i=X(s,lt(r));s.assign(i);let o=X(z(he(r,hn(X(i,O.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&_e(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},Ew=class extends Rr{static get className(){return"Adam"}constructor(e,t,n,a=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],P(()=>{this.accBeta1=ve(t).variable(),this.accBeta2=ve(n).variable()}),a==null&&(this.epsilon=O.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);P(()=>{let n=pe(1,this.accBeta1),a=pe(1,this.accBeta2);t.forEach((r,s)=>{let i=O.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:P(()=>qe(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:P(()=>qe(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedSecondMoment[s].variable,d=X(z(u,this.beta1),z(l,1-this.beta1)),c=X(z(p,this.beta2),z(lt(l),1-this.beta2)),h=he(d,n),m=he(c,a);u.assign(d),p.assign(c);let f=X(z(he(h,X(hn(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(z(this.accBeta1,this.beta1)),this.accBeta2.assign(z(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&_e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&_e(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),P(()=>{this.accBeta1.assign(Fr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Fr(this.beta2,this.iterations_+1))});let 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)}},Aw=class extends Rr{static get className(){return"Adamax"}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=[],P(()=>{this.iteration=ve(0).variable(),this.accBeta1=ve(t).variable()}),a==null&&(this.epsilon=O.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);P(()=>{let n=pe(1,this.accBeta1),a=he(-this.learningRate,X(z(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=O.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:qe(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:qe(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedWeightedInfNorm[s].variable,d=X(z(u,this.beta1),z(l,1-this.beta1)),c=z(p,this.beta2),h=zt(l),m=dr(c,h);u.assign(d),p.assign(m);let f=X(z(he(a,n),he(d,X(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(X(this.iteration,1)),this.accBeta1.assign(z(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&_e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&_e(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)}},hf=class extends Rr{static get className(){return"SGD"}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=O.registeredVariables[t];P(()=>{let s=X(z(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(ve(-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)}},Fw=class extends hf{static get className(){return"Momentum"}constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=ve(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=O.registeredVariables[t];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${t}/momentum`,variable:P(()=>qe(a).variable(!1))});let r=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&P(()=>{let i,o=X(z(this.m,r),s);this.useNesterov?i=X(z(this.c,X(s,z(o,this.m))),a):i=X(z(this.c,o),a),r.assign(o),a.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&_e(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(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}},$w=class extends Rr{static get className(){return"RMSProp"}constructor(e,t=.9,n=0,a=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=O.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=O.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:P(()=>qe(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:P(()=>qe(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:P(()=>qe(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;P(()=>{let l=X(z(i,this.decay),z(lt(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[n].variable,p=X(z(u,this.decay),z(s,1-this.decay)),d=he(z(s,this.learningRate),hn(pe(l,X(lt(p),this.epsilon)))),c=X(z(o,this.momentum),d);i.assign(l),u.assign(p),o.assign(c);let h=pe(a,c);a.assign(h)}else{let u=X(z(i,this.decay),z(lt(s),1-this.decay)),p=X(z(o,this.momentum),he(z(s,this.learningRate),hn(X(u,this.epsilon))));i.assign(u),o.assign(p);let d=pe(a,p);a.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&_e(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&_e(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&_e(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)}},mW=[Cw,_w,Ew,Aw,Fw,$w,hf];function fW(){for(let e of mW)lT(e)}var 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browser.");e.startsWith(Rl.URL_SCHEME)&&(e=e.slice(Rl.URL_SCHEME.length)),(e==null||e.length===0)&&(e=gW),this.modelJsonFileName=e+bW,this.weightDataFileName=e+yW}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],a=LS(e,n),r=window.URL.createObjectURL(new Blob([JSON.stringify(a)],{type:"application/json"})),s=this.modelJsonAnchor==null?document.createElement("a"):this.modelJsonAnchor;if(s.download=this.modelJsonFileName,s.href=r,await zk(()=>s.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){let 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Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){let a=n.batchSize==null?32:n.batchSize;nx(a);let r=!0,s=this.standardizeUserDataXY(e,t,r,a);try{let i=s[0].concat(s[1]);this.makeTestFunction();let o=this.testFunction,l=this.testLoop(o,i,a,n.verbose,n.steps);return On(l)}finally{za(s[0],e),za(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),RG(this,e,t)}checkNumSamples(e,t,n,a="steps"){let r;if(n!=null){if(r=null,t!=null)throw new V(`If ${a} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?r=e[0].shape[0]:r=e.shape[0];else throw new V(`Either the input data should have a defined shape, or ${a} shoud be specified.`);return r}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new V("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),a=n?t:[t],r=this.retrieveSymbolicTensors(a),s=new Js;if(e instanceof Te&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new V(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let o=0;oi.name);for(let i=0;i0){let a=[];throw t.forEach((r,s)=>{r==null&&a.push(e[s])}),new V(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(a)}`)}return t}predictLoop(e,t=32,n=!1){return P(()=>{let a=this.checkNumSamples(e);if(n)throw new Oe("Verbose predictLoop() is not implemented yet.");let r=ax(a,t),s=this.outputs.map(i=>[]);for(let i=0;i{let o=r[i][0],l=r[i][1],u=Gp(e,o,l),p=[];if(Array.isArray(u))for(let c=0;cs[l].push(o));return On(s.map(i=>Je(i,0)))})}predict(e,t={}){let n=p2(e);cI(n,this.inputNames,this.feedInputShapes,!1);try{let a=t.batchSize==null?32:t.batchSize;return nx(a),this.predictLoop(n,a)}finally{za(n,e)}}predictOnBatch(e){cI(e,this.inputNames,this.feedInputShapes,!0);let t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,n=!0,a){if(this.optimizer_==null)throw new Ba("You must compile a model before training/testing. 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t=yc(e.optimizer_config),n=Ga(t),a;if(typeof e.loss=="string")a=Xs(e.loss);else if(Array.isArray(e.loss))a=e.loss.map(s=>Xs(s));else if(e.loss!=null){a={};for(let s in e.loss)a[s]=Xs(e.loss[s])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(s=>Xs(s));else if(e.metrics!=null){r={};for(let s in e.metrics)r[s]=Xs(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=qt.getSaveHandlers(e);if(i.length===0)throw new V(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new V(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new V("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await qt.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:zG,generatedBy:`TensorFlow.js tfjs-layers 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Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:p}=UG(a.weightData,a.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&p.length>0&&await o.optimizer.setWeights(p),_e(u),_e(p.map(d=>d.tensor))}return o}function UG(e,t){let n=qt.decodeWeights(e,t),a={},r=[];return t.forEach(s=>{s.group==="optimizer"?r.push({name:s.name,tensor:n[s.name]}):a[s.name]=n[s.name]}),{modelWeights:a,optimizerWeights:r}}var Ol=class extends Er{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:mf("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new V(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Ol||e instanceof Er,n;if(t){if(n=e,n.outputs.length!==1)throw new V("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new V("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 V("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let a=HT({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(a)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new V(`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 V("All layers in a Sequential model should have a single output tensor. 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Add some layers first.");this.model=new Er({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new Ba("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Ba("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 Ba("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 Ba("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 V("Legacy serialization format not supported yet.");r=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Ol))throw new Oe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Ga(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new V("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 V("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}}};Ol.className="Sequential";ne.registerClass(Ol);function GG(e){return new Er(e)}function HG(e){return new Ol(e)}function d2(e){return HT(e)}function qG(e,t){Ta.registerCallbackConstructor(e,t)}var Gn=class extends ne.Serializable{getConfig(){return{}}},h2=class extends Gn{apply(e,t=1){return gU(e,t)}};h2.className="elu";ne.registerClass(h2);var m2=class extends Gn{apply(e){return Zm(e)}};m2.className="selu";ne.registerClass(m2);var f2=class extends Gn{apply(e){return Ke(e)}};f2.className="relu";ne.registerClass(f2);var g2=class extends Gn{apply(e){return P(()=>ls(6,Ke(e)))}};g2.className="relu6";ne.registerClass(g2);var b2=class extends Gn{apply(e){return e}};b2.className="linear";ne.registerClass(b2);var y2=class extends Gn{apply(e){return ha(e)}};y2.className="sigmoid";ne.registerClass(y2);var x2=class extends Gn{apply(e){return yU(e)}};x2.className="hardSigmoid";ne.registerClass(x2);var v2=class extends Gn{apply(e){return Oo(e)}};v2.className="softplus";ne.registerClass(v2);var w2=class extends Gn{apply(e){return bU(e)}};w2.className="softsign";ne.registerClass(w2);var k2=class extends Gn{apply(e){return pi(e)}};k2.className="tanh";ne.registerClass(k2);var s0=class extends Gn{apply(e,t=-1){return Xa(e,t)}};s0.className="softmax";ne.registerClass(s0);var I2=class extends Gn{apply(e,t=-1){return Um(e,t)}};I2.className="logSoftmax";ne.registerClass(I2);var S2=class extends Gn{apply(e,t=1){return P(()=>z(ha(z(e,t)),e))}};S2.className="swish";ne.registerClass(S2);var N2=class extends Gn{apply(e){return P(()=>z(e,pi(Oo(e))))}};N2.className="mish";ne.registerClass(N2);function ps(e){return e.getClassName()}function rx(e,t={}){return cd(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function cs(e){if(e==null){let t={};return t.className="linear",t.config={},rx(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},rx(t)}else return e instanceof Gn?e:rx(e)}function i0(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var T2=class extends ne.Serializable{},gd=class extends T2{constructor(e){super(),i0(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 P(()=>{let t=Nt([1]);return this.hasL1&&(t=X(t,fe(z(this.l1,zt(e))))),this.hasL2&&(t=X(t,fe(z(this.l2,hd(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};gd.className="L1L2";ne.registerClass(gd);function jG(e){return i0(e),new gd({l1:e!=null?e.l1:null,l2:0})}function KG(e){return i0(e),new gd({l2:e!=null?e.l2:null,l1:0})}var dI={l1l2:"L1L2"};function mt(e){return Lw(e)}function hI(e,t={}){return cd(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ct(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in dI?dI[e]:e,config:{}};return hI(t)}else return e instanceof T2?e:hI(e)}var o0=class extends Ue{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ce(e);let n=Ke(e);return this.maxValue!=null&&(n=nn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};o0.className="ReLU";ne.registerClass(o0);var l0=class extends Ue{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 n=Ce(e);return td(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};l0.className="LeakyReLU";ne.registerClass(l0);var u0=class extends Ue{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Tt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ct(e.alphaRegularizer),this.alphaConstraint=Yt(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 V(`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 a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a(Pt(t),t==="channelsFirst"?De(e,[0,2,3,1]):e))}function C2(e,t){return P(()=>(Pt(t),t==="channelsFirst"?De(e,[0,2,3,4,1]):e))}function XG(e,t,n,a=1,r="valid",s,i=1){return P(()=>{if(s==null&&(s=ja()),Pt(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=De(e,[0,2,1])),r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Om(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ya(o,n)),o})}function mI(e,t,n,a=[1,1],r="valid",s,i,o=null){return P(()=>{if(s==null&&(s=ja()),Pt(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=h0(e,s);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Dl.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=De(l,[0,3,1,2])),l})}function YG(e,t,n,a=[1,1,1],r="valid",s,i){return P(()=>{if(s==null&&(s=ja()),Pt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=C2(e,s);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Bv(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ya(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var m0=class extends Ue{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",m0.verifyArgs(t),this.rank=e,en(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Oe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=kl(t.kernelSize,e,"kernelSize"),this.strides=kl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,xa(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Pt(this.dataFormat),this.activation=cs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Tt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(t.biasConstraint),this.biasRegularizer=Ct(t.biasRegularizer),this.activityRegularizer=Ct(t.activityRegularizer),this.dilationRate=kl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`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 V(`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 V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(nr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!zw(e.kernelSize,"number",1,3))throw new V(`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:ps(this.activation),useBias:this.useBias,biasInitializer:At(this.biasInitializer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},bd=class extends m0{constructor(e,t){super(e,t),this.kernel=null,bd.verifyArgs(t),this.filters=t.filters,en(this.filters,"filters"),this.kernelInitializer=Tt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(t.kernelConstraint),this.kernelRegularizer=Ct(t.kernelRegularizer)}build(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`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 P(()=>{e=Ce(e);let n,a=this.bias==null?null:this.bias.read(),r=MT(this.activation.getClassName());if(r!=null&&this.rank===2)n=mI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=XG(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=mI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=YG(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Oe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=Qe(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},yd=class extends bd{constructor(e){super(2,e),yd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!zw(e.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};yd.className="Conv2D";ne.registerClass(yd);var xd=class extends bd{constructor(e){super(3,e),xd.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 V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};xd.className="Conv3D";ne.registerClass(xd);var f0=class extends yd{constructor(e){if(super(e),this.inputSpec=[new Wt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Qe(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Wt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ce(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],u=this.kernelSize[0],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=ar(o,d,u,this.padding),m=ar(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,1]));let g=Lm(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=De(g,[0,3,1,2])),this.bias!=null&&(g=Ya(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=Qe(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]=ar(t[a],o,s,this.padding),t[r]=ar(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};f0.className="Conv2DTranspose";ne.registerClass(f0);var g0=class extends xd{constructor(e){if(super(e),this.inputSpec=[new Wt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Qe(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Wt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ce(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],b=ar(l,m,d,this.padding),y=ar(u,f,c,this.padding),x=ar(p,g,h,this.padding),w=[r,b,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,4,1]));let I=Vv(n,this.kernel.read(),w,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=De(I,[0,4,1,2,3])),this.bias!==null&&(I=Ya(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=Qe(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=ar(t[a],u,i,this.padding),t[r]=ar(t[r],p,o,this.padding),t[s]=ar(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};g0.className="Conv3DTranspose";ne.registerClass(g0);var _2=class extends bd{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("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 V(`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=Tt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ct(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=Tt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ct(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Qe(e),e.length{e=Ce(e);let n;if(this.rank===1)throw new Oe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=De(e,[0,2,3,1])),n=Ns(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=De(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=At(this.depthwiseInitializer),e.pointwiseInitializer=At(this.pointwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.pointwiseRegularizer=mt(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};_2.className="SeparableConv";var b0=class extends _2{constructor(e){super(2,e)}};b0.className="SeparableConv2D";ne.registerClass(b0);var Tf=class extends bd{constructor(e){super(1,e),Tf.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"&&!zw(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Tf.className="Conv1D";ne.registerClass(Tf);var y0=class extends Ue{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 P(()=>{if(e=Ce(e),this.dataFormat==="channelsLast"){let n=yh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return yh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=yh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return yh(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}};y0.className="Cropping2D";ne.registerClass(y0);var x0=class extends Ue{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,Pt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,uU(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 P(()=>{let n=Ce(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=De(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Ea.resizeNearestNeighbor(n,[r,s]):Ea.resizeBilinear(n,[r,s]);return De(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Ea.resizeNearestNeighbor(n,[r,s]):Ea.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};x0.className="UpSampling2D";ne.registerClass(x0);function ZG(e,t,n=[1,1],a="valid",r,s){return P(()=>{r==null&&(r=ja()),Pt(r);let i=h0(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Is(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}var v0=class extends m0{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Tt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Ct(e.depthwiseRegularizer)}build(e){if(e=Qe(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 P(()=>{e=Ce(e);let n=ZG(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Qe(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=Ha(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ha(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=At(this.depthwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};v0.className="DepthwiseConv2D";ne.registerClass(v0);function E2(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("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 A2(e,t,n,a=!1,r,s,i=!1,o=!1){return P(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(qa(2,l));if(t=De(t,u),s!=null)throw new Oe("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=ie(ie(r,"bool"),"float32"),r.rank===l-1&&(r=Qt(r,-1)),r=De(r,u)),a&&(t=ga(t,0),r!=null&&(r=ga(r,0)));let p=[],d,c=n,h=t.shape[0],m=pt(t),f;r!=null&&(f=pt(r));for(let b=0;be(y,c));if(r==null)d=x[0],c=x[1];else{let w=P(()=>{let I=f[b],T=pe(ta(I),I),C=X(z(x[0],I),z(c[0],T)),E=c.map((F,D)=>X(z(x[1][D],I),z(F,T)));return{output:C,newStates:E}});d=w.output,c=w.newStates}o&&p.push(d)}let g;return o&&(g=Dt(p,1)),[d,g,c]})}var mr=class extends Ue{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Ef({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("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 Wt({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 qa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){_x(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 P(()=>{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;ns.shape[s.shape.length-1]),r))throw new V(`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=r.map(s=>new Wt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){P(()=>{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 V("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=>Nt([n,a])):this.states_=[Nt([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>Nt([n,a])):this.states_[0]=Nt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let a=0;aHt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=E2(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 Wt({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 Va){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let p=super.apply(o,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return P(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Ce(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 V(`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=A2((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],p=o[2];this.stateful&&this.resetStates(p,a);let d=this.returnSequences?u:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return P(()=>{let t=Nt(e.shape);return t=fe(t,[1,2]),t=dd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Tx(t,[1,n]):t):this.cell.stateSize>1?[Tx(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()===mr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),e),t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ga(a,n);return new e(Object.assign(t,{cell:r}))}};mr.className="RNN";ne.registerClass(mr);var vd=class extends Ue{},Cf=class extends vd{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,en(this.units,"units"),this.activation=cs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Ml([1,us([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ml([1,us([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,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 P(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0ta(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0ta(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=or(z(e,s),this.kernel.read()):r=or(e,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,or(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:ps(this.activation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),recurrentInitializer:At(this.recurrentInitializer),biasInitializer:At(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};Cf.className="SimpleRNNCell";ne.registerClass(Cf);var w0=class extends mr{constructor(e){e.cell=new Cf(e),super(e)}call(e,t){return P(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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)}};w0.className="SimpleRNN";ne.registerClass(w0);var _f=class extends vd{constructor(e){if(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",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,en(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=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Ml([1,us([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ml([1,us([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,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 P(()=>{if(e=e,e.length!==2)throw new V(`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],0ta(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0ta(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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)}};k0.className="GRU";ne.registerClass(k0);var wd=class extends vd{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,en(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=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Ml([1,us([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ml([1,us([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Qe(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 Ra{apply(i,o){let l=r.apply([s]),u=new gf().apply([s]),p=r.apply([s*2]);return Jk(Jk(l,u),p)}},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 P(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0ta(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0ta(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};I0.className="LSTM";ne.registerClass(I0);var Ef=class extends vd{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return P(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{ti(`RNNCell_${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(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ga(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 Ex(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):VT(t(),n),o=()=>md(i,t,a);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var JG=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{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("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 P(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=Nt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){P(()=>{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 V("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(()=>Nt(r)):this.states_=[Nt(r)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Nt(r)):this.states_[0]=Nt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;sHt(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],u=e[o?4:3],p=Ha(l,a[0],r,s[0],i[0]),d=Ha(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,p,d]:[p,d,n]]}};F2.className="ConvRNN2D";var Af=class extends wd{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,en(this.filters,"filters"),this.kernelSize=kl(n,2,"kernelSize"),this.kernelSize.forEach(o=>en(o,"kernelSize")),this.strides=kl(a||1,2,"strides"),this.strides.forEach(o=>en(o,"strides")),this.padding=r||"valid",xa(this.padding),this.dataFormat=s||"channelsLast",Pt(this.dataFormat),this.dilationRate=kl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>en(o,"dilationRate"))}build(e){var t;e=Qe(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`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,u=this.filters;o=new(t=class extends Ra{apply(p,d){let c=l.apply([u]),h=Jn([u]),m=l.apply([u*2]);return Ww([c,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 P(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0ta(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0ta(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,w,I,T]=zn(this.kernel.read(),i,y),[C,E,F,D]=this.useBias?zn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,w,E,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,T,D,this.padding);let[$,S,M,B]=zn(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),b=this.recurrentConv(b,B);let U=this.recurrentActivation.apply(X(u,m)),H=this.recurrentActivation.apply(X(p,f)),j=X(z(H,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,b)),this.activation.apply(j));return[K,K,j]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=JG(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=Rt(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ya(r,n,this.dataFormat):r}recurrentConv(e,t){return Rt(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Af.className="ConvLSTM2DCell";ne.registerClass(Af);var S0=class extends F2{constructor(e){let t=new Af(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};S0.className="ConvLSTM2D";ne.registerClass(S0);var Ff=class extends Ue{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.invokeCallHook(e,t);let n=Ce(e);if(0VT(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()}};Ff.className="Dropout";ne.registerClass(Ff);var N0=class extends Ff{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};N0.className="SpatialDropout1D";ne.registerClass(N0);var T0=class extends Ue{constructor(e){if(super(e),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,en(this.units,"units"),this.activation=cs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Ct(e.kernelRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.activityRegularizer=Ct(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 P(()=>{this.invokeCallHook(e,t);let n=Ce(e),a=MT(this.activation.getClassName()),r;return a!=null?r=or(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=or(n,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ps(this.activation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),biasInitializer:At(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};T0.className="Dense";ne.registerClass(T0);var C0=class extends Ue{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 V(`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],as(e,1)]}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ce(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r{this.invokeCallHook(e,t);let n=Ce(e);return this.activation.apply(n)})}getConfig(){let e={activation:ps(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};_0.className="Activation";ne.registerClass(_0);var E0=class extends Ue{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 P(()=>(e=Ce(e),hU(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};E0.className="RepeatVector";ne.registerClass(E0);var A0=class extends Ue{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t{this.invokeCallHook(e,t);let n=Ce(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return W(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};A0.className="Reshape";ne.registerClass(A0);var F0=class extends Ue{constructor(e){if(super(e),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=qa(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Wt({ndim:this.dims.length+1})]}computeOutputShape(e){e=Qe(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return De(Ce(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};F0.className="Permute";ne.registerClass(F0);var $0=class extends Ue{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=Ce(e),a=-1;return hc(di(n,this.maskValue),a)}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ce(e),a=-1,r=!0,s=hc(di(n,this.maskValue),a,r);return z(n,ie(s,n.dtype))})}};$0.className="Masking";ne.registerClass($0);var D0=class extends Ue{constructor(e){if(super(e),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(vt(e.inputLength))}this.inputDim=e.inputDim,en(this.inputDim,"inputDim"),this.outputDim=e.outputDim,en(this.outputDim,"outputDim"),this.embeddingsInitializer=Tt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ct(e.embeddingsRegularizer),this.activityRegularizer=Ct(e.activityRegularizer),this.embeddingsConstraint=Yt(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 P(()=>this.maskZero?(e=Ce(e),di(e,qe(e))):null)}computeOutputShape(e){if(e=Qe(e),this.inputLength==null)return[...e,this.outputDim];let t=vt(this.inputLength);if(t.length!==e.length-1)throw new V(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a{this.invokeCallHook(e,t);let n=Ce(e);n.dtype!=="int32"&&(n=ir(n,"int32"));let a=BT(this.embeddings.read(),W(n,[n.size]));return W(a,Qe(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:At(this.embeddingsInitializer),embeddingsRegularizer:mt(this.embeddingsRegularizer),activityRegularizer:mt(this.activityRegularizer),embeddingsConstraint:Xt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};D0.className="Embedding";ne.registerClass(D0);var Uo=class extends Ue{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Oe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length1)throw new V(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;rr.length);e.indexOf(null)===-1&&ns(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return P(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=us(a);for(let s of e){let i=s.rank;for(let o=0;o1){let u=qa(1,l).concat([0]);n.push(De(o,u)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,u=o[l-1],p=[u].concat(o.slice(0,o.length-1));s=W(De(W(s,[-1,u]),[1,0]),p)}else if(i>1){let o=[i-1].concat(qa(0,i-1));s=De(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a{if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an Array");if(!Array.isArray(e))throw new V("`inputs` should be an Array");if(t.length!==e.length)throw new V(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:Qt(a,0));let n=t[0];for(let a=1;a{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new V("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return P(()=>Ww(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new V("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new V("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new V(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return P(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s3||t.shape.length>3)throw new Oe("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got 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Array.isArray(this.axes)?a=this.axes.map((r,s)=>Hp(r,e[s].shape.length)):a=[Hp(this.axes,t.shape.length),Hp(this.axes,n.shape.length)],this.normalize&&(t=Zh(t,a[0]),n=Zh(n,a[1])),QG(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Hp(this.axes,e.length),Hp(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Oe("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}};W0.className="Dot";ne.registerClass(W0);var B0=class extends Ue{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 P(()=>{this.invokeCallHook(e,t);let n=Ce(e);return md(()=>X(ff(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};B0.className="GaussianNoise";ne.registerClass(B0);var V0=class extends Ue{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 P(()=>{this.invokeCallHook(e,t);let n=Ce(e);return this.rate>0&&this.rate<1?md(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return z(n,ff(n.shape,1,a))},()=>n,t.training||!1):n})}};V0.className="GaussianDropout";ne.registerClass(V0);var U0=class extends Ue{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ce(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 P(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return md(()=>{let a=Ce(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=$r(ep(n),this.rate);o=ir(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,p=X(z(a,o),z(X(o,-1),i));return X(z(p,l),u)},()=>Ce(e),t.training||!1)}return e})}};U0.className="AlphaDropout";ne.registerClass(U0);function xc(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=Fv(e,t,n,a,r,s);else if(e.rank===3)i=$v(e,t,n,a,r,s);else if(e.rank===4)i=Dv(e,t,n,a,r,s);else throw new Oe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function eH(e,t,n,a,r=.001){return P(()=>{let s=rd(e,a),i=s.mean,o=s.variance;return[xc(e,i,o,n,t,r),i,o]})}function tH(e,t,n,a,r=.001){return P(()=>{let s=rd(e,a),i=s.mean,o=s.variance,l=[];for(let h of qa(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let u=W(i,l),p=W(o,l),d=t==null?null:W(t,l),c=n==null?null:W(n,l);return[xc(e,u,p,c,d,r),i,o]})}function nH(e,t,n,a,r=.001){return v.arraysEqual(a.slice().sort(),qa(0,e.rank-1))?eH(e,t,n,a,r):tH(e,t,n,a,r)}var G0=class extends Ue{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=Tt(e.betaInitializer||"zeros"),this.gammaInitializer=Tt(e.gammaInitializer||"ones"),this.movingMeanInitializer=Tt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Tt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Yt(e.betaConstraint),this.gammaConstraint=Yt(e.gammaConstraint),this.betaRegularizer=Ct(e.betaRegularizer),this.gammaRegularizer=Ct(e.gammaRegularizer)}build(e){e=Qe(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Wt({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 P(()=>{let n=t.training==null?!1:t.training,a=Ce(e),r=a.shape,s=r.length,i=qa(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=mi(1,s);l[o]=r[o];let u=i.slice();u.sort();let p=!v.arraysEqual(u,qa(0,s).slice(0,s-1)),d=()=>{if(p){let g=W(this.movingMean.read(),l),b=W(this.movingVariance.read(),l),y=this.center?W(this.beta.read(),l):null,x=this.scale?W(this.gamma.read(),l):null;return xc(a,g,b,y,x,this.epsilon)}else return xc(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 d();let[c,h,m]=nH(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,b,y)=>{P(()=>{let x=1-y,w=g.read(),I=z(pe(w,b),x);g.write(pe(w,I))})};return f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum),c})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:At(this.betaInitializer),gammaInitializer:At(this.gammaInitializer),movingMeanInitializer:At(this.movingMeanInitializer),movingVarianceInitializer:At(this.movingVarianceInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer),betaConstraint:Xt(this.betaConstraint),gammaConstraint:Xt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};G0.className="BatchNormalization";ne.registerClass(G0);var H0=class extends Ue{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=Tt(e.betaInitializer||"zeros"),this.gammaInitializer=Tt(e.gammaInitializer||"ones"),this.betaRegularizer=Ct(e.betaRegularizer),this.gammaRegularizer=Ct(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Qe(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==ns(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=Ce(e),a=n.shape,r=a.length;return P(()=>{let{mean:s,variance:i}=rd(n,this.axis,!0),o=mi(1,r);for(let h of this.axis)o[h]=a[h];let l=h=>h!=null&&h.shape.length!==r?W(h,o):h,u=this.scale?l(this.gamma.read()):null,p=this.center?l(this.beta.read()):null,d=[],c=[];for(let h=0;h{if(e.rank!==4)throw new V(`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 V("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ja()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],ya(e,a)})}var q0=class extends Ue{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ja():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new V(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 V(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new V(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Wt({ndim:4})]}computeOutputShape(e){e=Qe(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 P(()=>aH(Ce(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};q0.className="ZeroPadding2D";ne.registerClass(q0);function $f(e,t,n,a,r,s){return P(()=>{Pt(r),OT(s),xa(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=ja()),s==null&&(s="max"),e=h0(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Mt(e,t,n,o):i=ba(e,t,n,o),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}function $2(e,t,n,a,r,s){return P(()=>{Pt(r),OT(s),xa(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=ja()),s==null&&(s="max"),e=C2(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=rw(e,t,n,o):i=Av(e,t,n,o),r==="channelsFirst"&&(i=De(i,[0,4,1,2,3])),i})}var D2=class extends Ue{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 V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(en(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 V(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,xa(this.padding),this.inputSpec=[new Wt({ndim:3})]}computeOutputShape(e){e=Qe(e);let t=Ha(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return P(()=>{this.invokeCallHook(e,t),e=dd(Ce(e),2);let n=this.poolingFunction(Ce(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Ts(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},j0=class extends D2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),xa(a),$f(e,t,n,a,r,"max")}};j0.className="MaxPooling1D";ne.registerClass(j0);var K0=class extends D2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),xa(a),$f(e,t,n,a,r,"avg")}};K0.className="AveragePooling1D";ne.registerClass(K0);var R2=class extends Ue{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 V(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];en(this.poolSize,"poolSize"),en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),xa(this.padding),this.inputSpec=[new Wt({ndim:4})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ce(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},X0=class extends R2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),xa(a),$f(e,t,n,a,r,"max")}};X0.className="MaxPooling2D";ne.registerClass(X0);var Y0=class extends R2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),xa(a),$f(e,t,n,a,r,"avg")}};Y0.className="AveragePooling2D";ne.registerClass(Y0);var M2=class extends Ue{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 V(`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];en(this.poolSize,"poolSize"),en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),xa(this.padding),this.inputSpec=[new Wt({ndim:5})]}computeOutputShape(e){e=Qe(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=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(n,this.poolSize[1],this.padding,this.strides[1]),a=Ha(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 P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ce(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}},Z0=class extends M2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),xa(a),$2(e,t,n,a,r,"max")}};Z0.className="MaxPooling3D";ne.registerClass(Z0);var J0=class extends M2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),xa(a),$2(e,t,n,a,r,"avg")}};J0.className="AveragePooling3D";ne.registerClass(J0);var P2=class extends Ue{constructor(e){super(e),this.inputSpec=[new Wt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:p}=sH(a.weightData,a.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&p.length>0&&await o.optimizer.setWeights(p),_e(u),_e(p.map(d=>d.tensor))}return o}function sH(e,t){let n=qt.decodeWeights(e,t),a={},r=[];return t.forEach(s=>{s.group==="optimizer"?r.push({name:s.name,tensor:n[s.name]}):a[s.name]=n[s.name]}),{modelWeights:a,optimizerWeights:r}}var Ll=class extends Er{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:ff("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new V(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Ll||e instanceof Er,n;if(t){if(n=e,n.outputs.length!==1)throw new V("All layers in a Sequential model should have a single output tensor. 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For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=JT(this.outputs[0])}this.inboundNodes=[],new Sf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:gi(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(a=>a.shape),outputShapes:this.outputs[0].shape})}else{let a=e.apply(this.outputs[0]);if(Array.isArray(a))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[a],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(Ze(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new Er({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new Ba("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Ba("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 Ba("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 Ba("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 V("Legacy serialization format not supported yet.");r=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Ll))throw new Oe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Ga(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new V("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 V("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}}};Ll.className="Sequential";ne.registerClass(Ll);function iH(e){return new Er(e)}function oH(e){return new Ll(e)}function v2(e){return QT(e)}function lH(e,t){Ca.registerCallbackConstructor(e,t)}var Gn=class extends ne.Serializable{getConfig(){return{}}},w2=class extends Gn{apply(e,t=1){return $U(e,t)}};w2.className="elu";ne.registerClass(w2);var k2=class extends Gn{apply(e){return Jm(e)}};k2.className="selu";ne.registerClass(k2);var I2=class extends Gn{apply(e){return Ke(e)}};I2.className="relu";ne.registerClass(I2);var S2=class extends Gn{apply(e){return P(()=>us(6,Ke(e)))}};S2.className="relu6";ne.registerClass(S2);var N2=class extends Gn{apply(e){return e}};N2.className="linear";ne.registerClass(N2);var T2=class extends Gn{apply(e){return ma(e)}};T2.className="sigmoid";ne.registerClass(T2);var C2=class extends Gn{apply(e){return RU(e)}};C2.className="hardSigmoid";ne.registerClass(C2);var _2=class extends Gn{apply(e){return zo(e)}};_2.className="softplus";ne.registerClass(_2);var E2=class extends Gn{apply(e){return DU(e)}};E2.className="softsign";ne.registerClass(E2);var A2=class extends Gn{apply(e){return di(e)}};A2.className="tanh";ne.registerClass(A2);var i0=class extends Gn{apply(e,t=-1){return Xa(e,t)}};i0.className="softmax";ne.registerClass(i0);var F2=class extends Gn{apply(e,t=-1){return Gm(e,t)}};F2.className="logSoftmax";ne.registerClass(F2);var $2=class extends Gn{apply(e,t=1){return P(()=>z(ma(z(e,t)),e))}};$2.className="swish";ne.registerClass($2);var D2=class extends Gn{apply(e){return P(()=>z(e,di(zo(e))))}};D2.className="mish";ne.registerClass(D2);function cs(e){return e.getClassName()}function sx(e,t={}){return cd(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function ds(e){if(e==null){let t={};return t.className="linear",t.config={},sx(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},sx(t)}else return e instanceof Gn?e:sx(e)}function o0(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var R2=class extends ne.Serializable{},gd=class extends R2{constructor(e){super(),o0(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 P(()=>{let t=Nt([1]);return this.hasL1&&(t=X(t,fe(z(this.l1,Wt(e))))),this.hasL2&&(t=X(t,fe(z(this.l2,hd(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};gd.className="L1L2";ne.registerClass(gd);function uH(e){return o0(e),new gd({l1:e!=null?e.l1:null,l2:0})}function pH(e){return o0(e),new gd({l2:e!=null?e.l2:null,l1:0})}var mI={l1l2:"L1L2"};function mt(e){return zw(e)}function fI(e,t={}){return cd(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ct(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in mI?mI[e]:e,config:{}};return fI(t)}else return e instanceof R2?e:fI(e)}var l0=class extends Be{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Te(e);let n=Ke(e);return this.maxValue!=null&&(n=rn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};l0.className="ReLU";ne.registerClass(l0);var u0=class extends Be{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 n=Te(e);return td(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};u0.className="LeakyReLU";ne.registerClass(u0);var p0=class extends Be{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Tt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ct(e.alphaRegularizer),this.alphaConstraint=Yt(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 V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Ze(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a(Pt(t),t==="channelsFirst"?De(e,[0,2,3,1]):e))}function M2(e,t){return P(()=>(Pt(t),t==="channelsFirst"?De(e,[0,2,3,4,1]):e))}function cH(e,t,n,a=1,r="valid",s,i=1){return P(()=>{if(s==null&&(s=ja()),Pt(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=De(e,[0,2,1])),r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Lm(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ya(o,n)),o})}function gI(e,t,n,a=[1,1],r="valid",s,i,o=null){return P(()=>{if(s==null&&(s=ja()),Pt(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=m0(e,s);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Rl.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=De(l,[0,3,1,2])),l})}function dH(e,t,n,a=[1,1,1],r="valid",s,i){return P(()=>{if(s==null&&(s=ja()),Pt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=M2(e,s);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Vv(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ya(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var f0=class extends Be{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",f0.verifyArgs(t),this.rank=e,nn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Oe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Il(t.kernelSize,e,"kernelSize"),this.strides=Il(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,va(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Pt(this.dataFormat),this.activation=ds(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Tt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(t.biasConstraint),this.biasRegularizer=Ct(t.biasRegularizer),this.activityRegularizer=Ct(t.activityRegularizer),this.dilationRate=Il(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`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 V(`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 V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(nr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Ww(e.kernelSize,"number",1,3))throw new V(`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:cs(this.activation),useBias:this.useBias,biasInitializer:At(this.biasInitializer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},bd=class extends f0{constructor(e,t){super(e,t),this.kernel=null,bd.verifyArgs(t),this.filters=t.filters,nn(this.filters,"filters"),this.kernelInitializer=Tt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(t.kernelConstraint),this.kernelRegularizer=Ct(t.kernelRegularizer)}build(e){e=Ze(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`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 P(()=>{e=Te(e);let n,a=this.bias==null?null:this.bias.read(),r=UT(this.activation.getClassName());if(r!=null&&this.rank===2)n=gI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=cH(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=gI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=dH(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Oe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=Ze(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},yd=class extends bd{constructor(e){super(2,e),yd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Ww(e.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};yd.className="Conv2D";ne.registerClass(yd);var xd=class extends bd{constructor(e){super(3,e),xd.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 V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};xd.className="Conv3D";ne.registerClass(xd);var g0=class extends yd{constructor(e){if(super(e),this.inputSpec=[new Bt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Ze(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Te(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],u=this.kernelSize[0],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=ar(o,d,u,this.padding),m=ar(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,1]));let g=zm(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=De(g,[0,3,1,2])),this.bias!=null&&(g=Ya(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=Ze(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]=ar(t[a],o,s,this.padding),t[r]=ar(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};g0.className="Conv2DTranspose";ne.registerClass(g0);var b0=class extends xd{constructor(e){if(super(e),this.inputSpec=[new Bt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Ze(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Te(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],b=ar(l,m,d,this.padding),y=ar(u,f,c,this.padding),x=ar(p,g,h,this.padding),w=[r,b,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,4,1]));let I=Uv(n,this.kernel.read(),w,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=De(I,[0,4,1,2,3])),this.bias!==null&&(I=Ya(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=Ze(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=ar(t[a],u,i,this.padding),t[r]=ar(t[r],p,o,this.padding),t[s]=ar(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};b0.className="Conv3DTranspose";ne.registerClass(b0);var P2=class extends bd{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("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 V(`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=Tt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ct(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=Tt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ct(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Ze(e),e.length{e=Te(e);let n;if(this.rank===1)throw new Oe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=De(e,[0,2,3,1])),n=Ts(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=De(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=At(this.depthwiseInitializer),e.pointwiseInitializer=At(this.pointwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.pointwiseRegularizer=mt(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};P2.className="SeparableConv";var y0=class extends P2{constructor(e){super(2,e)}};y0.className="SeparableConv2D";ne.registerClass(y0);var Cf=class extends bd{constructor(e){super(1,e),Cf.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"&&!Ww(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Cf.className="Conv1D";ne.registerClass(Cf);var x0=class extends Be{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 P(()=>{if(e=Te(e),this.dataFormat==="channelsLast"){let n=yh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return yh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=yh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return yh(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}};x0.className="Cropping2D";ne.registerClass(x0);var v0=class extends Be{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,Pt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,NU(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 P(()=>{let n=Te(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=De(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Qn.resizeNearestNeighbor(n,[r,s]):Qn.resizeBilinear(n,[r,s]);return De(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Qn.resizeNearestNeighbor(n,[r,s]):Qn.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};v0.className="UpSampling2D";ne.registerClass(v0);function hH(e,t,n=[1,1],a="valid",r,s){return P(()=>{r==null&&(r=ja()),Pt(r);let i=m0(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Is(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}var w0=class extends f0{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Tt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Ct(e.depthwiseRegularizer)}build(e){if(e=Ze(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 P(()=>{e=Te(e);let n=hH(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Ze(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=Ha(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ha(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=At(this.depthwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};w0.className="DepthwiseConv2D";ne.registerClass(w0);function O2(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("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 L2(e,t,n,a=!1,r,s,i=!1,o=!1){return P(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(qa(2,l));if(t=De(t,u),s!=null)throw new Oe("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=ie(ie(r,"bool"),"float32"),r.rank===l-1&&(r=tn(r,-1)),r=De(r,u)),a&&(t=ba(t,0),r!=null&&(r=ba(r,0)));let p=[],d,c=n,h=t.shape[0],m=pt(t),f;r!=null&&(f=pt(r));for(let b=0;be(y,c));if(r==null)d=x[0],c=x[1];else{let w=P(()=>{let I=f[b],T=pe(na(I),I),C=X(z(x[0],I),z(c[0],T)),E=c.map((F,D)=>X(z(x[1][D],I),z(F,T)));return{output:C,newStates:E}});d=w.output,c=w.newStates}o&&p.push(d)}let g;return o&&(g=Dt(p,1)),[d,g,c]})}var mr=class extends Be{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Af({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("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 Bt({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 qa(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 P(()=>{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;ns.shape[s.shape.length-1]),r))throw new V(`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=r.map(s=>new Bt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){P(()=>{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 V("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=>Nt([n,a])):this.states_=[Nt([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>Nt([n,a])):this.states_[0]=Nt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let a=0;aHt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=O2(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 Bt({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 Va){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let p=super.apply(o,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return P(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Te(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 V(`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=L2((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],p=o[2];this.stateful&&this.resetStates(p,a);let d=this.returnSequences?u:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return P(()=>{let t=Nt(e.shape);return t=fe(t,[1,2]),t=dd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Cx(t,[1,n]):t):this.cell.stateSize>1?[Cx(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()===mr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),e),t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ga(a,n);return new e(Object.assign(t,{cell:r}))}};mr.className="RNN";ne.registerClass(mr);var vd=class extends Be{},_f=class extends vd{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,nn(this.units,"units"),this.activation=ds(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Pl([1,ps([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Pl([1,ps([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Ze(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 P(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0na(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0na(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=or(z(e,s),this.kernel.read()):r=or(e,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,or(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:cs(this.activation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),recurrentInitializer:At(this.recurrentInitializer),biasInitializer:At(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};_f.className="SimpleRNNCell";ne.registerClass(_f);var k0=class extends mr{constructor(e){e.cell=new _f(e),super(e)}call(e,t){return P(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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)}};k0.className="SimpleRNN";ne.registerClass(k0);var Ef=class extends vd{constructor(e){if(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",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,nn(this.units,"units"),this.activation=ds(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ds(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Pl([1,ps([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Pl([1,ps([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Ze(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 P(()=>{if(e=e,e.length!==2)throw new V(`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],0na(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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)}};I0.className="GRU";ne.registerClass(I0);var wd=class extends vd{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,nn(this.units,"units"),this.activation=ds(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ds(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Pl([1,ps([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Pl([1,ps([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Ze(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 Ra{apply(i,o){let l=r.apply([s]),u=new bf().apply([s]),p=r.apply([s*2]);return eI(eI(l,u),p)}},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 P(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0na(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};S0.className="LSTM";ne.registerClass(S0);var Af=class extends vd{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return P(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{ai(`RNNCell_${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(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ga(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 Ax(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):YT(t(),n),o=()=>md(i,t,a);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var mH=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{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("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 P(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=Nt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){P(()=>{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 V("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(()=>Nt(r)):this.states_=[Nt(r)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Nt(r)):this.states_[0]=Nt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;sHt(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],u=e[o?4:3],p=Ha(l,a[0],r,s[0],i[0]),d=Ha(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,p,d]:[p,d,n]]}};z2.className="ConvRNN2D";var Ff=class extends wd{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,nn(this.filters,"filters"),this.kernelSize=Il(n,2,"kernelSize"),this.kernelSize.forEach(o=>nn(o,"kernelSize")),this.strides=Il(a||1,2,"strides"),this.strides.forEach(o=>nn(o,"strides")),this.padding=r||"valid",va(this.padding),this.dataFormat=s||"channelsLast",Pt(this.dataFormat),this.dilationRate=Il(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>nn(o,"dilationRate"))}build(e){var t;e=Ze(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`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,u=this.filters;o=new(t=class extends Ra{apply(p,d){let c=l.apply([u]),h=Jn([u]),m=l.apply([u*2]);return Bw([c,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 P(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0na(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0na(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,w,I,T]=zn(this.kernel.read(),i,y),[C,E,F,D]=this.useBias?zn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,w,E,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,T,D,this.padding);let[$,S,M,B]=zn(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),b=this.recurrentConv(b,B);let U=this.recurrentActivation.apply(X(u,m)),H=this.recurrentActivation.apply(X(p,f)),j=X(z(H,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,b)),this.activation.apply(j));return[K,K,j]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=mH(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=Rt(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ya(r,n,this.dataFormat):r}recurrentConv(e,t){return Rt(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Ff.className="ConvLSTM2DCell";ne.registerClass(Ff);var N0=class extends z2{constructor(e){let t=new Ff(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};N0.className="ConvLSTM2D";ne.registerClass(N0);var $f=class extends Be{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a{this.invokeCallHook(e,t);let n=Te(e);if(0YT(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};$f.className="Dropout";ne.registerClass($f);var T0=class extends $f{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};T0.className="SpatialDropout1D";ne.registerClass(T0);var C0=class extends Be{constructor(e){if(super(e),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,nn(this.units,"units"),this.activation=ds(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Ct(e.kernelRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.activityRegularizer=Ct(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Ze(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=Ze(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Te(e),a=UT(this.activation.getClassName()),r;return a!=null?r=or(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=or(n,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:cs(this.activation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),biasInitializer:At(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};C0.className="Dense";ne.registerClass(C0);var _0=class extends Be{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Ze(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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Be{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 P(()=>{this.invokeCallHook(e,t);let n=Te(e);return md(()=>X(gf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};V0.className="GaussianNoise";ne.registerClass(V0);var U0=class extends Be{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 P(()=>{this.invokeCallHook(e,t);let n=Te(e);return this.rate>0&&this.rate<1?md(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return z(n,gf(n.shape,1,a))},()=>n,t.training||!1):n})}};U0.className="GaussianDropout";ne.registerClass(U0);var G0=class extends Be{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Bt({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 P(()=>{let n=t.training==null?!1:t.training,a=Te(e),r=a.shape,s=r.length,i=qa(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=gi(1,s);l[o]=r[o];let u=i.slice();u.sort();let p=!v.arraysEqual(u,qa(0,s).slice(0,s-1)),d=()=>{if(p){let 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e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:At(this.betaInitializer),gammaInitializer:At(this.gammaInitializer),movingMeanInitializer:At(this.movingMeanInitializer),movingVarianceInitializer:At(this.movingVarianceInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer),betaConstraint:Xt(this.betaConstraint),gammaConstraint:Xt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};H0.className="BatchNormalization";ne.registerClass(H0);var q0=class extends Be{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 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n=Te(e),a=n.shape,r=a.length;return P(()=>{let{mean:s,variance:i}=rd(n,this.axis,!0),o=gi(1,r);for(let h of this.axis)o[h]=a[h];let l=h=>h!=null&&h.shape.length!==r?W(h,o):h,u=this.scale?l(this.gamma.read()):null,p=this.center?l(this.beta.read()):null,d=[],c=[];for(let h=0;h{if(e.rank!==4)throw new V(`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 V("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ja()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. 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s==="max"?i=Mt(e,t,n,o):i=ya(e,t,n,o),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}function W2(e,t,n,a,r,s){return P(()=>{Pt(r),HT(s),va(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=ja()),s==null&&(s="max"),e=M2(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=sw(e,t,n,o):i=Fv(e,t,n,o),r==="channelsFirst"&&(i=De(i,[0,4,1,2,3])),i})}var B2=class extends Be{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 V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(nn(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 V(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);nn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,va(this.padding),this.inputSpec=[new Bt({ndim:3})]}computeOutputShape(e){e=Ze(e);let t=Ha(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return P(()=>{this.invokeCallHook(e,t),e=dd(Te(e),2);let n=this.poolingFunction(Te(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Cs(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},K0=class extends B2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),Df(e,t,n,a,r,"max")}};K0.className="MaxPooling1D";ne.registerClass(K0);var X0=class extends B2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),Df(e,t,n,a,r,"avg")}};X0.className="AveragePooling1D";ne.registerClass(X0);var V2=class extends Be{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 V(`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];nn(this.poolSize,"poolSize"),nn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),va(this.padding),this.inputSpec=[new Bt({ndim:4})]}computeOutputShape(e){e=Ze(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(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 P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(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}},Y0=class extends V2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),Df(e,t,n,a,r,"max")}};Y0.className="MaxPooling2D";ne.registerClass(Y0);var Z0=class extends V2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),Df(e,t,n,a,r,"avg")}};Z0.className="AveragePooling2D";ne.registerClass(Z0);var U2=class extends Be{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 V(`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];nn(this.poolSize,"poolSize"),nn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),va(this.padding),this.inputSpec=[new Bt({ndim:5})]}computeOutputShape(e){e=Ze(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=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(n,this.poolSize[1],this.padding,this.strides[1]),a=Ha(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 P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(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}},J0=class extends U2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),W2(e,t,n,a,r,"max")}};J0.className="MaxPooling3D";ne.registerClass(J0);var Q0=class extends U2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),W2(e,t,n,a,r,"avg")}};Q0.className="AveragePooling3D";ne.registerClass(Q0);var G2=class extends Be{constructor(e){super(e),this.inputSpec=[new Bt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Ga(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?wH:e.mergeMode,vH(this.mergeMode),e.weights)throw new Oe("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()):On(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=O2(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 V("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(p=>new 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i;return this.mergeMode==="concat"?i=Bw([a,r]):this.mergeMode==="sum"?i=X(a,r):this.mergeMode==="ave"?i=z(.5,X(a,r)):this.mergeMode==="mul"?i=z(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){ai(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),ai(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 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 n=Ga(t.layer);if(delete t.layer,t.numConstants!=null)throw new Oe("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};s1.className="Bidirectional";ne.registerClass(s1);var i1=class extends Be{constructor(e){super(e),this.scale=e.scale,e.offset?this.offset=e.offset:this.offset=0}getConfig(){let e={scale:this.scale,offset:this.offset},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return P(()=>(e=Te(e),e.dtype!=="float32"&&(e=ir(e,"float32")),X(z(e,this.scale),this.offset)))}};i1.className="Rescaling";ne.registerClass(i1);var{resizeBilinear:kH,cropAndResize:IH}=Qn,o1=class extends Be{constructor(e){super(e),this.height=e.height,this.width=e.width}centerCrop(e,t,n,a,r,s,i,o){return P(()=>{let l,u=!1,p=t/s,d=n/i,c=(a+t)/s,h=(r+n)/i,m=[p,d,c,h],f=[];e.rank===3?(u=!0,l=Dt([e])):l=e;for(let x=0;x{let r=kH(e,[t,n]);return ir(r,a)})}call(e,t){return P(()=>{let n=Te(e),a=n.dtype,r=n.shape,s=r[r.length-3],i=r[r.length-2],o=0;s!==this.height&&(o=Math.floor((s-this.height)/2));let l=0;return i!==this.width&&(l=Math.floor((i-this.width)/2),l===0&&(l=1)),o>=0&&l>=0?this.centerCrop(n,o,l,this.height,this.width,s,i,a):this.upsize(e,this.height,this.width,a)})}getConfig(){let e={height:this.height,width:this.width},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){e=Ze(e);let t=e.length-3,n=e.length-2;return e[t]=this.height,e[n]=this.width,e}};o1.className="CenterCrop";ne.registerClass(o1);function SH(e,t,n,a){let r=Te(e);if(r.dtype!=="int32"&&(r=ir(r,"int32")),t==="int")return r;let s=r.shape;if(r.rank===0&&(r=tn(r,-1)),t==="oneHot"&&r.shape[r.shape.length-1]!==1&&(r=tn(r,-1)),r.rank>2)throw new V(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${r.rank}.`);let i=["multiHot","oneHot"].includes(t),o=r,l;if(typeof a!="undefined"&&t==="count"?l=qh(o,a,n,i):l=qh(o,[],n,i),t!=="tfIdf")return l;if(a)return z(l,a);throw new V("When outputMode is 'tfIdf', weights must be provided.")}var l1=class extends Be{constructor(e){super(e),this.numTokens=e.numTokens,e.outputMode?this.outputMode=e.outputMode:this.outputMode="multiHot"}getConfig(){let e={numTokens:this.numTokens,outputMode:this.outputMode},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){return e=Ze(e),e==null?[this.numTokens]:this.outputMode==="oneHot"&&e[e.length-1]!==1?(e.push(this.numTokens),e):(e[e.length-1]=this.numTokens,e)}call(e,t){return P(()=>{e=Te(e),e.dtype!=="int32"&&(e=ir(e,"int32"));let n;if(typeof t.countWeights!="undefined"){if(this.outputMode!=="count")throw new V(`countWeights is not used when outputMode !== count. + Received countWeights=${t.countWeights}`);n=Te(t.countWeights)}let a=fa(e),r=_l(e),s=Cn(this.numTokens,a).bufferSync().get(0),i=Dr(r,0).bufferSync().get(0);if(!(s&&i))throw new V(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return SH(e,this.outputMode,this.numTokens,n)})}};l1.className="CategoryEncoding";ne.registerClass(l1);var NH=["bilinear","nearest"],bI=new Set(NH),u1=class extends Be{constructor(e){if(super(e),this.height=e.height,this.width=e.width,e.interpolation)if(bI.has(e.interpolation))this.interpolation=e.interpolation;else throw new V(`Invalid interpolation parameter: ${e.interpolation} is not implemented`);else this.interpolation="bilinear";this.cropToAspectRatio=!!e.cropToAspectRatio}computeOutputShape(e){e=Ze(e);let t=e[2];return[this.height,this.width,t]}getConfig(){let e={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return P(()=>{let n=[this.height,this.width];if(this.interpolation==="bilinear")return Qn.resizeBilinear(e,n,!this.cropToAspectRatio);if(this.interpolation==="nearest")return Qn.resizeNearestNeighbor(e,n,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...bI]} are supported`)})}};u1.className="Resizing";ne.registerClass(u1);var j2=class{constructor(e){this.seed=e}next(){if(this.seed!==void 0)return this.seed++}};j2.className="RandomSeed";var K2=class extends Be{constructor(e){super(e),this.randomGenerator=new j2(e.seed)}getConfig(){let 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TypeError(`Node type ${e.op} is not implemented`)}},$q=(e,t,n,a=rn)=>{switch(e.op){case"EuclideanNorm":return[a.euclideanNorm(k("x",e,t,n),k("axis",e,t,n),k("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[a.localResponseNormalization(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[a.softmax(k("x",e,t,n))];case"LogSoftmax":return[a.logSoftmax(k("x",e,t,n))];case"SparseToDense":return[a.sparseToDense(k("sparseIndices",e,t,n),k("outputShape",e,t,n),k("sparseValues",e,t,n),k("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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implemented`)}},Pq=(e,t,n,a=rn)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=a.sparse.sparseFillEmptyRows(k("indices",e,t,n),k("values",e,t,n),k("denseShape",e,t,n),k("defaultValue",e,t,n));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=a.sparse.sparseReshape(k("inputIndices",e,t,n),k("inputShape",e,t,n),k("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[a.sparse.sparseSegmentMean(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];case"SparseSegmentSum":return[a.sparse.sparseSegmentSum(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Oq=(e,t,n,a=rn)=>{switch(e.op){case"FFT":return[a.fft(k("x",e,t,n))];case"IFFT":return[a.ifft(k("x",e,t,n))];case"RFFT":return[a.rfft(k("x",e,t,n))];case"IRFFT":return[a.irfft(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Lq=(e,t,n,a=rn)=>{switch(e.op){case"StaticRegexReplace":return[a.string.staticRegexReplace(k("input",e,t,n),k("pattern",e,t,n),k("rewrite",e,t,n),k("replaceGlobal",e,t,n))];case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=a.string.stringNGrams(k("data",e,t,n),k("dataSplits",e,t,n),k("separator",e,t,n),k("nGramWidths",e,t,n),k("leftPad",e,t,n),k("rightPad",e,t,n),k("padWidth",e,t,n),k("preserveShortSequences",e,t,n));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=a.string.stringSplit(k("input",e,t,n),k("delimiter",e,t,n),k("skipEmpty",e,t,n));return[r,s,i]}case"StringToHashBucketFast":return[a.string.stringToHashBucketFast(k("input",e,t,n),k("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},zq=(e,t,n,a=rn)=>{switch(e.op){case"Cast":return[a.cast(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[a.expandDims(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[a.squeeze(k("x",e,t,n),r)]}case"Reshape":return[a.reshape(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[a.mirrorPad(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[a.pad(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),s=k("paddings",e,t,n);return[a.spaceToBatchND(k("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),s=k("crops",e,t,n);return[a.batchToSpaceND(k("x",e,t,n),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),s=k("dataFormat",e,t,n).toUpperCase();return[a.depthToSpace(k("x",e,t,n),r,s)]}case"BroadcastTo":return[a.broadcastTo(k("x",e,t,n),k("shape",e,t,n))];case"BroadcastArgs":return[a.broadcastArgs(k("s0",e,t,n),k("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function II(e,t,n,a,r=P){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>mq(i,o,l));case"basic_math":return r(()=>fq(i,o,l));case"control":return wq(i,o,l);case"convolution":return r(()=>kq(i,o,l));case"creation":return r(()=>Iq(i,o,l));case"dynamic":return Sq(i,o,l);case"evaluation":return r(()=>Nq(i,o,l));case"image":return r(()=>Eq(i,o,l));case"graph":return r(()=>Tq(i,o,l));case"logical":return r(()=>Aq(i,o,l));case"matrices":return r(()=>Fq(i,o,l));case"normalization":return r(()=>$q(i,o,l));case"ragged":return r(()=>Dq(i,o,l));case"reduction":return r(()=>Rq(i,o,l));case"slice_join":return r(()=>Mq(i,o,l));case"sparse":return r(()=>Pq(i,o,l));case"spectral":return r(()=>Oq(i,o,l));case"string":return r(()=>Lq(i,o,l));case"transformation":return r(()=>zq(i,o,l));case"hash_table":return _q(i,o,l,a);case"custom":let u=K2(i.op);if(u&&u.customExecutor)return u.customExecutor(new hq(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var SI=class{constructor(e={},t={},n={},a={},r){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.parseNodeNameCache=r,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;tt.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 NI(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(c=>Yn(c)[0]));a=a||[];let p=new Set(a.map(c=>Yn(c.name)[0])),d=[...t];for(;d.length>0;){let c=d.pop();if((yl(c)||jq(c)||Kq(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&!u.has(c.name)&&!p.has(c.name)){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function Wq(e,t){let{usedNodes:n,inputs:a}=t,r=Object.keys(a).map(g=>Yn(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>n.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(b=>[b.name,b])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),d={};for(let g of u){d[g.name]=d[g.name]||0;for(let b of g.children)i(b)||(d[b.name]=Number.POSITIVE_INFINITY),d[b.name]=(d[b.name]||0)+1}let c=Object.entries(d).filter(([,g])=>g===0).map(([g])=>g),h=[...c];for(;c.length>0;){let g=c.pop(),b=p.get(g);for(let y of b.children.filter(i))--d[y.name]===0&&(h.push(y.name),c.push(y.name))}let m=h.map(g=>p.get(g)),f=Bq(m,l);return Vq(f,l),f}function Bq(e,t){let n=new Map(e.map(s=>[s.name,s])),a=t.map(s=>s.name),r=new Set(a);for(;a.length>0;){let s=a.pop(),i=n.get(s);for(let o of i.children)!n.has(o.name)||r.has(o.name)||(r.add(o.name),a.push(o.name))}return e.filter(s=>r.has(s.name))}var wh=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function Vq(e,t){let n=new Map(e.map((o,l)=>[o.name,l])),a=new Set(t.map(o=>o.name)),r=o=>a.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!n.has(l.name))throw new wh(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new wh(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!n.has(l.name))throw new wh(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new wh(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function Uq(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>yl(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;oe[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Hx(e.functions[n],this)})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPARATOR)+"--"+a.join(this.SEPARATOR)}compile(e,t){let n=NI(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${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 l=t.map(p=>p.name),u=Object.keys(e);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${u}]. Missing the following inputs: [${a}]`)}let i=Wq(this.graph,n),o=Uq(i);return{orderedNodes:i,nodeLiveUntilMap:o}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return Ht(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,n])=>[t,this.cloneTensorList(n)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(d=>this.graph.nodes[Yn(d)[0]]),r=t.map(d=>Yn(d)[0]),s=new Set(r),i=r.map(d=>this.graph.nodes[d]);i.length===0&&(i=this._outputs);let o=this.getCompilationKey(a,i),l=this.compiledMap.get(o);l==null&&(l=this.compile(e,i),this.compiledMap.set(o,l));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let u={},p={};return P(()=>{let d=new SI(this.weightMap,u,p,this.functionExecutorMap,this.parseNodeNameCache),c=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(g=>{let[b,y]=Yn(g,d),x=[];x[y]=e[g],c[b]=x,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(x))});let h=this.getFrozenTensorIds(c),{orderedNodes:m,nodeLiveUntilMap:f}=l;for(let g of m){if(c[g.name])continue;let b=II(g,c,d,this._resourceManager);if(v.isPromise(b))throw new Error(`The execution of the op '${g.op}' returned a promise. Please use model.executeAsync() instead.`);c[g.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(g,c,d,h,s,f.get(g.name))}return this.parent==null&&d.dispose(h),t.map(g=>cn(g,c,d))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,n,a,r,s,i){if(!(yl(t)||s.has(e))){for(let o of n[e])o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length);for(let o of t.inputs){if(yl(o))continue;let l=yI(o.name,n,a);if(l!=null)for(let u of l){if(!u||u.kept||r.has(u.id))continue;let p=i[u.id];p===1?(u.dispose(),delete i[u.id]):p!=null&&i[u.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,n,a,r,s){if(!(yl(e)||r.has(e.name))&&s!=null)for(let i of s){let o=yI(i,t,n);for(let l of o)!l||l.kept||a.has(l.id)||l.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,n=!1,a={},r={}){this.disposeIntermediateTensors(),n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let s=new SI(this.weightMap,a,r,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,s,t,n),o=t.map(d=>cn(d,i,s)),l=o.map(d=>d.id),u=Object.keys(e).map(d=>e[d].id),p=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach(d=>{d.forEach(c=>{c&&!c.isDisposed&&!p.has(c.id)&&c.dispose()})}),this.parent==null&&s.dispose(p),o}async executeFunctionAsync(e,t,n){let a=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(a,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,a){let r=Object.keys(e),s=r.map(x=>this.graph.nodes[Yn(x)[0]]),i=n.map(x=>Yn(x)[0]),o=new Set(i),l=i.map(x=>this.graph.nodes[x]);l.length===0&&(l=this._outputs);let{usedNodes:u,missingInputs:p,dynamicNode:d,syncInputs:c}=NI(e,l,this.weightMap,this._initNodes),h=[...s,...this.graph.weights,...this._initNodes||[]].map(x=>({node:x,contexts:t.currentContext})),m=Object.assign({},this.weightMap);Object.keys(e).forEach(x=>{let[w,I]=Yn(x),T=[];T[I]=e[x],m[w]=T});let f={},g=this.getFrozenTensorIds(m),b={};for(;h.length>0;){let x=this.processStack(s,h,t,m,b,g,o,f,u);await Promise.all(x)}d==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=l.filter(x=>!yl(x)&&!cn(x.name,m,t)).map(x=>x.name);if(y.length>0){let x="";throw d!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${c}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${p}]. ${x}`)}return m}processStack(e,t,n,a,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();n.currentContext=p.contexts;let d="";if(p.node.op==="Enter"&&k("isConstant",p.node,a,n)&&([d]=Nr(p.node.name,n)),a[p.node.name]==null){let c=II(p.node,a,n,this._resourceManager);d||([d]=Nr(p.node.name,n));let h=n.currentContext;v.isPromise(c)?u.push(c.then(m=>(a[d]=m,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(m)),n.currentContext=h,this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l),m))):(a[d]=c,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(c)),this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l))}else this.processChildNodes(p.node,t,n,a,r,l)}return u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Nr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!cn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!cn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Yn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.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 ${n.dtype}`)})}mapInputs(e){var t,n;let a={};for(let r in e){let s=(n=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||n===void 0?void 0:n[r];s!=null?a[s.name]=e[r]:a[r]=e[r]}return a}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Yn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var n,a;let r=(a=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||a===void 0?void 0:a[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[n]=Yn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Xq=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Yq="?tfjs-format=file",Zq="model.json",m1=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},n=qt){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new Xq}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(n=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Hx(xI.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=xI.Instance.transformGraph(e.modelInitializer);this.initializer=new Hx(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof Te?[e]:e,n={};return t.forEach((a,r)=>n[this.structuredOutputKeys[r]]=a),n}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof Te)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let a=0;a1?n:n[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&_e(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function Jq(e,t={},n=qt){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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tj="4.3.0",gC={};Ee(gC,{CSVDataset:()=>NC,Dataset:()=>rp,FileDataSource:()=>$C,TextLineDataset:()=>SC,URLDataSource:()=>DC,array:()=>Sj,csv:()=>Mj,func:()=>Pj,generator:()=>Oj,microphone:()=>zj,version_data:()=>Wj,webcam:()=>Lj,zip:()=>Nj});var nj=fs(hm()),aj=fs(hm());function rj(e,t){return am(e,t)}function am(e,t,n=new Map,a=new Set){if(e==null)return null;if(typeof Blob=="function"&&e instanceof Blob)return e.slice();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(zl(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),e.__proto__&&(s.__proto__=e.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function sj(e,t=yC){return bC(e,t)}function bC(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(zl(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=bC(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 yC(e){return e===null?null:zl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function xC(e,t){let n=new Map;am(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(v.isPromise(r)){let s=await r;n.set(a,s)}}return am(e,t,n)}function zl(e){let t=!1;if(G().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=pS();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Te)&&!(e instanceof Promise)&&!t)}function ij(e){return e==null||oj(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Te||v.isTypedArray(e)}function oj(e){return e===null||typeof e!="object"&&typeof e!="function"}function lj(e){return rj(e,uj)}function uj(e){return e instanceof Te?{value:e.clone(),recurse:!1}:zl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var vC=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}},f1=class extends vC{constructor(){super(f1.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;at===!0)}rowMajorBatch(e,t=!0){return new bj(this,e,t)}columnMajorBatch(e,t=!0,n=yC){return this.rowMajorBatch(e,t).map(a=>sj(a,n))}concatenate(e,t){return new kC(wC([this,e]),t)}take(e){return e<0||e==null?this:new gj(this,e)}skip(e){return e<0||e==null?this:new fj(this,e)}prefetch(e){return new IC(this,e)}shuffle(e,t){return new Ij(this,e,t)}serial(){return new mj(this)}},dj=class extends an{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:lj(e),done:!1}}},hj=class extends an{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: ${e.message}`,e}}},mj=class extends an{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},fj=class extends an{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},bj=class extends an{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},yj=class extends an{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;_e(e.value)}}},xj=class extends an{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},vj=class extends an{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},TI=class extends an{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},b1=class extends an{constructor(){super(),this.outputQueue=new f1,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}}},wj=class extends b1{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return!0}},kC=class extends an{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries 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xC(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Qr.FAIL:throw new Error(`Zipped streams should have the same length. 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If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=nj.alea(t||v.now().toString());return Xn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Xn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};rp.MAX_BUFFER_SIZE=1e4;function Xn(e,t=null){return new class extends rp{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function Sj(e){return Xn(async()=>wC(e),e.length)}function Nj(e){if(!zl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await xC(e,a=>{if(a instanceof rp)return{value:a.iterator(),recurse:!1};if(zl(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return cj(n,Qr.SHORTEST)},t)}function Tj(e){if(e===null)return null;let t=e[0];return ij(t)?{value:Cj(e),recurse:!1}:{value:null,recurse:!0}}function Cj(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Te?Dt(e):bn(e)}var SC=class extends rp{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` -`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},kh='"',jp=Symbol("out"),CI=Symbol("field"),Ih=Symbol("quote"),ix=Symbol("quoteafterquote"),_I=Symbol("quoteinquote"),NC=class extends rp{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&&v.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(v.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}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 SC(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.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 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;r14||!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_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new TC(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]===-1/0&&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(v.sizeFromShape(t));return n.set(e,n.length-e.length),bn(n,t)}},CC=class extends an{constructor(e,t){if(super(),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=je([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=Aa([s,r,o,i],[1,4])}else this.cropBox=Aa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!G().get("IS_BROWSER"))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 CC(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Wo.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 P(()=>{let t=Qt(ie(e,"float32"),0),n;n=Ea.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return W(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.")}},_C=class{},EC=class extends an{split(e){return new _j(this,e)}},_j=class extends 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_C{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return FC(this.url)?new $C(this.url,this.fileOptions).iterator():Dj(this.url,this.fileOptions)}};function Mj(e,t={}){return new NC(new DC(e),t)}function Pj(e){let t=g1(e);return Xn(async()=>t)}function Oj(e){return Xn(async()=>{let t=await e();return g1(()=>t.next())})}async function Lj(e,t){return CC.create(e,t)}async function zj(e){return TC.create(e)}var Wj="4.3.0";function ge(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var Bj=hr.whereImpl,Df=class extends Tc{nextDataId(){return Df.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new mm(this,Ca())}write(e,t,n){this.firstUse&&(this.firstUse=!1,G().get("IS_NODE")&&N.warn(` + ${a}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=Hx(s,n),o=a===0?0:e.size/a,l=P(()=>{let p=[];e=W(e,[1,a,o]);for(let 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TypeError(`Node type ${e.op} is not implemented`)}},Yq=(e,t,n,a=on)=>{switch(e.op){case"EuclideanNorm":return[a.euclideanNorm(k("x",e,t,n),k("axis",e,t,n),k("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[a.localResponseNormalization(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[a.softmax(k("x",e,t,n))];case"LogSoftmax":return[a.logSoftmax(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Zq=(e,t,n,a=on)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:r,outputDenseValues:s}=a.raggedGather(k("paramsNestedSplits",e,t,n),k("paramsDenseValues",e,t,n),k("indices",e,t,n),k("outputRaggedRank",e,t,n));return r.concat(s)}case"RaggedRange":{let{rtNestedSplits:r,rtDenseValues:s}=a.raggedRange(k("starts",e,t,n),k("limits",e,t,n),k("splits",e,t,n));return[r,s]}case"RaggedTensorToTensor":return[a.raggedTensorToTensor(k("shape",e,t,n),k("values",e,t,n),k("defaultValue",e,t,n),k("rowPartitionTensors",e,t,n),k("rowPartitionTypes",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Jq=(e,t,n,a=on)=>{switch(e.op){case"Max":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.max(k("x",e,t,n),o,l)]}case"Mean":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.mean(k("x",e,t,n),o,l)]}case"Min":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.min(k("x",e,t,n),o,l)]}case"Sum":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.sum(k("x",e,t,n),o,l)]}case"All":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.all(k("x",e,t,n),o,l)]}case"Any":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.any(k("x",e,t,n),o,l)]}case"ArgMax":{let o=k("axis",e,t,n);return[a.argMax(k("x",e,t,n),o)]}case"ArgMin":{let o=k("axis",e,t,n);return[a.argMin(k("x",e,t,n),o)]}case"Prod":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.prod(k("x",e,t,n),o,l)]}case"Cumprod":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumprod(k("x",e,t,n),o,l,u)]}case"Cumsum":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumsum(k("x",e,t,n),o,l,u)]}case"Bincount":let r=k("x",e,t,n),s=k("weights",e,t,n),i=k("size",e,t,n);return[a.bincount(r,s,i)];case"DenseBincount":{let o=k("x",e,t,n),l=k("weights",e,t,n),u=k("size",e,t,n),p=k("binaryOutput",e,t,n);return[a.denseBincount(o,l,u,p)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Qq=(e,t,n,a=on)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),s=k("axis",e,t,n),i=k("tensors",e,t,n);return i=i.slice(0,r),[a.concat(i,s)]}case"Gather":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gather(r,a.cast(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),s=k("batchDims",e,t,n),i=k("x",e,t,n),o=k("indices",e,t,n);return[a.gather(i,a.cast(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,n),s=[];for(let o=0;o{let r=k("axis",e,t,n),s=k("tensors",e,t,n),i=s[0].shape,o=a.squeeze(s[0]).shape,l=s.map(u=>{let p=v.arraysEqual(u.shape,i);if(!p&&!v.arraysEqual(a.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:a.reshape(u,i)});return[a.stack(l,r)]});case"Unpack":{let r=k("axis",e,t,n),s=k("tensor",e,t,n);return a.unstack(s,r)}case"Tile":{let r=k("reps",e,t,n);return[a.tile(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),s=k("numOrSizeSplits",e,t,n),i=k("x",e,t,n);return a.split(i,s,r)}case"ScatterNd":{let r=k("indices",e,t,n),s=k("values",e,t,n),i=k("shape",e,t,n);return[a.scatterND(r,s,i)]}case"GatherNd":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gatherND(r,s)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),s=k("outputShape",e,t,n),i=k("sparseValues",e,t,n),o=k("defaultValue",e,t,n);return[a.sparseToDense(r,i,s,i.dtype===o.dtype?o:a.cast(o,i.dtype))]}case"TensorScatterUpdate":{let r=k("indices",e,t,n),s=k("values",e,t,n),i=k("tensor",e,t,n);return[a.tensorScatterUpdate(i,r,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},ej=(e,t,n,a=on)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=a.sparse.sparseFillEmptyRows(k("indices",e,t,n),k("values",e,t,n),k("denseShape",e,t,n),k("defaultValue",e,t,n));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=a.sparse.sparseReshape(k("inputIndices",e,t,n),k("inputShape",e,t,n),k("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[a.sparse.sparseSegmentMean(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];case"SparseSegmentSum":return[a.sparse.sparseSegmentSum(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},tj=(e,t,n,a=on)=>{switch(e.op){case"FFT":return[a.fft(k("x",e,t,n))];case"IFFT":return[a.ifft(k("x",e,t,n))];case"RFFT":return[a.rfft(k("x",e,t,n))];case"IRFFT":return[a.irfft(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},nj=(e,t,n,a=on)=>{switch(e.op){case"StaticRegexReplace":return[a.string.staticRegexReplace(k("input",e,t,n),k("pattern",e,t,n),k("rewrite",e,t,n),k("replaceGlobal",e,t,n))];case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=a.string.stringNGrams(k("data",e,t,n),k("dataSplits",e,t,n),k("separator",e,t,n),k("nGramWidths",e,t,n),k("leftPad",e,t,n),k("rightPad",e,t,n),k("padWidth",e,t,n),k("preserveShortSequences",e,t,n));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=a.string.stringSplit(k("input",e,t,n),k("delimiter",e,t,n),k("skipEmpty",e,t,n));return[r,s,i]}case"StringToHashBucketFast":return[a.string.stringToHashBucketFast(k("input",e,t,n),k("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},aj=(e,t,n,a=on)=>{switch(e.op){case"Cast":return[a.cast(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[a.expandDims(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[a.squeeze(k("x",e,t,n),r)]}case"Reshape":return[a.reshape(k("x",e,t,n),k("shape",e,t,n))];case"EnsureShape":return[a.ensureShape(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[a.mirrorPad(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[a.pad(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),s=k("paddings",e,t,n);return[a.spaceToBatchND(k("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),s=k("crops",e,t,n);return[a.batchToSpaceND(k("x",e,t,n),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),s=k("dataFormat",e,t,n).toUpperCase();return[a.depthToSpace(k("x",e,t,n),r,s)]}case"BroadcastTo":return[a.broadcastTo(k("x",e,t,n),k("shape",e,t,n))];case"BroadcastArgs":return[a.broadcastArgs(k("s0",e,t,n),k("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function TI(e,t,n,a,r=P){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>$q(i,o,l));case"basic_math":return r(()=>Dq(i,o,l));case"control":return zq(i,o,l);case"convolution":return r(()=>Wq(i,o,l));case"creation":return r(()=>Bq(i,o,l));case"dynamic":return Vq(i,o,l);case"evaluation":return r(()=>Uq(i,o,l));case"image":return r(()=>jq(i,o,l));case"graph":return r(()=>Gq(i,o,l));case"logical":return r(()=>Kq(i,o,l));case"matrices":return r(()=>Xq(i,o,l));case"normalization":return r(()=>Yq(i,o,l));case"ragged":return r(()=>Zq(i,o,l));case"reduction":return r(()=>Jq(i,o,l));case"slice_join":return r(()=>Qq(i,o,l));case"sparse":return r(()=>ej(i,o,l));case"spectral":return r(()=>tj(i,o,l));case"string":return r(()=>nj(i,o,l));case"transformation":return r(()=>aj(i,o,l));case"hash_table":return qq(i,o,l,a);case"custom":let u=rC(i.op);if(u&&u.customExecutor)return u.customExecutor(new Fq(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var CI=class{constructor(e={},t={},n={},a={},r){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.parseNodeNameCache=r,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;tt.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 _I(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(c=>Yn(c)[0]));a=a||[];let p=new Set(a.map(c=>Yn(c.name)[0])),d=[...t];for(;d.length>0;){let c=d.pop();if((Zs(c)||cj(c)||dj(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&!u.has(c.name)&&!p.has(c.name)){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function rj(e,t){let{usedNodes:n,inputs:a}=t,r=Object.keys(a).map(g=>Yn(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>n.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(b=>[b.name,b])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),d={};for(let g of u){d[g.name]=d[g.name]||0;for(let b of g.children)i(b)||(d[b.name]=Number.POSITIVE_INFINITY),d[b.name]=(d[b.name]||0)+1}let c=Object.entries(d).filter(([,g])=>g===0).map(([g])=>g),h=[...c];for(;c.length>0;){let g=c.pop(),b=p.get(g);for(let y of b.children.filter(i))--d[y.name]===0&&(h.push(y.name),c.push(y.name))}let m=h.map(g=>p.get(g)),f=sj(m,l);return ij(f,l),f}function sj(e,t){let n=new Map(e.map(s=>[s.name,s])),a=t.map(s=>s.name),r=new Set(a);for(;a.length>0;){let s=a.pop(),i=n.get(s);for(let o of i.children)!n.has(o.name)||r.has(o.name)||(r.add(o.name),a.push(o.name))}return e.filter(s=>r.has(s.name))}var wh=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function ij(e,t){let n=new Map(e.map((o,l)=>[o.name,l])),a=new Set(t.map(o=>o.name)),r=o=>a.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!n.has(l.name))throw new wh(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new wh(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!n.has(l.name))throw new wh(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new wh(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function oj(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>Zs(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;oe[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new qx(e.functions[n],this)})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPARATOR)+"--"+a.join(this.SEPARATOR)}compile(e,t){let n=_I(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${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 l=t.map(p=>p.name),u=Object.keys(e);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${u}]. Missing the following inputs: [${a}]`)}let i=rj(this.graph,n),o=oj(i);return{orderedNodes:i,nodeLiveUntilMap:o}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return Ht(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,n])=>[t,this.cloneTensorList(n)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(d=>this.graph.nodes[Yn(d)[0]]),r=t.map(d=>Yn(d)[0]),s=new Set(r),i=r.map(d=>this.graph.nodes[d]);i.length===0&&(i=this._outputs);let o=this.getCompilationKey(a,i),l=this.compiledMap.get(o);l==null&&(l=this.compile(e,i),this.compiledMap.set(o,l));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let u={},p={};return P(()=>{let d=new CI(this.weightMap,u,p,this.functionExecutorMap,this.parseNodeNameCache),c=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(g=>{let[b,y]=Yn(g,d),x=[];x[y]=e[g],c[b]=x,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(x))});let h=this.getFrozenTensorIds(c),{orderedNodes:m,nodeLiveUntilMap:f}=l;for(let g of m){if(c[g.name])continue;let b=TI(g,c,d,this._resourceManager);if(v.isPromise(b))throw new Error(`The execution of the op '${g.op}' returned a promise. Please use model.executeAsync() instead.`);c[g.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(g,c,d,h,s,f.get(g.name))}return this.parent==null&&d.dispose(h),t.map(g=>cn(g,c,d))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,n,a,r,s,i){if(!(Zs(t)||s.has(e))){for(let o of n[e])o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length);for(let o of t.inputs){if(Zs(o))continue;let l=wI(o.name,n,a);if(l!=null)for(let u of l){if(!u||u.kept||r.has(u.id))continue;let p=i[u.id];p===1?(u.dispose(),delete i[u.id]):p!=null&&i[u.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,n,a,r,s){function i(o){return Zs(o)||r.has(o.name)}if(!(Zs(e)||s==null))for(let o of s){if(i(o))continue;let l=wI(o.name,t,n);for(let u of l)!u||u.kept||a.has(u.id)||u.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,n=!1,a={},r={}){this.disposeIntermediateTensors(),n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let s=new CI(this.weightMap,a,r,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,s,t,n),o=t.map(d=>cn(d,i,s)),l=o.map(d=>d.id),u=Object.keys(e).map(d=>e[d].id),p=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach(d=>{d.forEach(c=>{c&&!c.isDisposed&&!p.has(c.id)&&c.dispose()})}),this.parent==null&&s.dispose(p),o}async executeFunctionAsync(e,t,n){let a=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(a,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,a){let r=Object.keys(e),s=r.map(x=>this.graph.nodes[Yn(x)[0]]),i=n.map(x=>Yn(x)[0]),o=new Set(i),l=i.map(x=>this.graph.nodes[x]);l.length===0&&(l=this._outputs);let{usedNodes:u,missingInputs:p,dynamicNode:d,syncInputs:c}=_I(e,l,this.weightMap,this._initNodes),h=[...s,...this.graph.weights,...this._initNodes||[]].map(x=>({node:x,contexts:t.currentContext})),m=Object.assign({},this.weightMap);Object.keys(e).forEach(x=>{let[w,I]=Yn(x),T=[];T[I]=e[x],m[w]=T});let f={},g=this.getFrozenTensorIds(m),b={};for(;h.length>0;){let x=this.processStack(s,h,t,m,b,g,o,f,u);await Promise.all(x)}d==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=l.filter(x=>!Zs(x)&&!cn(x.name,m,t)).map(x=>x.name);if(y.length>0){let x="";throw d!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${c}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${p}]. ${x}`)}return m}processStack(e,t,n,a,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();n.currentContext=p.contexts;let d="";if(p.node.op==="Enter"&&k("isConstant",p.node,a,n)&&([d]=Nr(p.node.name,n)),a[p.node.name]==null){let c=TI(p.node,a,n,this._resourceManager);d||([d]=Nr(p.node.name,n));let h=n.currentContext;v.isPromise(c)?u.push(c.then(m=>(a[d]=m,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(m)),n.currentContext=h,this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l),m))):(a[d]=c,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(c)),this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l))}else this.processChildNodes(p.node,t,n,a,r,l)}return u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Nr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!cn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!cn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Yn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.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 ${n.dtype}`)})}mapInputs(e){var t,n;let a={};for(let r in e){let s=(n=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||n===void 0?void 0:n[r];s!=null?a[s.name]=e[r]:a[r]=e[r]}return a}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Yn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var n,a;let r=(a=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||a===void 0?void 0:a[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[n]=Yn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},hj=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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a=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new qx(kI.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=kI.Instance.transformGraph(e.modelInitializer);this.initializer=new qx(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof Ce?[e]:e,n={};return t.forEach((a,r)=>n[this.structuredOutputKeys[r]]=a),n}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof Ce)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let a=0;a1?n:n[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await 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xj="4.5.0",TC={};Ee(TC,{CSVDataset:()=>MC,Dataset:()=>rp,FileDataSource:()=>VC,TextLineDataset:()=>RC,URLDataSource:()=>UC,array:()=>Vj,csv:()=>Qj,func:()=>e5,generator:()=>t5,microphone:()=>a5,version_data:()=>r5,webcam:()=>n5,zip:()=>Uj});var vj=gs(hm()),wj=gs(hm());function kj(e,t){return am(e,t)}function am(e,t,n=new Map,a=new Set){if(e==null)return null;if(typeof Blob=="function"&&e instanceof Blob)return e.slice();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(Wl(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),e.__proto__&&(s.__proto__=e.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function Ij(e,t=_C){return CC(e,t)}function CC(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(Wl(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=CC(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 _C(e){return e===null?null:Wl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function EC(e,t){let n=new Map;am(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(v.isPromise(r)){let s=await r;n.set(a,s)}}return am(e,t,n)}function Wl(e){let t=!1;if(G().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=hS();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ce)&&!(e instanceof Promise)&&!t)}function Sj(e){return e==null||Nj(e)||Array.isArray(e)||typeof 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this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},$j=class extends sn{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Dj=class extends sn{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Mj=class extends sn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},Pj=class extends sn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;_e(e.value)}}},Oj=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},Lj=class extends sn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},EI=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},x1=class extends sn{constructor(){super(),this.outputQueue=new b1,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},zj=class extends x1{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return!0}},$C=class extends sn{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}},es;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(es||(es={}));var Wj=class extends sn{constructor(e,t=es.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 sn?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await EC(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case es.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case es.SHORTEST:return{value:null,done:!0};case es.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},DC=class extends sn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new AC(t)}summary(){return`${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()}},Bj=class extends DC{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=wj.alea(n||v.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}}},rp=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is + ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Xn(async()=>(await n.iterator()).columnMajorBatch(e,t,Gj),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Xn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Xn(async()=>(await t.iterator()).filter(a=>P(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Xn(async()=>(await t.iterator()).map(n=>P(()=>e(n))),this.size)}mapAsync(e){let t=this;return Xn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Xn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Xn(async()=>{let a=y1(async()=>({value:await t.iterator(),done:!1}));return _j(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=vj.alea(t||v.now().toString());return Xn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Xn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};rp.MAX_BUFFER_SIZE=1e4;function Xn(e,t=null){return new class extends rp{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function Vj(e){return Xn(async()=>FC(e),e.length)}function Uj(e){if(!Wl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await EC(e,a=>{if(a instanceof rp)return{value:a.iterator(),recurse:!1};if(Wl(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Ej(n,es.SHORTEST)},t)}function Gj(e){if(e===null)return null;let t=e[0];return Sj(t)?{value:Hj(e),recurse:!1}:{value:null,recurse:!0}}function Hj(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ce?Dt(e):bn(e)}var RC=class extends rp{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` +`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},kh='"',jp=Symbol("out"),AI=Symbol("field"),Ih=Symbol("quote"),ox=Symbol("quoteafterquote"),FI=Symbol("quoteinquote"),MC=class extends rp{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&&v.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(v.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}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 RC(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.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 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;r14||!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_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new PC(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]===-1/0&&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(v.sizeFromShape(t));return n.set(e,n.length-e.length),bn(n,t)}},OC=class extends sn{constructor(e,t){if(super(),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=je([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=Aa([s,r,o,i],[1,4])}else this.cropBox=Aa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!G().get("IS_BROWSER"))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 OC(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Vo.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return P(()=>{let t=tn(ie(e,"float32"),0),n;n=Qn.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return W(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},LC=class{},zC=class extends sn{split(e){return new qj(this,e)}},qj=class extends zC{constructor(e,t){super(),this.upstream=e,this.impl=new jj(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},jj=class extends x1{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Kj=class extends sn{decodeUTF8(){return new Xj(this)}},Xj=class extends zC{constructor(e){super(),this.upstream=e,this.impl=new Yj(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Yj=class extends x1{constructor(e){if(super(),this.upstream=e,G().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=hS();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return G().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},WC=class extends Kj{constructor(e,t={}){super(),this.file=e,this.options=t,v.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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof 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LC{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return BC(this.url)?new VC(this.url,this.fileOptions).iterator():Zj(this.url,this.fileOptions)}};function Qj(e,t={}){return new MC(new UC(e),t)}function e5(e){let t=y1(e);return Xn(async()=>t)}function t5(e){return Xn(async()=>{let t=await e();return y1(()=>t.next())})}async function n5(e,t){return OC.create(e,t)}async function a5(e){return PC.create(e)}var r5="4.5.0";function ge(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var s5=hr.whereImpl,Rf=class extends Tc{nextDataId(){return Rf.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new mm(this,_a())}write(e,t,n){this.firstUse&&(this.firstUse=!1,G().get("IS_NODE")&&N.warn(` ============================ Hi, looks like you are running TensorFlow.js in Node.js. 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p=N.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,b=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,w=p.effectiveFilterDepth,I=p.effectiveFilterHeight,T=p.effectiveFilterWidth,C=w-1-p.padInfo.front,E=T-1-p.padInfo.left,F=I-1-p.padInfo.top,D=Le(s.shape,"float32"),$=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M=p.outDepth||Math.floor(te)!==te))for(let re=0;re=p.outHeight||Math.floor(se)!==se))for(let ye=0;ye=p.outWidth||Math.floor(ue)!==ue)continue;let be=S.get(M,te,se,ue,B);ee+=be}}}D.set(ee*$,M,U,H,j,B)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var zK={kernelName:_c,backendName:"cpu",kernelFunc:LK};function WK(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,b=p.effectiveFilterHeight,y=p.effectiveFilterWidth,x=y-1-p.padInfo.left,w=b-1-p.padInfo.top,I=Le(i.shape,"float32"),T=1/(h*m),C=n.data.get(r.dataId).values,E=Le(r.shape,"float32",C);for(let F=0;F=p.outHeight||Math.floor(j)!==j))for(let K=0;K=p.outWidth||Math.floor(Z)!==Z)continue;let J=E.get(F,j,Z,D);U+=J}}I.set(U*T,F,$,S,D)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var BK={kernelName:bm,backendName:"cpu",kernelFunc:WK};function VK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((b,y)=>b*y),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=xt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Un({inputs:{x:h},backend:n,attrs:{perm:u}}),f=xt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=gi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var HK={kernelName:Zl,backendName:"cpu",kernelFunc:GK};function qK(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,u=v1(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var jK={kernelName:Jl,backendName:"cpu",kernelFunc:qK};function KK(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var XK={kernelName:Ec,backendName:"cpu",kernelFunc:KK},YK=it(ys,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(v.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 u=0;uf.shape);N.assertParamsConsistent(i,s);let o=N.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>v.sizeFromShape(f.shape)>0);if(l.length===1)return pr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(w=>fi({inputs:{input:w},backend:n})),g=l.map(w=>Bl({inputs:{input:w},backend:n})),b=Vl({inputs:f,backend:n,attrs:{axis:s}}),y=Vl({inputs:g,backend:n,attrs:{axis:s}}),x=Zn({inputs:{real:b,imag:y},backend:n});return f.forEach(w=>n.disposeIntermediateTensorInfo(w)),g.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),x}let u=l.map(f=>{let g=[-1,v.sizeFromShape(f.shape.slice(s))];return xt({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=N.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=w1(p,o,t[0].dtype,d),h=N.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var t8={kernelName:Ql,backendName:"cpu",kernelFunc:Vl};function __(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,b=c.padInfo.left,y=c.padInfo.top,x=c.dataFormat==="channelsLast",w=new Bt(c.outShape,r.dtype),I=v.computeStrides(r.shape),T=v.computeStrides(s.shape),C=I[0],E=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=w.strides[0],S=x?w.strides[1]:w.strides[2],M=x?w.strides[2]:1,B=x?1:w.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,j=w.values;for(let K=0;K=c.inHeight)continue;let ye=re*T[0],ue=Z+se*E;for(let be=0;be=c.inWidth)continue;let ht=ye+We*T[1],st=ue+Ge*F,tt=ht;for(let nt=0;nt=u.inDepth)continue;let K=H*F[0],Z=$+j*E[1];for(let J=0;J=u.inHeight)continue;let se=K+te*F[1],ye=Z+re*E[2];for(let ue=0;ue=u.inWidth)continue;let Ge=se+Se*F[2],ht=ye+We*u.inChannels,st=Ge;for(let tt=0;ttMath.cos(e)),m8={kernelName:Pi,backendName:"cpu",kernelFunc:h8},f8=it(Oi,e=>Math.cosh(e)),g8={kernelName:Oi,backendName:"cpu",kernelFunc:f8};function b8(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=Le([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,w=n.data.get(r.dataId).values,I=v.computeStrides(r.shape),T=v.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-F)*(d-1)/(f-1):0,U=g>1?(S-D)*(c-1)/(g-1):0;for(let H=0;H1?F*(d-1)+H*B:.5*(F+$)*(d-1);if(j<0||j>d-1){for(let K=0;K1?D*(c-1)+ee*U:.5*(D+S)*(c-1);if(ae<0||ae>c-1){for(let ye=0;ye1?D*(c-1)+K*U:.5*(D+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aeb+m-y-1:(b,y)=>b+y;for(let b=0;bb+m-y-1:(b,y)=>b+y;for(let b=0;b`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=N.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:b,padInfo:y}=h,x=y.left,w=y.top,I=h.outChannels/h.inChannels,T=new Bt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,E=n.data.get(s.dataId).values,F=T.values;for(let D=0;D=h.inHeight)continue;let K=H*d[0],Z=$+j*p[1];for(let J=0;J=h.inWidth)continue;let se=K+te*d[1],ye=Z+re*h.inChannels,ue=ee,be=se;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:b,outWidth:y,padInfo:x,strideHeight:w,strideWidth:I,filterHeight:T,filterWidth:C,dilationHeight:E,dilationWidth:F,outShape:D}=N.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=v.sizeFromShape(D),S=D.length,M=v.getArrayFromDType(a.dtype,$);for(let B=0;B=0&&te=0&&seJ&&(J=be)}}}let ee=v.locToIndex([B,U,j,Z],S,v.computeStrides(D));M[ee]=J}}}return{dataId:l.write(v.toTypedArray(M,a.dtype),D,a.dtype),shape:D,dtype:a.dtype}}},M8={kernelName:Nl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:F}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===F.length,()=>`Error in ${Nl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=v.toNestedArray(F,u.data.get(s.dataId).values),$=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=se,Z=ee,J=te)}}}$[Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(v.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},P8={kernelName:Sl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:F}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===F.length,()=>`Error in ${Sl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=v.toNestedArray(F,u.data.get(s.dataId).values),$=v.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=se,Z=ae,J=re)}}}$[S][Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(v.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function kd(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ge(r,"sum");let o;r.dtype==="bool"?o=hs({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=pr({inputs:{x:r},backend:n});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=N.getAxesPermutation(u,l),d=u,c=o;p!=null&&(c=Un({inputs:{x:o},backend:n,attrs:{perm:p}}),d=N.getInnerMostAxes(d.length,l)),N.assertAxesAreInnerMostDims("sum",d,c.shape.length);let[h,m]=N.computeOutAndReduceShapes(c.shape,d),f=N.upcastType(c.dtype,"int32"),g=rm(n,h,f),b=v.sizeFromShape(m),y=n.data.get(g.dataId).values,x=n.data.get(c.dataId).values;for(let w=0;w=0&&(c=kd({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var z8={kernelName:km,backendName:"cpu",kernelFunc:L8};function W8(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;ge([a,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l=0?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",s)}var B8={kernelName:su,backendName:"cpu",kernelFunc:W8},V8=N.ERF_P,U8=N.ERF_A1,G8=N.ERF_A2,H8=N.ERF_A3,q8=N.ERF_A4,j8=N.ERF_A5,K8=it(iu,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+V8*n);return t*(1-((((j8*a+q8)*a+H8)*a+G8)*a+U8)*a*Math.exp(-n*n))}),X8={kernelName:iu,backendName:"cpu",kernelFunc:K8};function om(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),xt({inputs:{x:r},backend:n,attrs:{shape:o}})}var Y8={kernelName:lu,backendName:"cpu",kernelFunc:om},Z8=Gt((e,t)=>e/t),$1=sn(Bi,Z8),jx={kernelName:Bi,backendName:"cpu",kernelFunc:$1};function A_(e,t,n){let a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),d=v.getTypedArrayFromDType("float32",p),c=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:a}=e,r=n,s=v.getTypedArrayFromDType(a.dtype,v.sizeFromShape(a.shape)),[i,o,l,u]=a.shape,p=r.data.get(a.dataId).values;for(let d=0;d=0&&y=0,()=>`GatherV2: the index value ${I} is not in [0, ${p-1}]`)}let d=o;o==null&&(d=0);let c=v.sizeFromShape(s.shape),h=N.segment_util.collectGatherOpShapeInfo(r,s,l,d),m=xt({inputs:{x:r},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=xt({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),g=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],b=n.bufferSync(f),y=n.bufferSync(m),x=jC(y,b,g);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.makeTensorInfo(h.outputShape,x.dtype,x.values)}var hX={kernelName:pu,backendName:"cpu",kernelFunc:dX};function mX(e){let{inputs:t,backend:n}=e,{input:a}=t,r=v.sizeFromShape(a.shape),s=a.shape[a.shape.length-1],i=r/s,o=xt({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=A_(o,!0,n),u=xt({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var fX={kernelName:Sm,backendName:"cpu",kernelFunc:mX},gX=it(Yi,e=>Number.isFinite(e)?1:0,"bool"),bX={kernelName:Yi,backendName:"cpu",kernelFunc:gX},yX=it(Zi,e=>Math.abs(e)===1/0?1:0,"bool"),xX={kernelName:Zi,backendName:"cpu",kernelFunc:yX},vX=it(Ji,e=>Number.isNaN(e)?1:0,"bool"),wX={kernelName:Ji,backendName:"cpu",kernelFunc:vX};function kX(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=JC(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var IX={kernelName:fu,backendName:"cpu",kernelFunc:kX},SX=it(to,e=>Math.log1p(e)),NX={kernelName:to,backendName:"cpu",kernelFunc:SX},TX=Gt((e,t)=>e&&t),CX=sn(gu,TX,null,"bool"),_X={kernelName:gu,backendName:"cpu",kernelFunc:CX},EX=it(bu,e=>e?0:1,"bool"),AX={kernelName:bu,backendName:"cpu",kernelFunc:EX},FX=Gt((e,t)=>e||t),$X=sn(yu,FX,null,"bool"),DX={kernelName:yu,backendName:"cpu",kernelFunc:$X};function RX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;ge(r,"LRN");let u=r.shape[3],p=u-1,d=n.data.get(r.dataId).values,c=v.sizeFromShape(r.shape),h=new Float32Array(c);function m(f){let g=f%u,b=f-g+Math.max(0,g-s),y=f-g+Math.min(g+s,p),x=0;for(;b<=y;b++){let w=d[b];x+=w*w}return x}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. 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i8(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,b=p.effectiveFilterHeight,y=p.effectiveFilterWidth,x=y-1-p.padInfo.left,w=b-1-p.padInfo.top,I=Le(i.shape,"float32"),T=1/(h*m),C=n.data.get(r.dataId).values,E=Le(r.shape,"float32",C);for(let F=0;F=p.outHeight||Math.floor(j)!==j))for(let K=0;K=p.outWidth||Math.floor(Z)!==Z)continue;let J=E.get(F,j,Z,D);U+=J}}I.set(U*T,F,$,S,D)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var o8={kernelName:bm,backendName:"cpu",kernelFunc:i8};function l8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((b,y)=>b*y),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=xt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Un({inputs:{x:h},backend:n,attrs:{perm:u}}),f=xt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=yi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var c8={kernelName:Jl,backendName:"cpu",kernelFunc:p8};function d8(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,u=k1(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var h8={kernelName:Ql,backendName:"cpu",kernelFunc:d8};function m8(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var f8={kernelName:Ec,backendName:"cpu",kernelFunc:m8},g8=it(ys,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(v.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 u=0;uf.shape);N.assertParamsConsistent(i,s);let o=N.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>v.sizeFromShape(f.shape)>0);if(l.length===1)return pr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(w=>bi({inputs:{input:w},backend:n})),g=l.map(w=>Vl({inputs:{input:w},backend:n})),b=Ul({inputs:f,backend:n,attrs:{axis:s}}),y=Ul({inputs:g,backend:n,attrs:{axis:s}}),x=Zn({inputs:{real:b,imag:y},backend:n});return f.forEach(w=>n.disposeIntermediateTensorInfo(w)),g.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),x}let u=l.map(f=>{let g=[-1,v.sizeFromShape(f.shape.slice(s))];return xt({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=N.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=I1(p,o,t[0].dtype,d),h=N.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var w8={kernelName:eu,backendName:"cpu",kernelFunc:Ul};function z_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,b=c.padInfo.left,y=c.padInfo.top,x=c.dataFormat==="channelsLast",w=new Vt(c.outShape,r.dtype),I=v.computeStrides(r.shape),T=v.computeStrides(s.shape),C=I[0],E=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=w.strides[0],S=x?w.strides[1]:w.strides[2],M=x?w.strides[2]:1,B=x?1:w.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,j=w.values;for(let K=0;K=c.inHeight)continue;let ye=re*T[0],ue=Z+se*E;for(let be=0;be=c.inWidth)continue;let ht=ye+We*T[1],st=ue+Ge*F,tt=ht;for(let nt=0;nt=u.inDepth)continue;let K=H*F[0],Z=$+j*E[1];for(let J=0;J=u.inHeight)continue;let se=K+te*F[1],ye=Z+re*E[2];for(let ue=0;ue=u.inWidth)continue;let Ge=se+Se*F[2],ht=ye+We*u.inChannels,st=Ge;for(let tt=0;ttMath.cos(e)),R8={kernelName:Li,backendName:"cpu",kernelFunc:D8},M8=it(zi,e=>Math.cosh(e)),P8={kernelName:zi,backendName:"cpu",kernelFunc:M8};function O8(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=Le([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,w=n.data.get(r.dataId).values,I=v.computeStrides(r.shape),T=v.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-F)*(d-1)/(f-1):0,U=g>1?(S-D)*(c-1)/(g-1):0;for(let H=0;H1?F*(d-1)+H*B:.5*(F+$)*(d-1);if(j<0||j>d-1){for(let K=0;K1?D*(c-1)+ee*U:.5*(D+S)*(c-1);if(ae<0||ae>c-1){for(let ye=0;ye1?D*(c-1)+K*U:.5*(D+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aeb+m-y-1:(b,y)=>b+y;for(let b=0;bb+m-y-1:(b,y)=>b+y;for(let b=0;b`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. 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ee=v.locToIndex([B,U,j,Z],S,v.computeStrides(D));M[ee]=J}}}return{dataId:l.write(v.toTypedArray(M,a.dtype),D,a.dtype),shape:D,dtype:a.dtype}}},tX={kernelName:Tl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:F}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===F.length,()=>`Error in ${Tl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=v.toNestedArray(F,u.data.get(s.dataId).values),$=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=se,Z=ee,J=te)}}}$[Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(v.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},nX={kernelName:Nl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:F}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===F.length,()=>`Error in ${Nl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=v.toNestedArray(F,u.data.get(s.dataId).values),$=v.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=se,Z=ae,J=re)}}}$[S][Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(v.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function kd(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ge(r,"sum");let o;r.dtype==="bool"?o=ms({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=pr({inputs:{x:r},backend:n});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=N.getAxesPermutation(u,l),d=u,c=o;p!=null&&(c=Un({inputs:{x:o},backend:n,attrs:{perm:p}}),d=N.getInnerMostAxes(d.length,l)),N.assertAxesAreInnerMostDims("sum",d,c.shape.length);let[h,m]=N.computeOutAndReduceShapes(c.shape,d),f=N.upcastType(c.dtype,"int32"),g=rm(n,h,f),b=v.sizeFromShape(m),y=n.data.get(g.dataId).values,x=n.data.get(c.dataId).values;for(let w=0;w=0&&(c=kd({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var sX={kernelName:Im,backendName:"cpu",kernelFunc:rX};function iX(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;ge([a,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l=0?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",s)}var oX={kernelName:iu,backendName:"cpu",kernelFunc:iX},lX=N.ERF_P,uX=N.ERF_A1,pX=N.ERF_A2,cX=N.ERF_A3,dX=N.ERF_A4,hX=N.ERF_A5,mX=it(ou,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+lX*n);return t*(1-((((hX*a+dX)*a+cX)*a+pX)*a+uX)*a*Math.exp(-n*n))}),fX={kernelName:ou,backendName:"cpu",kernelFunc:mX};function om(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),xt({inputs:{x:r},backend:n,attrs:{shape:o}})}var gX={kernelName:uu,backendName:"cpu",kernelFunc:om},bX=Ot((e,t)=>e/t),R1=Zt(Ui,bX),Kx={kernelName:Ui,backendName:"cpu",kernelFunc:R1};function B_(e,t,n){let a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),d=v.getTypedArrayFromDType("float32",p),c=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:a}=e,r=n,s=v.getTypedArrayFromDType(a.dtype,v.sizeFromShape(a.shape)),[i,o,l,u]=a.shape,p=r.data.get(a.dataId).values;for(let d=0;d=0&&y=0,()=>`GatherV2: the index value ${I} is not in [0, ${p-1}]`)}let d=o;o==null&&(d=0);let c=v.sizeFromShape(s.shape),h=N.segment_util.collectGatherOpShapeInfo(r,s,l,d),m=xt({inputs:{x:r},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=xt({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),g=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],b=n.bufferSync(f),y=n.bufferSync(m),x=r_(y,b,g);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.makeTensorInfo(h.outputShape,x.dtype,x.values)}var DX={kernelName:cu,backendName:"cpu",kernelFunc:$X};function RX(e){let{inputs:t,backend:n}=e,{input:a}=t,r=v.sizeFromShape(a.shape),s=a.shape[a.shape.length-1],i=r/s,o=xt({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=B_(o,!0,n),u=xt({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var MX={kernelName:Nm,backendName:"cpu",kernelFunc:RX},PX=it(Ji,e=>Number.isFinite(e)?1:0,"bool"),OX={kernelName:Ji,backendName:"cpu",kernelFunc:PX},LX=it(Qi,e=>Math.abs(e)===1/0?1:0,"bool"),zX={kernelName:Qi,backendName:"cpu",kernelFunc:LX},WX=it(eo,e=>Number.isNaN(e)?1:0,"bool"),BX={kernelName:eo,backendName:"cpu",kernelFunc:WX};function VX(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=u_(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var UX={kernelName:gu,backendName:"cpu",kernelFunc:VX},GX=it(ao,e=>Math.log1p(e)),HX={kernelName:ao,backendName:"cpu",kernelFunc:GX},qX=Ot((e,t)=>e&&t),jX=Zt(bu,qX,null,"bool"),KX={kernelName:bu,backendName:"cpu",kernelFunc:jX},XX=it(yu,e=>e?0:1,"bool"),YX={kernelName:yu,backendName:"cpu",kernelFunc:XX},ZX=Ot((e,t)=>e||t),JX=Zt(xu,ZX,null,"bool"),QX={kernelName:xu,backendName:"cpu",kernelFunc:JX};function eY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;ge(r,"LRN");let u=r.shape[3],p=u-1,d=n.data.get(r.dataId).values,c=v.sizeFromShape(r.shape),h=new Float32Array(c);function m(f){let g=f%u,b=f-g+Math.max(0,g-s),y=f-g+Math.min(g+s,p),x=0;for(;b<=y;b++){let w=d[b];x+=w*w}return x}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. 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To speed things up dram 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:u}}function Go(e,t,n="index"){let a=v.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 Mf(e,t,n="index"){let a=v.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / outShapeStrides[${s}]`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function CZ(e,t){let n=e.length,a=e.map(s=>`${t}[${s}]`),r=new Array(n-1);r[n-2]=a[n-1];for(let s=n-3;s>=0;--s)r[s]=`(${r[s+1]} * ${a[s+1]})`;return r}function _Z(e,t,n="index"){let a=e.map((s,i)=>i),r=CZ(a,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function P1(e){let t=v.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:u}}function qo(e,t,n="index"){let a=v.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 Pf(e,t,n="index"){let a=v.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / outShapeStrides[${s}]`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function jZ(e,t){let n=e.length,a=e.map(s=>`${t}[${s}]`),r=new Array(n-1);r[n-2]=a[n-1];for(let s=n-3;s>=0;--s)r[s]=`(${r[s+1]} * ${a[s+1]})`;return r}function KZ(e,t,n="index"){let a=e.map((s,i)=>i),r=jZ(a,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function L1(e){let t=v.computeStrides(e).map(n=>n.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; } -`}function O1(){return` +`}function z1(){return` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } -`}var lE=` +`}var xE=` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -155,22 +159,22 @@ Hi, looks like you are running TensorFlow.js in Node.js. 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4:a.push("uniform ivec4 outShape;"),a.push("uniform ivec3 outShapeStrides;");break;default:break}a.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(c=>{a.push(`uniform ${c.type} ${c.name}${c.arrayIndex?`[${c.arrayIndex}]`:""};`)});let r=a.join(` -`),s=e.map(c=>AZ(c,t,n.packedInputs,n.enableShapeUniforms)).join(` -`),i=t.texShape,o=_n(),l=DZ(o),u,p,d=PZ(o);return t.isPacked?(u=FZ(t.logicalShape,i,n.enableShapeUniforms),p=MZ(o)):(u=$Z(t.logicalShape,i,n.enableShapeUniforms),p=RZ(o)),n.packedInputs&&(d+=WZ),[d,l,p,r,u,s,n.userCode].join(` -`)}function op(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return JZ(e,t);case 1:return eJ(e,t);case 2:return nJ(e,t);case 3:return rJ(e,t);case 4:return iJ(e,t);case 5:return oJ(e);case 6:return lJ(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function pE(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return ZZ(e);case 1:return QZ(e,t);case 2:return tJ(e,t);case 3:return aJ(e,t);default:return sJ(e,t)}}function AZ(e,t,n=!1,a){let r="";n?r+=pE(e,a):r+=op(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=uJ(e,t):r+=pJ(e,t)),r}function FZ(e,t,n){switch(e.length){case 0:return cE();case 1:return BZ(e,t,n);case 2:return XZ(e,t,n);case 3:return UZ(e,t,n);default:return HZ(e,t,n)}}function $Z(e,t,n){switch(e.length){case 0:return cE();case 1:return VZ(e,t,n);case 2:return YZ(e,t,n);case 3:return GZ(e,t,n);case 4:return qZ(e,t,n);case 5:return jZ(e,t);case 6:return KZ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function DZ(e){return` +`,{getBroadcastDims:vE}=N;function XZ(e,t,n){let a=[];if(e.forEach(c=>{let h=v.sizeFromShape(c.shapeInfo.logicalShape);if(c.shapeInfo.isUniform?a.push(`uniform float ${c.name}${h>1?`[${h}]`:""};`):(a.push(`uniform sampler2D ${c.name};`),a.push(`uniform int offset${c.name};`)),n.enableShapeUniforms){let{uniformShape:m}=W1(n.packedInputs,c.shapeInfo.logicalShape,c.shapeInfo.texShape);switch(m.length){case 1:a.push(`uniform int ${c.name}Shape;`);break;case 2:a.push(`uniform ivec2 ${c.name}Shape;`);break;case 3:a.push(`uniform ivec3 ${c.name}Shape;`);break;case 4:a.push(`uniform ivec4 ${c.name}Shape;`);break;default:break}a.push(`uniform ivec2 ${c.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:a.push("uniform int outShape;");break;case 2:a.push("uniform ivec2 outShape;"),a.push("uniform int outShapeStrides;");break;case 3:a.push("uniform ivec3 outShape;"),a.push("uniform ivec2 outShapeStrides;");break;case 4:a.push("uniform ivec4 outShape;"),a.push("uniform ivec3 outShapeStrides;");break;default:break}a.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(c=>{a.push(`uniform ${c.type} ${c.name}${c.arrayIndex?`[${c.arrayIndex}]`:""};`)});let r=a.join(` 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oJ(e,t,n);case 2:return fJ(e,t,n);case 3:return uJ(e,t,n);default:return cJ(e,t,n)}}function JZ(e,t,n){switch(e.length){case 0:return kE();case 1:return lJ(e,t,n);case 2:return gJ(e,t,n);case 3:return pJ(e,t,n);case 4:return dJ(e,t,n);case 5:return hJ(e,t);case 6:return mJ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function QZ(e){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${e.texture2D}(textureSampler, uv).r; } - `}function RZ(e){return` + `}function eJ(e){return` void setOutput(float val) { ${e.output} = vec4(val, 0, 0, 0); } - `}function MZ(e){return` + `}function tJ(e){return` void setOutput(vec4 val) { ${e.output} = val; } - `}function PZ(e){return`${e.version} + `}function nJ(e){return`${e.version} precision highp float; precision highp int; precision highp sampler2D; @@ -225,10 +229,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return fract((p3.x + p3.y) * p3.z); } - ${OZ} - ${LZ} - ${zZ} - `}var OZ=` + ${aJ} + ${rJ} + ${sJ} + `}var aJ=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -240,7 +244,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,LZ=` +`,rJ=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -248,7 +252,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,zZ=` +`,sJ=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -257,7 +261,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,WZ=` +`,iJ=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -268,11 +272,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function cE(){return` +`;function kE(){return` int getOutputCoords() { return 0; } - `}function BZ(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?` + `}function oJ(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } @@ -301,7 +305,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${a[0]}, ${a[1]})); return 2 * (resTexRC.x * ${a[1]} + resTexRC.y); } - `}function VZ(e,t,n){return t[0]===1?n?` + `}function lJ(e,t,n){return t[0]===1?n?` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } @@ -329,7 +333,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${t[0]}, ${t[1]})); return resTexRC.x * ${t[1]} + resTexRC.y; } - `}function UZ(e,t,n){if(n)return` + `}function uJ(e,t,n){if(n)return` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); @@ -360,15 +364,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `}function GZ(e,t,n){if(n)return` + `}function pJ(e,t,n){if(n)return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Mf(["r","c","d"],e)} + ${Pf(["r","c","d"],e)} return ivec3(r, c, d); } -`;let a=Go(["r","c","d"],e);return` +`;let a=qo(["r","c","d"],e);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -376,7 +380,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a} return ivec3(r, c, d); } - `}function HZ(e,t,n){if(n)return` + `}function cJ(e,t,n){if(n)return` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -417,15 +421,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec${e.length}(${l}); } - `}function qZ(e,t,n){if(n)return` + `}function dJ(e,t,n){if(n)return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Mf(["r","c","d","d2"],e)} + ${Pf(["r","c","d","d2"],e)} return ivec4(r, c, d, d2); } - `;let a=Go(["r","c","d","d2"],e);return` + `;let a=qo(["r","c","d","d2"],e);return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -433,7 +437,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a} return ivec4(r, c, d, d2); } - `}function jZ(e,t){let n=Go(["r","c","d","d2","d3"],e);return` + `}function hJ(e,t){let n=qo(["r","c","d","d2","d3"],e);return` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -445,7 +449,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } - `}function KZ(e,t){let n=Go(["r","c","d","d2","d3","d4"],e);return` + `}function mJ(e,t){let n=qo(["r","c","d","d2","d3","d4"],e);return` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -456,7 +460,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec6 result = ivec6(r, c, d, d2, d3, d4); return result; } - `}function XZ(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?` + `}function fJ(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); @@ -489,7 +493,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec2(r, c); } - `}function YZ(e,t,n){return v.arraysEqual(e,t)?n?` + `}function gJ(e,t,n){return v.arraysEqual(e,t)?n?` ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); } @@ -543,15 +547,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${e[1]}; return ivec2(r, c); } - `}function Ho(e){return`offset${e}`}function ZZ(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=_n();return` + `}function jo(e){return`offset${e}`}function bJ(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=_n();return` vec4 ${n}() { return ${a.texture2D}(${t}, halfCR); } - `}function JZ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${a}() {return ${n};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return` + `}function yJ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${a}() {return ${n};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return` float ${a}() { return sampleTexture(${n}, halfCR); } - `;let i=Ho(n);if(t)return` + `;let i=jo(n);if(t)return` float ${a}() { vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i}); return sampleTexture(${n}, uv); @@ -561,7 +565,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${o}, ${l}, ${i}); return sampleTexture(${n}, uv); } - `}function QZ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=_n();if(t)return` + `}function xJ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=_n();if(t)return` vec4 ${a}(int index) { ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( @@ -574,7 +578,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${i[0]}, ${i[1]}, index); return ${s.texture2D}(${n}, uv); } - `}function eJ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return` + `}function vJ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return` float ${a}(int index) { ${lp(e)} } @@ -582,7 +586,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float ${a}(int index) { return sampleTexture(${n}, halfCR); } - `;let o=Ho(n);return i===1?t?` + `;let o=jo(n);return i===1?t?` float ${a}(int index) { vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0])); return sampleTexture(${n}, uv); @@ -612,7 +616,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${s}, ${i}, index + ${o}); return sampleTexture(${n}, uv); } - `}function tJ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=_n();if(s!=null&&v.arraysEqual(n,s))return t?` + `}function wJ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=_n();if(s!=null&&v.arraysEqual(n,s))return t?` vec4 ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]); @@ -636,7 +640,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = packedUVfrom2D(${p}, ${u[0]}, ${u[1]}, row, col); return ${l.texture2D}(${a}, uv); } - `}function nJ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&v.arraysEqual(n,s)){if(t)return` + `}function kJ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&v.arraysEqual(n,s)){if(t)return` float ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]); return sampleTexture(${a}, uv); @@ -656,7 +660,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int index = round(dot(vec2(row, col), vec2(${n[1]}, 1))); ${lp(e)} } - `;let u=s[0],p=s[1],d=Ho(a);return p===1?t?` + `;let u=s[0],p=s[1],d=jo(a);return p===1?t?` float ${r}(int row, int col) { float index = dot(vec3(row, col, ${d}), vec3(${a}Shape[1], 1, 1)); vec2 uv = vec2(0.5, (index + 0.5) / float(${a}TexShape[0])); @@ -694,8 +698,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${u}, ${p}, index); return sampleTexture(${a}, uv); } -`}function aJ(e,t){let n=e.shapeInfo.logicalShape,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)];if(n[0]===1){let c=n.slice(1),h=[1,2],m=up(e,c),f=["b","row","col"];return` - ${pE(m,t)} +`}function IJ(e,t){let n=e.shapeInfo.logicalShape,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)];if(n[0]===1){let c=n.slice(1),h=[1,2],m=up(e,c),f=["b","row","col"];return` + ${wE(m,t)} vec4 ${r}(int b, int row, int col) { return ${r}(${pp(f,h)}); } @@ -714,7 +718,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${l}, ${u}, ${d}, ${p}, b, row, col); return ${o.texture2D}(${a}, uv); } - `}function rJ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[1]*n[2],i=n[2],{newShape:o,keptDims:l}=v.squeezeShape(n),u=o;if(u.length=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(` + `}function EJ(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=vE(e.shapeInfo.logicalShape,t.logicalShape),l=ct(i),u=i-s,p,d=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(` `);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((g,b)=>`coords.${d[b+u]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,f=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=` return vec4(outputValue.xy, outputValue.xy); `;else if(m&&!f)i===1?h=` @@ -973,20 +977,20 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 outputValue = get${a}(${c}); ${h} } - `}function pJ(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&&v.arraysEqual(i,s))return` + `}function AJ(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&&v.arraysEqual(i,s))return` float ${r}() { return sampleTexture(${n}, resultUV); } - `;let u=ct(l),p=uE(e.shapeInfo.logicalShape,t.logicalShape),d=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&p.length>=1?c="coords = 0;":c=p.map(f=>`coords.${h[f+d]} = 0;`).join(` + `;let u=ct(l),p=vE(e.shapeInfo.logicalShape,t.logicalShape),d=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&p.length>=1?c="coords = 0;":c=p.map(f=>`coords.${h[f+d]} = 0;`).join(` `);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+d]}`).join(", "),` float ${r}() { ${u} coords = getOutputCoords(); ${c} return get${a}(${m}); } - `}function ct(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 ${e} is not yet supported`)}function L1(e,t,n){let{newShape:a,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!v.arraysEqual(t,n)&&a.lengthe[n]).join(", ")}function cJ(e,t,n,a){let r=n.map((p,d)=>{let c={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(c.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[d],shapeInfo:c}}),s=r.map(p=>p.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=EZ(r,i,t),l=B_(e.gl,o),u=e.createProgram(l);return G().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},dE(e,t,u))}function dE(e,t,n){let a=[],r=[],s,i,o,l=null,u=null;u=e.getUniformLocation(n,"NAN",!1),G().getNumber("WEBGL_VERSION")===1&&(l=e.getUniformLocation(n,"INFINITY",!1));let p=!1;for(let d of t.variableNames){let c={name:d,uniform:e.getUniformLocation(n,d,p),offset:e.getUniformLocation(n,`offset${d}`,p)};t.enableShapeUniforms&&(c.shape=e.getUniformLocation(n,`${d}Shape`,p),c.texShape=e.getUniformLocation(n,`${d}TexShape`,p)),a.push(c)}if(t.enableShapeUniforms&&(s=e.getUniformLocation(n,"outShape",p),o=e.getUniformLocation(n,"outShapeStrides",p),i=e.getUniformLocation(n,"outTexShape",p)),t.customUniforms)for(let d of t.customUniforms)r.push(e.getUniformLocation(n,d.name,p));return{variablesLocations:a,customUniformLocations:r,infLoc:l,nanLoc:u,outShapeLocation:s,outShapeStridesLocation:o,outTexShapeLocation:i}}function MI(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(!v.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(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function dJ(e,t,n,a,r){t.program.enableShapeUniforms||(MI(t.inShapeInfos,n),MI([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),G().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN);for(let l=0;l{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:d}=L1(e.packedInputs,i.shape,l),c="",h="",m="";if(p.length===1&&e.packedInputs){let I=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];c=`${I[0]>1}_${I[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let I=v.computeStrides(p);m=`${I[0]===l[1]}_${I[I.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),b=v.sizeFromShape(i.shape)===1,y=N.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&f===n.shape.length&&v.arraysEqual(l,n.texData.texShape),w=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${f}_${x}_${u?d:""}_${p.length}_${b}_${y}_${g}_${c}_${h}_${m}_${w}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r+`${G().getNumber("WEBGL_VERSION")}`,s}function vn(e){return G().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var mJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=vc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=_n();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` + `}function ct(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 ${e} is not yet supported`)}function W1(e,t,n){let{newShape:a,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!v.arraysEqual(t,n)&&a.lengthe[n]).join(", ")}function FJ(e,t,n,a){let r=n.map((p,d)=>{let c={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(c.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[d],shapeInfo:c}}),s=r.map(p=>p.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=XZ(r,i,t),l=J_(e.gl,o),u=e.createProgram(l);return G().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(e.buildVao(u),Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},IE(e,t,u)))}function IE(e,t,n){let a=[],r=[],s,i,o,l=null,u=null;u=e.getUniformLocation(n,"NAN",!1),G().getNumber("WEBGL_VERSION")===1&&(l=e.getUniformLocation(n,"INFINITY",!1));let p=!1;for(let d of t.variableNames){let c={name:d,uniform:e.getUniformLocation(n,d,p),offset:e.getUniformLocation(n,`offset${d}`,p)};t.enableShapeUniforms&&(c.shape=e.getUniformLocation(n,`${d}Shape`,p),c.texShape=e.getUniformLocation(n,`${d}TexShape`,p)),a.push(c)}if(t.enableShapeUniforms&&(s=e.getUniformLocation(n,"outShape",p),o=e.getUniformLocation(n,"outShapeStrides",p),i=e.getUniformLocation(n,"outTexShape",p)),t.customUniforms)for(let d of t.customUniforms)r.push(e.getUniformLocation(n,d.name,p));return{variablesLocations:a,customUniformLocations:r,infLoc:l,nanLoc:u,outShapeLocation:s,outShapeStridesLocation:o,outTexShapeLocation:i}}function LI(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(!v.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(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function $J(e,t,n,a,r){t.program.enableShapeUniforms||(LI(t.inShapeInfos,n),LI([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),e.bindVertexArray(t.webGLProgram.vao),G().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN);for(let l=0;l{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:d}=W1(e.packedInputs,i.shape,l),c="",h="",m="";if(p.length===1&&e.packedInputs){let I=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];c=`${I[0]>1}_${I[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let I=v.computeStrides(p);m=`${I[0]===l[1]}_${I[I.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),b=v.sizeFromShape(i.shape)===1,y=N.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&f===n.shape.length&&v.arraysEqual(l,n.texData.texShape),w=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${f}_${x}_${u?d:""}_${p.length}_${b}_${y}_${g}_${c}_${h}_${m}_${w}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r+`${G().getNumber("WEBGL_VERSION")}`,s}function vn(e){return G().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var RJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=vc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=_n();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Mf(["r","c","d"],e):Go(["r","c","d"],e)} + ${this.enableShapeUniforms?Pf(["r","c","d"],e):qo(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1004,9 +1008,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},fJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=vc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=_n();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` + `}},MJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=vc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=_n();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Mf(["r","c","d"],e):Go(["r","c","d"],e)} + ${this.enableShapeUniforms?Pf(["r","c","d"],e):qo(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1024,26 +1028,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},gJ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ca.DOWNLOAD;let t=_n();this.outputShape=e,this.userCode=` - ${lE} + `}},PJ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=da.DOWNLOAD;let t=_n();this.outputShape=e,this.userCode=` + ${xE} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } - `}},bJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ca.DOWNLOAD;let t=_n();this.outputShape=e,this.userCode=` - ${lE} + `}},OJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=da.DOWNLOAD;let t=_n();this.outputShape=e,this.userCode=` + ${xE} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } - `}},yJ={R:0,G:1,B:2,A:3},PI=class{constructor(e,t=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let a=_n();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let s="";for(let i=0;ikE,createBufferFromOutputTexture:()=>NE,createFloat16MatrixTexture:()=>yE,createFloat16PackedMatrixTexture:()=>wE,createFloat32MatrixTexture:()=>bE,createIndexBuffer:()=>gE,createPackedMatrixTexture:()=>vE,createUnsignedBytesMatrixTexture:()=>xE,createVertexBuffer:()=>fE,createVertexShader:()=>mE,downloadByteEncodedFloatMatrixFromOutputTexture:()=>CE,downloadFloat32MatrixFromBuffer:()=>TE,downloadMatrixFromPackedOutputTexture:()=>EE,downloadPackedMatrixFromBuffer:()=>_E,getInternalFormatForFloat16MatrixTexture:()=>W1,getInternalFormatForFloat16PackedMatrixTexture:()=>U1,getInternalFormatForFloat32MatrixTexture:()=>z1,getInternalFormatForPackedMatrixTexture:()=>V1,getInternalFormatForUnsignedBytesMatrixTexture:()=>B1,uploadDenseMatrixToTexture:()=>IE,uploadPixelDataToTexture:()=>SE});function mE(e){let t=_n(),n=`${t.version} + `}},SE={};Ee(SE,{bindVertexProgramAttributeStreams:()=>DE,createBufferFromOutputTexture:()=>PE,createFloat16MatrixTexture:()=>EE,createFloat16PackedMatrixTexture:()=>$E,createFloat32MatrixTexture:()=>_E,createIndexBuffer:()=>CE,createPackedMatrixTexture:()=>FE,createUnsignedBytesMatrixTexture:()=>AE,createVertexBuffer:()=>TE,createVertexShader:()=>NE,downloadByteEncodedFloatMatrixFromOutputTexture:()=>LE,downloadFloat32MatrixFromBuffer:()=>OE,downloadMatrixFromPackedOutputTexture:()=>WE,downloadPackedMatrixFromBuffer:()=>zE,getInternalFormatForFloat16MatrixTexture:()=>V1,getInternalFormatForFloat16PackedMatrixTexture:()=>H1,getInternalFormatForFloat32MatrixTexture:()=>B1,getInternalFormatForPackedMatrixTexture:()=>G1,getInternalFormatForUnsignedBytesMatrixTexture:()=>U1,uploadDenseMatrixToTexture:()=>RE,uploadPixelDataToTexture:()=>ME});function NE(e){let t=_n(),n=`${t.version} precision highp float; ${t.attribute} vec3 clipSpacePos; ${t.attribute} vec2 uv; @@ -1115,7 +1119,7 @@ vec2 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de(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function zE(e,t,n,a,r,s,i,o){let l=e,u=new Float32Array(PZ(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function WE(e,t,n){let a=new Float32Array(t*n*4);return de(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var Mh=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=G().getNumber("WEBGL_VERSION");if(e!=null?(this.gl=e,K_(t,e)):this.gl=Ka(t),e=this.gl,G().getNumber("WEBGL_VERSION")===2){let r=e;this.createVertexArray=()=>de(r,()=>r.createVertexArray()),this.bindVertexArray=s=>de(r,()=>r.bindVertexArray(s)),this.deleteVertexArray=s=>de(r,()=>r.deleteVertexArray(s)),this.getVertexArray=()=>de(r,()=>r.getParameter(r.VERTEX_ARRAY_BINDING))}else if(e!=null){let r=e.getExtension("OES_vertex_array_object");if(r==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>de(e,()=>r.createVertexArrayOES()),this.bindVertexArray=s=>de(e,()=>r.bindVertexArrayOES(s)),this.deleteVertexArray=s=>de(e,()=>r.deleteVertexArrayOES(s)),this.getVertexArray=()=>de(e,()=>e.getParameter(r.VERTEX_ARRAY_BINDING_OES))}let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),G().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Qp(this.gl,r),ha(this.gl,s))this.textureHalfFloatExtension=Qp(this.gl,s);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(n),ha(this.gl,a))this.colorBufferHalfFloatExtension=Qp(this.gl,a);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(n="EXT_color_buffer_float",ha(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(ha(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=TE(this.gl),this.indexBuffer=CE(this.gl),this.framebuffer=sE(this.gl),this.textureConfig=P1(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. 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;de(e,()=>e.finish()),de(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),de(e,()=>e.deleteFramebuffer(this.framebuffer)),de(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),de(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),de(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),_E(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),EE(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),AE(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),ME(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),RE(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),$E(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),FE(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Zx(this.gl,this.framebuffer),this.outputTexture=null),de(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>LE(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return zE(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return OE(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=PE(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(G().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 G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>WE(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=NE(t));let n=Q_(t);de(t,()=>t.attachShader(n,this.vertexShader)),de(t,()=>t.attachShader(n,e)),eE(t,n);let a=Object.assign(n,{vao:this.createVertexArray()});return this.debug&&Fh(t,a),a}buildVao(e){this.setProgram(e),this.bindVertexArray(e.vao);let t=this.gl;de(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),DE(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(de(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Fh(this.gl,this.program),de(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?oE(this.gl,e,t):lE(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),de(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(),uE(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=sp(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&&Fh(this.gl,this.program),ec(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}de(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),de(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Qp(this.gl,G().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(G().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(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,G().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=WJ(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){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let n;"setTimeoutCustom"in G().platform&&(n=G().platform.setTimeoutCustom.bind(G().platform)),v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),$h(this.gl,e,this.framebuffer),this.debug&&ec(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?($h(this.gl,this.outputTexture,this.framebuffer),this.debug&&ec(this.gl)):Zx(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;$h(a,e,this.framebuffer),this.debug&&ec(a),this.outputTexture=e,de(a,()=>a.viewport(0,0,t,n)),de(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),de(this.gl,()=>this.gl.scissor(e,t,n,a))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function WJ(e){let t=0;for(;t`${e}.${n}`)}function In(e,t){return t===1?[e]:GE(e,t)}function F9(e,t){if(e===1)return"rc";let n="";for(let a=0;a= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), cEdge ? 0. : getA(${t[1]}), rEdge ? 0. : getA(${t[2]}), - rEdge || cEdge ? 0. : getA(${t[3]})`}},RE=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2===1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=` + rEdge || cEdge ? 0. : getA(${t[3]})`}},HE=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);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); @@ -1154,8 +1158,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${a>0?"}":""} `}this.userCode=` - ${h9(t,this.enableShapeUniforms)} - ${this.enableShapeUniforms?O1():P1(e)} + ${D9(t,this.enableShapeUniforms)} + ${this.enableShapeUniforms?z1():L1(e)} void main() { ivec3 rc = getOutputCoords(); @@ -1170,12 +1174,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function h9(e,t){return` + `}};function D9(e,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?_Z(["r","c","d"],"inputShape"):Go(["r","c","d"],e)} + ${t?KZ(["r","c","d"],"inputShape"):qo(["r","c","d"],e)} return ivec3(r, c, d); } - `}var m9=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){let a=LI(t,n),r=zI(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=OI(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return a===dn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===dn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===dn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===dn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===dn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=LI(n,a),s=zI(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=OI(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=G().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function f9(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;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function OI(e,t,n,a,r){let s=g9(t,a),i;if(r){let[l,u]=sp(e[0],e[1]);i=l*u}else{let[l,u]=Id(e[0],e[1]);i=l*u}let o=f9(n,s);return i*o}function g9(e,t){switch(e){case dn.PACKED_2X2_FLOAT32:return V1(t);case dn.PACKED_2X2_FLOAT16:return U1(t);case dn.UNPACKED_FLOAT32:return z1(t);case dn.UNPACKED_FLOAT16:return W1(t);case dn.PACKED_4X1_UNSIGNED_BYTE:return B1(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function b9(e){return G().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?dn.PACKED_2X2_FLOAT32:dn.UNPACKED_FLOAT32:e?dn.PACKED_2X2_FLOAT16:dn.UNPACKED_FLOAT16}function LI(e,t){if(e===ca.UPLOAD)return dn.PACKED_2X2_FLOAT32;if(e===ca.RENDER||e==null)return b9(t);if(e===ca.DOWNLOAD||e===ca.PIXELS)return dn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function zI(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var rr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` + `}var R9=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){let a=BI(t,n),r=VI(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=WI(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return a===dn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===dn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===dn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===dn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===dn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=BI(n,a),s=VI(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=WI(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=G().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function M9(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;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function WI(e,t,n,a,r){let s=P9(t,a),i;if(r){let[l,u]=sp(e[0],e[1]);i=l*u}else{let[l,u]=Id(e[0],e[1]);i=l*u}let o=M9(n,s);return i*o}function P9(e,t){switch(e){case dn.PACKED_2X2_FLOAT32:return G1(t);case dn.PACKED_2X2_FLOAT16:return H1(t);case dn.UNPACKED_FLOAT32:return B1(t);case dn.UNPACKED_FLOAT16:return V1(t);case dn.PACKED_4X1_UNSIGNED_BYTE:return U1(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function O9(e){return G().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?dn.PACKED_2X2_FLOAT32:dn.UNPACKED_FLOAT32:e?dn.PACKED_2X2_FLOAT16:dn.UNPACKED_FLOAT16}function BI(e,t){if(e===da.UPLOAD)return dn.PACKED_2X2_FLOAT32;if(e===da.RENDER||e==null)return O9(t);if(e===da.DOWNLOAD||e===da.PIXELS)return dn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function VI(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var rr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } @@ -1186,11 +1190,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},Ma="if (isnan(x)) return x;",y9="return x;",WI="return abs(x);",x9="return (x >= 0.0) ? x : (exp(x) - 1.0);",v9=Ma+` + `}},Ma="if (isnan(x)) return x;",L9="return x;",UI="return abs(x);",z9="return (x >= 0.0) ? x : (exp(x) - 1.0);",W9=Ma+` return (x < 0.0) ? 0.0 : x; -`,w9=Ma+` +`,B9=Ma+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Xr="return x;",k9="return 1.0 / (1.0 + exp(-1.0 * x));",I9="return x;",S9=` +`,Yr="return x;",V9="return 1.0 / (1.0 + exp(-1.0 * x));",U9="return x;",G9=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1199,7 +1203,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,N9=` +`,H9=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1209,7 +1213,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,T9=` +`,q9=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1219,7 +1223,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,C9="return 1.0 / (1.0 + exp(-1.0 * x));",es=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` +`,j9="return 1.0 / (1.0 + exp(-1.0 * x));",ts=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } @@ -1230,17 +1234,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},_9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.length,n=In("rc",t),a=ct(t),r=c9(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` + `}},K9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.length,n=In("rc",t),a=ct(t),r=F9(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})); } - `}},E9=hr.whereImpl,A9=1e-7,F9=1e-4,ox={};function $9(e){return e in ox||(ox[e]={}),ox[e]}var D9=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),R9=600;function M9(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*R9/1024/1024}var Pf=class extends Tc{nextDataId(){return Pf.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Mh)t=e;else{let n=Ka(G().getNumber("WEBGL_VERSION"),e);t=new Mh(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Ka(G().getNumber("WEBGL_VERSION"));t=new Mh(n),this.binaryCache=$9(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new m9(this.gpgpu),this.numMBBeforeWarning=M9(),this.texData=new mm(this,Ca())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,n,a,r,s){let i=this.makeTensorInfo(t,n),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[a,r]},o.texShape=[a,r];let l=tc(t),u=new PI(l,!1,s),p=this.runWebGLProgram(u,[i],n,[[a,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,n){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().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:ca.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(G().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:ca.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 d;o?d=new es(i,Xr):d=new rr(i,Xr);let c=this.runWebGLProgram(d,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(a==="complex64"){let d=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);p=N.mergeRealAndImagArrays(d,c)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}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 es(a,Xr):h=new rr(a,Xr);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(G().getBool("DEBUG")&&!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 l=null,u;if(s!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...Nh(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];p=N.mergeRealAndImagArrays(m,f)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(a);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;de(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,p),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ca().removeDataId(e,this),this.pendingDeletes--),d}readToGPU(e,t={}){let n=this.texData.get(e),{values:a,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let c;o?c=new es(r,Xr):c=new rr(r,Xr);let h=this.runWebGLProgram(c,[{dataId:e,shape:r,dtype:i}],i),m=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),m}if(l==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=Ca().makeTensorFromTensorInfo(u),d=this.texData.get(u.dataId);return Object.assign({tensorRef:p},d.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(a=>v.decodeString(a));return Le(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}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=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.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,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 u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=D9){return G().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)0&&v.isString(n[0])){let r=n.map(s=>v.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){return Ca().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new _9(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new d9(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[bi(e.shape),...yi(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[bi(t),...yi(t)],s=new RE(r,n),i=!0,o=[n],l=this.runWebGLProgram(s,[a],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:a,shape:r,dtype:s}=n;if(t!=null){let d=v.sizeFromShape(r),c=t[0]*t[1]*4;v.assert(d<=c,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=tc(r),o;a?o=new fJ(i):o=new mJ(i);let l=!0,u=[t!=null?t:Nh(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,n,a,r=!1,s){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===vc.DENSE){let g=s!=null?s:Nh(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!wc(b.shape,g.shape)){let y=g,x=g.shape;g.shape=b.shape,g=this.packedReshape(g,x),l.push(g),b=this.texData.get(g.dataId),y.shape=x}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},d=hJ(e,u,p),c=this.getAndSaveBinary(d,()=>cJ(this.gpgpu,e,u,p)),h=this.activeTimers!=null,m;h&&(m=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||dJ(this.gpgpu,c,u,p,a),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=G().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=v.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}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||(G().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=P(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=G().getBool("DEBUG");G().set("DEBUG",!1);let t=this.abs(ve(1e-8)).dataSync()[0];if(G().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?A9:F9}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,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=tE(n,o),t.texShape=p),r!=null){let d=tc(n),c,h=p[1],m=p[0],f=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!f)&&([h,m]=sp(p[0],p[1])),o?c=new xJ(d,f):c=new PI(d,f);let g=f?[m,h]:p,b=this.makeTensorInfo(g,a),y=this.texData.get(b.dataId);f?y.usage=ca.PIXELS:y.usage=ca.UPLOAD,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,m,r);let x=[[m,h]],w=!0,I=this.runWebGLProgram(c,[b],a,x,w),T=this.texData.get(I.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=v.now()-u)}else{let d=this.acquireTexture(p,i,a,o);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return t!=null&&(n.values=P9(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]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(a=>{try{this.checkCompletion_(t),a(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await Pw(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(M1(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){let{variablesLocations:t,customUniformLocations:n,infLoc:a,nanLoc:r,outShapeLocation:s,outShapeStridesLocation:i,outTexShapeLocation:o}=dE(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=n,e.infLoc=a,e.nanLoc=r,e.outShapeLocation=s,e.outShapeStridesLocation=i,e.outTexShapeLocation=o}}createTensorFromGPUData(e,t,n){e.channels=e.channels||"RGBA";let{texture:a,height:r,width:s,channels:i}=e,o=Ca().backend;if(!o.gpgpu.gl.isTexture(a))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(a,t,n,r,s,i);return Ca().makeTensorFromDataId(l,t,n,o)}};Pf.nextDataId=0;function P9(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;anew Pf,2);var L9={forceHalfFloat:ME},H1=` + `}},X9=hr.whereImpl,Y9=1e-7,Z9=1e-4,lx={};function J9(e){return e in lx||(lx[e]={}),lx[e]}var Q9=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),eQ=600;function tQ(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*eQ/1024/1024}var Of=class extends Tc{nextDataId(){return Of.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Mh)t=e;else{let n=Ka(G().getNumber("WEBGL_VERSION"),e);t=new Mh(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Ka(G().getNumber("WEBGL_VERSION"));t=new Mh(n),this.binaryCache=J9(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new R9(this.gpgpu),this.numMBBeforeWarning=tQ(),this.texData=new mm(this,_a())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,n,a,r,s){let i=this.makeTensorInfo(t,n),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[a,r]},o.texShape=[a,r];let l=tc(t),u=new zI(l,!1,s),p=this.runWebGLProgram(u,[i],n,[[a,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,n){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().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:da.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(G().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:da.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 d;o?d=new ts(i,Yr):d=new rr(i,Yr);let c=this.runWebGLProgram(d,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(a==="complex64"){let d=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);p=N.mergeRealAndImagArrays(d,c)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}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 ts(a,Yr):h=new rr(a,Yr);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(G().getBool("DEBUG")&&!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 l=null,u;if(s!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...Nh(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];p=N.mergeRealAndImagArrays(m,f)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(a);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;de(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,p),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&_a().removeDataId(e,this),this.pendingDeletes--),d}readToGPU(e,t={}){let n=this.texData.get(e),{values:a,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let c;o?c=new ts(r,Yr):c=new rr(r,Yr);let h=this.runWebGLProgram(c,[{dataId:e,shape:r,dtype:i}],i),m=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),m}if(l==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=_a().makeTensorFromTensorInfo(u),d=this.texData.get(u.dataId);return Object.assign({tensorRef:p},d.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(a=>v.decodeString(a));return Le(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}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=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.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,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 u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=Q9){return G().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)0&&v.isString(n[0])){let r=n.map(s=>v.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){return _a().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new K9(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new $9(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[xi(e.shape),...vi(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[xi(t),...vi(t)],s=new HE(r,n),i=!0,o=[n],l=this.runWebGLProgram(s,[a],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:a,shape:r,dtype:s}=n;if(t!=null){let d=v.sizeFromShape(r),c=t[0]*t[1]*4;v.assert(d<=c,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=tc(r),o;a?o=new MJ(i):o=new RJ(i);let l=!0,u=[t!=null?t:Nh(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,n,a,r=!1,s){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===vc.DENSE){let g=s!=null?s:Nh(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!wc(b.shape,g.shape)){let y=g,x=g.shape;g.shape=b.shape,g=this.packedReshape(g,x),l.push(g),b=this.texData.get(g.dataId),y.shape=x}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},d=DJ(e,u,p),c=this.getAndSaveBinary(d,()=>FJ(this.gpgpu,e,u,p)),h=this.activeTimers!=null,m;h&&(m=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||$J(this.gpgpu,c,u,p,a),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=G().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=v.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}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||(G().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=P(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=G().getBool("DEBUG");G().set("DEBUG",!1);let t=this.abs(ve(1e-8)).dataSync()[0];if(G().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Y9:Z9}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,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=dE(n,o),t.texShape=p),r!=null){let d=tc(n),c,h=p[1],m=p[0],f=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!f)&&([h,m]=sp(p[0],p[1])),o?c=new zJ(d,f):c=new zI(d,f);let g=f?[m,h]:p,b=this.makeTensorInfo(g,a),y=this.texData.get(b.dataId);f?y.usage=da.PIXELS:y.usage=da.UPLOAD,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,m,r);let x=[[m,h]],w=!0,I=this.runWebGLProgram(c,[b],a,x,w),T=this.texData.get(I.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=v.now()-u)}else{let d=this.acquireTexture(p,i,a,o);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return t!=null&&(n.values=nQ(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]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(a=>{try{this.checkCompletion_(t),a(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await Ow(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(O1(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);let{variablesLocations:t,customUniformLocations:n,infLoc:a,nanLoc:r,outShapeLocation:s,outShapeStridesLocation:i,outTexShapeLocation:o}=IE(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=n,e.infLoc=a,e.nanLoc=r,e.outShapeLocation=s,e.outShapeStridesLocation=i,e.outTexShapeLocation=o}}createTensorFromGPUData(e,t,n){e.channels=e.channels||"RGBA";let{texture:a,height:r,width:s,channels:i}=e,o=_a().backend;if(!o.gpgpu.gl.isTexture(a))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(a,t,n,r,s,i);return _a().makeTensorFromDataId(l,t,n,o)}};Of.nextDataId=0;function nQ(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;anew Of,2);var rQ={forceHalfFloat:qE},j1=` if (isnan(a)) return a; if (isnan(b)) return b; -`,Ul=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` +`,Gl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -1250,7 +1254,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}},qo=` + `}},Ko=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; @@ -1299,13 +1303,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function na(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 z9={kernelName:Xi,backendName:"webgl",kernelFunc:na};function _s(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=na({inputs:{x:a},backend:n}),l=na({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var W9={kernelName:ym,backendName:"webgl",kernelFunc:_s},PE="return (a < 0.) ? b * a : a;",OE=` + `}};function aa(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 sQ={kernelName:Zi,backendName:"webgl",kernelFunc:aa};function Es(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=aa({inputs:{x:a},backend:n}),l=aa({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var iQ={kernelName:xm,backendName:"webgl",kernelFunc:Es},jE="return (a < 0.) ? b * a : a;",KE=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function B9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nd(OE,r.shape,i.shape):new Ul(PE,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var V9={kernelName:Qi,backendName:"webgl",kernelFunc:B9},LE="return (a < 0.) ? b * a : a;",zE=` +`;function oQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nd(KE,r.shape,i.shape):new Gl(jE,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var lQ={kernelName:to,backendName:"webgl",kernelFunc:oQ},XE="return (a < 0.) ? b * a : a;",YE=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function U9(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nd(zE,a.shape,r.shape):new Ul(LE,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var G9={kernelName:fo,backendName:"webgl",kernelFunc:U9},cp="if (isnan(x)) return x;";function Ye({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 d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=G().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new es(i.shape,t):p=new rr(i.shape,e),o.runWebGLProgram(p,[i],l)}}function mn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,b]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[w,I]=x,T={dataId:w.dataId,dtype:w.dtype,shape:l.shape},C={dataId:I.dataId,dtype:I.dtype,shape:u.shape},E=new Ul(e,l.shape,u.shape);return p.runWebGLProgram(E,[T,C],fa(w.dtype,I.dtype))}),y=_s({inputs:{real:g,imag:b},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(b),y}let d=s||fa(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(m):m,b=l.dtype==="string"?N.fromUint8ToStringArray(f):f,[y,x]=r(l.shape,u.shape,g,b,d),w=p.makeTensorInfo(x,d),I=p.texData.get(w.dataId);return I.values=y,w}let c=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Nd(t,l.shape,u.shape,n):h=new Ul(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function kc(e,t=!1){if(e==="linear")return t?I9:y9;if(e==="relu")return t?N9:v9;if(e==="elu")return t?S9:x9;if(e==="relu6")return t?T9:w9;if(e==="prelu")return t?zE:LE;if(e==="leakyrelu")return t?OE:PE;if(e==="sigmoid")return t?C9:k9;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var WE=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,this.enableShapeUniforms=vn(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=a?"i * 2, rc.y":"rc.y, i * 2",c=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 uQ(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nd(YE,a.shape,r.shape):new Gl(XE,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var pQ={kernelName:bo,backendName:"webgl",kernelFunc:uQ},cp="if (isnan(x)) return x;";function Ye({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 d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=G().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new ts(i.shape,t):p=new rr(i.shape,e),o.runWebGLProgram(p,[i],l)}}function mn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,b]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[w,I]=x,T={dataId:w.dataId,dtype:w.dtype,shape:l.shape},C={dataId:I.dataId,dtype:I.dtype,shape:u.shape},E=new Gl(e,l.shape,u.shape);return p.runWebGLProgram(E,[T,C],ga(w.dtype,I.dtype))}),y=Es({inputs:{real:g,imag:b},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(b),y}let d=s||ga(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(m):m,b=l.dtype==="string"?N.fromUint8ToStringArray(f):f,[y,x]=r(l.shape,u.shape,g,b,d),w=p.makeTensorInfo(x,d),I=p.texData.get(w.dataId);return I.values=y,w}let c=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Nd(t,l.shape,u.shape,n):h=new Gl(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function kc(e,t=!1){if(e==="linear")return t?U9:L9;if(e==="relu")return t?H9:W9;if(e==="elu")return t?G9:z9;if(e==="relu6")return t?q9:B9;if(e==="prelu")return t?YE:XE;if(e==="leakyrelu")return t?KE:jE;if(e==="sigmoid")return t?j9:V9;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var ZE=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,this.enableShapeUniforms=vn(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=a?"i * 2, rc.y":"rc.y, i * 2",c=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) { @@ -1344,7 +1348,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}},BI={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},VI=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=` + `}},GI={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},HI=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=` float binaryOpComplex( float areal, float aimag, float breal, float bimag) { ${e} @@ -1357,7 +1361,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float bimag = getBImagAtOutCoords(); setOutput(binaryOpComplex(areal, aimag, breal, bimag)); } - `}},UI="return a * b;";function q1(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=N.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new VI(BI.REAL,a.shape,r.shape),p=new VI(BI.IMAG,a.shape,r.shape),d=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=_s({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[u,p]=BJ(a.shape,r.shape,o.values,l.values,s),d=n.makeTensorInfo(p,s),c=n.texData.get(d.dataId);return c.values=u,d}let i;return G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Nd(UI,a.shape,r.shape):i=new Ul(UI,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var H9={kernelName:po,backendName:"webgl",kernelFunc:q1};function q9(e,t,n){let a=[bi(e.shape),...yi(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[bi(t),...yi(t)],i=new RE(s,a),o=!0,l=[a],u=n.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ce(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!wc(r.shape,l)&&!(p.texture!==null&&wc(p.shape,l))?q9(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var j9={kernelName:Au,backendName:"webgl",kernelFunc:ce},GI=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 p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%n>0&&(u=` + `}},qI="return a * b;";function K1(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=N.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new HI(GI.REAL,a.shape,r.shape),p=new HI(GI.IMAG,a.shape,r.shape),d=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Es({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[u,p]=o9(a.shape,r.shape,o.values,l.values,s),d=n.makeTensorInfo(p,s),c=n.texData.get(d.dataId);return c.values=u,d}let i;return G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Nd(qI,a.shape,r.shape):i=new Gl(qI,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var cQ={kernelName:ho,backendName:"webgl",kernelFunc:K1};function dQ(e,t,n){let a=[xi(e.shape),...vi(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[xi(t),...vi(t)],i=new HE(s,a),o=!0,l=[a],u=n.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ce(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!wc(r.shape,l)&&!(p.texture!==null&&wc(p.shape,l))?dQ(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var hQ={kernelName:Fu,backendName:"webgl",kernelFunc:ce},jI=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 p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%n>0&&(u=` if (inIdx < 0 || inIdx >= ${r}) { return 0.0; } @@ -1410,7 +1414,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(sumValue); } - `}},K9=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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 u=Math.floor(n/4)*4,p=n%4,d=` + `}},mQ=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}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,p=n%4,d=` if (${t==="sum"}) { sumValue += dot(values, ones); } else if (${t==="prod"}) { @@ -1502,12 +1506,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function X9(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=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function jo(e,t,n,a){let r=X9(e.shape),s=e;for(let i=0;i6)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;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=ct(this.rank),r=DE("rc",this.rank),s=new Array(this.rank);for(let u=0;u6)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;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=ct(this.rank),r=GE("rc",this.rank),s=new Array(this.rank);for(let u=0;u`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let w=n?[b,d,h]:[b,h,d],I=a?[y,m,c]:[y,c,m],T=ce({inputs:{x:e},backend:r,attrs:{shape:w}}),C=ce({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[T,C],F=Math.max(b,y),D=n?T.shape[1]:T.shape[2],$=s!=null,S=i!=null,M=l==="leakyrelu",B=l!=null?kc(l,!0):null,U=$||S||M||B!=null,H;if((h===1||m===1)&&D>BE&&U===!1){let K=T,Z=C;n&&(K=Sn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),E.push(K)),a&&(Z=Sn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(Z));let J=m!==1,ee=m===1,ae=K;J&&(ae=ce({inputs:{x:K},backend:r,attrs:{shape:[F,D,1]}}),E.push(ae));let te=m===1?2:1,re=Z;ee&&(re=ce({inputs:{x:Z},backend:r,attrs:{shape:[F,1,D]}}),E.push(re));let se=q1({inputs:{a:ae,b:re},backend:r});H=Lf({inputs:{x:se},backend:r,attrs:{axis:te,keepDims:!0}}),E.push(se)}else{let K=fa(e.dtype,t.dtype),Z=new WE(w,I,[F,h,m],n,a,$,B,S,M),J=[T,C];if(s!=null&&J.push(s),S&&J.push(i),M){let ee=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));J.push(ee),E.push(ee)}H=r.runWebGLProgram(Z,J,K)}let j=ce({inputs:{x:H},backend:r,attrs:{shape:x}});E.push(H);for(let K of E)r.disposeIntermediateTensorInfo(K);return j}function nQ(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return um({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var aQ={kernelName:ai,backendName:"webgl",kernelFunc:nQ},HI="return abs(x);";function rQ(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=FE(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new es(a.shape,HI):r=new rr(a.shape,HI),n.runWebGLProgram(r,[a],a.dtype)}var sQ={kernelName:Hl,backendName:"webgl",kernelFunc:rQ},iQ=Ma+` + `}};function Lf(e,t,n){let a=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new yQ(e.shape,t):new gQ(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function xQ(e,t,n,a){let r=t,s=e.shape.length,i=v.parseAxisParam(r,e.shape),o=i,l=N.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=Lf(e,l,a),o=N.getInnerMostAxes(o.length,s)),N.assertAxesAreInnerMostDims("sum",o,s);let[d,c]=N.computeOutAndReduceShapes(p.shape,o),h=d;n&&(h=N.expandShapeToKeepDim(d,i));let m=v.sizeFromShape(c),f=v.sizeFromShape(e.shape)/m,g=ce({inputs:{x:p},attrs:{shape:[f,m]},backend:a}),b=Rm(e.dtype),y=Xo(g,b,"sum",a),x=ce({inputs:{x:y},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(y),u&&a.disposeIntermediateTensorInfo(p),x}function zf(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return xQ(r,s,i,n)}var vQ={kernelName:Do,backendName:"webgl",kernelFunc:zf};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 p=0;p`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let w=n?[b,d,h]:[b,h,d],I=a?[y,m,c]:[y,c,m],T=ce({inputs:{x:e},backend:r,attrs:{shape:w}}),C=ce({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[T,C],F=Math.max(b,y),D=n?T.shape[1]:T.shape[2],$=s!=null,S=i!=null,M=l==="leakyrelu",B=l!=null?kc(l,!0):null,U=$||S||M||B!=null,H;if((h===1||m===1)&&D>JE&&U===!1){let K=T,Z=C;n&&(K=Sn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),E.push(K)),a&&(Z=Sn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(Z));let J=m!==1,ee=m===1,ae=K;J&&(ae=ce({inputs:{x:K},backend:r,attrs:{shape:[F,D,1]}}),E.push(ae));let te=m===1?2:1,re=Z;ee&&(re=ce({inputs:{x:Z},backend:r,attrs:{shape:[F,1,D]}}),E.push(re));let se=K1({inputs:{a:ae,b:re},backend:r});H=zf({inputs:{x:se},backend:r,attrs:{axis:te,keepDims:!0}}),E.push(se)}else{let K=ga(e.dtype,t.dtype),Z=new ZE(w,I,[F,h,m],n,a,$,B,S,M),J=[T,C];if(s!=null&&J.push(s),S&&J.push(i),M){let ee=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));J.push(ee),E.push(ee)}H=r.runWebGLProgram(Z,J,K)}let j=ce({inputs:{x:H},backend:r,attrs:{shape:x}});E.push(H);for(let K of E)r.disposeIntermediateTensorInfo(K);return j}function kQ(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return um({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var IQ={kernelName:si,backendName:"webgl",kernelFunc:kQ},KI="return abs(x);";function SQ(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=VE(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ts(a.shape,KI):r=new rr(a.shape,KI),n.runWebGLProgram(r,[a],a.dtype)}var NQ={kernelName:ql,backendName:"webgl",kernelFunc:SQ},TQ=Ma+` if (abs(x) > 1.) { return NAN; } return acos(x); -`,oQ=Ye({opSnippet:iQ}),lQ={kernelName:wi,backendName:"webgl",kernelFunc:oQ},uQ=Ma+` +`,CQ=Ye({opSnippet:TQ}),_Q={kernelName:Ii,backendName:"webgl",kernelFunc:CQ},EQ=Ma+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,pQ=Ye({opSnippet:uQ}),cQ={kernelName:ki,backendName:"webgl",kernelFunc:pQ},qI="return a + b;",dQ=mn({opSnippet:qI,packedOpSnippet:qI,supportsComplex:!0,cpuKernelImpl:wJ}),hQ={kernelName:bs,backendName:"webgl",kernelFunc:dQ},mQ=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));`,AQ=Ye({opSnippet:EQ}),FQ={kernelName:Si,backendName:"webgl",kernelFunc:AQ},XI="return a + b;",$Q=mn({opSnippet:XI,packedOpSnippet:XI,supportsComplex:!0,cpuKernelImpl:BJ}),DQ={kernelName:bs,backendName:"webgl",kernelFunc:$Q},RQ=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(` `)} @@ -1539,7 +1543,7 @@ return log(x + sqrt(x * x - 1.0));`,pQ=Ye({opSnippet:uQ}),cQ={kernelName:ki,back float result = ${a}; setOutput(result); } - `}},fQ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` + `}},MQ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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(` `)} @@ -1547,7 +1551,7 @@ return log(x + sqrt(x * x - 1.0));`,pQ=Ye({opSnippet:uQ}),cQ={kernelName:ki,back vec4 result = ${a}; setOutput(result); } - `}};function Ph(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return na({inputs:{x:a[0]},backend:n});if(a.length>G().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Ph({inputs:a.slice(0,o),backend:n}),u=Ph({inputs:a.slice(o),backend:n});return Ph({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>fa(o,l)),s=a.map(o=>o.shape),i=G().getBool("WEBGL_PACK")?new fQ(a[0].shape,s):new mQ(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var gQ={kernelName:Ii,backendName:"webgl",kernelFunc:Ph};function bQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=jo(f,f.dtype,"all",n),b;if(i){let y=N.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var yQ={kernelName:ql,backendName:"webgl",kernelFunc:bQ};function xQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=jo(f,f.dtype,"any",n),b;if(i){let y=N.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var vQ={kernelName:jl,backendName:"webgl",kernelFunc:xQ},wQ=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 Ph(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return aa({inputs:{x:a[0]},backend:n});if(a.length>G().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Ph({inputs:a.slice(0,o),backend:n}),u=Ph({inputs:a.slice(o),backend:n});return Ph({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ga(o,l)),s=a.map(o=>o.shape),i=G().getBool("WEBGL_PACK")?new MQ(a[0].shape,s):new RQ(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var PQ={kernelName:Ni,backendName:"webgl",kernelFunc:Ph};function OQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=Xo(f,f.dtype,"all",n),b;if(i){let y=N.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var LQ={kernelName:jl,backendName:"webgl",kernelFunc:OQ};function zQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=Xo(f,f.dtype,"any",n),b;if(i){let y=N.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var WQ={kernelName:Kl,backendName:"webgl",kernelFunc:zQ},BQ=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]; @@ -1567,7 +1571,7 @@ return log(x + sqrt(x * x - 1.0));`,pQ=Ye({opSnippet:uQ}),cQ={kernelName:ki,back } setOutput(float(bestIndex)); } - `}},kQ=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let 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=ct(o),u=In("coords",o),p,d;if(s===1){d=o+1;let C=ct(d);p=` + `}},VQ=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let 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=ct(o),u=In("coords",o),p,d;if(s===1){d=o+1;let C=ct(d);p=` ${C} sourceLocR = ${C}(${u.join()}, 0); ++${u[o-1]}; ${C} sourceLocG = ${C}(${u.join()}, 0); @@ -1629,25 +1633,25 @@ return log(x + sqrt(x * x - 1.0));`,pQ=Ye({opSnippet:uQ}),cQ={kernelName:ki,back } setOutput(bestIndex); } - `}};function VE(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=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new wQ(o,n,a==null),u=[t];a!=null&&u.push(a);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let d=VE(e,t,n,p);return e.disposeIntermediateTensorInfo(p),d}function UE(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=N.computeOptimalWindowSize(s),o=new kQ(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=UE(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}return u}function GE(e,t,n,a){let r=[n];if(N.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!G().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=N.computeOutAndReduceShapes(l.shape,r),d=v.sizeFromShape(p),c=ce({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});s.push(c);let h=VE(e,c,a);s.push(h);let m=ce({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return UE(e,t,a)}function IQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=GE(n,l,i[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var SQ={kernelName:Kl,backendName:"webgl",kernelFunc:IQ};function NQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=GE(n,l,i[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var TQ={kernelName:Xl,backendName:"webgl",kernelFunc:NQ},CQ=Ma+` + `}};function QE(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=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new BQ(o,n,a==null),u=[t];a!=null&&u.push(a);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let d=QE(e,t,n,p);return e.disposeIntermediateTensorInfo(p),d}function eA(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=N.computeOptimalWindowSize(s),o=new 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UQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=tA(n,l,i[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var GQ={kernelName:Xl,backendName:"webgl",kernelFunc:UQ};function HQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=tA(n,l,i[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var qQ={kernelName:Yl,backendName:"webgl",kernelFunc:HQ},jQ=Ma+` if (abs(x) > 1.) { return NAN; } return asin(x); 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2.0;`,oee=Ye({opSnippet:iee}),lee={kernelName:Ei,backendName:"webgl",kernelFunc:oee},Ic=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,u=e.dilationWidth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,c=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) * ${e.inChannels} + d`,b="0.0";if(m||(b="-1.0 / 1e-20"),n){let C=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${c}, ${h}); @@ -1788,7 +1792,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(${x}); } - `}},j1=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,u=e.dilationDepth,p=e.dilationHeight,d=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let F=">=";this.userCode=` + `}},X1=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,u=e.dilationDepth,p=e.dilationHeight,d=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let F=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${g}, ${b}); @@ -1951,7 +1955,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(${I}); } - `}};function UQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ip(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=N.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return na({inputs:{x:r},backend:n});let d=new Ic(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var GQ={kernelName:Ei,backendName:"webgl",kernelFunc:UQ};function HQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=[1,1,1],d=N.computePool3DInfo(r.shape,s,i,p,o,l,u),c=new j1(d,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var qQ={kernelName:Yl,backendName:"webgl",kernelFunc:HQ},jQ=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,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,d=1/(t*n);this.userCode=` + `}};function uee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ip(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=N.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return aa({inputs:{x:r},backend:n});let d=new Ic(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var pee={kernelName:Fi,backendName:"webgl",kernelFunc:uee};function cee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=[1,1,1],d=N.computePool3DInfo(r.shape,s,i,p,o,l,u),c=new X1(d,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var dee={kernelName:Zl,backendName:"webgl",kernelFunc:cee},hee=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,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,d=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${u}, ${p}); const float avgMultiplier = float(${d}); @@ -1993,7 +1997,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(dotProd); } - `}},KQ=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,u=e.dilationWidth,p=e.effectiveFilterDepth,d=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=p-1-e.padInfo.front,m=d-1-e.padInfo.top,f=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=` + `}},mee=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,u=e.dilationWidth,p=e.effectiveFilterDepth,d=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=p-1-e.padInfo.front,m=d-1-e.padInfo.top,f=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=` const ivec3 pads = ivec3(${h}, ${m}, ${f}); const float avgMultiplier = float(${g}); @@ -2049,7 +2053,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(dotProd); } - `}};function XQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=N.computePool3DInfo(i.shape,o,l,d,u,p),h=new KQ(c);return n.runWebGLProgram(h,[r],i.dtype)}var YQ={kernelName:_c,backendName:"webgl",kernelFunc:XQ};function ZQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ip([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=new jQ(p);return n.runWebGLProgram(d,[r],i.dtype)}var JQ={kernelName:bm,backendName:"webgl",kernelFunc:ZQ};function QQ(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 eee={kernelName:Ai,backendName:"webgl",kernelFunc:QQ},tee=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function fee(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=N.computePool3DInfo(i.shape,o,l,d,u,p),h=new mee(c);return n.runWebGLProgram(h,[r],i.dtype)}var gee={kernelName:_c,backendName:"webgl",kernelFunc:fee};function bee(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ip([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=new hee(p);return n.runWebGLProgram(d,[r],i.dtype)}var yee={kernelName:bm,backendName:"webgl",kernelFunc:bee};function xee(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 vee={kernelName:$i,backendName:"webgl",kernelFunc:xee},wee=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2059,7 +2063,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam float inv = scale * inversesqrt(variance + float(${s})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}},nee=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}},kee=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${o}; @@ -2072,7 +2076,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam setOutput((x - mean) * inv + offset); } - `}},aee=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=G().getBool("WEBGL_PACK_NORMALIZATION")?new nee(a.shape,r.shape,s.shape,p,d,l):new tee(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},ree={kernelName:ji,backendName:"webgl",kernelFunc:aee},see=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ct(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=iee(this.rank),a,r=e.map((s,i)=>`sourceLoc.${Qx[i]} = start[${i}] + coords.${Qx[i]};`);a=` + `}},Iee=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.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 u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=G().getBool("WEBGL_PACK_NORMALIZATION")?new kee(a.shape,r.shape,s.shape,p,d,l):new wee(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},See={kernelName:Xi,backendName:"webgl",kernelFunc:Iee},Nee=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ct(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Tee(this.rank),a,r=e.map((s,i)=>`sourceLoc.${ev[i]} = start[${i}] + coords.${ev[i]};`);a=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${r.join(` @@ -2082,7 +2086,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam ${a} setOutput(getSource(${n})); } - `}},Qx=["x","y","z","w","u","v"];function iee(e){if(e===1)return"sourceLoc";if(e<=6)return Qx.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var oee=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ct(this.rank),n=In("coords",this.rank),a=In("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=` + `}},ev=["x","y","z","w","u","v"];function Tee(e){if(e===1)return"sourceLoc";if(e<=6)return ev.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Cee=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ct(this.rank),n=In("coords",this.rank),a=In("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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]}; @@ -2111,7 +2115,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam ${o} setOutput(result); } - `}};function lee(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=Kt.computeFlatOffset(t,v.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 dp(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=Kt.parseSliceParams(r,s,i);if(Kt.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=JJ(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=Kt.isSliceContinous(r.shape,o,l);if(u||!p){let d=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new oee(l):new see(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),lee(r,o,l,n)}var uee={kernelName:Ou,backendName:"webgl",kernelFunc:dp},pee=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=[],m=ce({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Sn({inputs:{x:m},backend:n,attrs:{perm:u}}),g=ce({inputs:{x:f},backend:n,attrs:{shape:p}}),b=dp({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},cee={kernelName:Zl,backendName:"webgl",kernelFunc:pee};function dee(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),u=AE(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var hee={kernelName:Jl,backendName:"webgl",kernelFunc:dee};function mee(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var fee={kernelName:Ec,backendName:"webgl",kernelFunc:mee},gee="return float(a != b);",HE=mn({opSnippet:gee,cpuKernelImpl:UJ,dtype:"bool"}),bee={kernelName:Su,backendName:"webgl",kernelFunc:HE};function Td(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return na({inputs:{x:r.complexTensorInfos.real},backend:n})}var yee={kernelName:Fm,backendName:"webgl",kernelFunc:Td},xee="return float(int(x));";function vee(e,t){let n=new rr(e.shape,xee),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function ev(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return na({inputs:{x:r},backend:n});let i=Nt(r.shape),o=ev({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=_s({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Td({inputs:{input:r},backend:n}),o=ev({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=na({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(n.shouldExecuteOnCPU([r])){let i=n.texData.get(r.dataId).values,[o,l,u]=IJ(i,r.shape,r.dtype,s);return n.makeTensorInfo(o,l,u)}if(s==="int32")return vee(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=HE({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var wee={kernelName:Fi,backendName:"webgl",kernelFunc:ev},jI="return ceil(x);",kee=Ye({opSnippet:jI,packedOpSnippet:jI,cpuKernelImpl:SJ}),Iee={kernelName:$i,backendName:"webgl",kernelFunc:kee},See=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + `}};function _ee(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=Kt.computeFlatOffset(t,v.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 dp(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=Kt.parseSliceParams(r,s,i);if(Kt.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=y9(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=Kt.isSliceContinous(r.shape,o,l);if(u||!p){let d=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Cee(l):new Nee(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),_ee(r,o,l,n)}var Eee={kernelName:Lu,backendName:"webgl",kernelFunc:dp},Aee=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=[],m=ce({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Sn({inputs:{x:m},backend:n,attrs:{perm:u}}),g=ce({inputs:{x:f},backend:n,attrs:{shape:p}}),b=dp({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},Fee={kernelName:Jl,backendName:"webgl",kernelFunc:Aee};function $ee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=BE(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var Dee={kernelName:Ql,backendName:"webgl",kernelFunc:$ee};function Ree(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var Mee={kernelName:Ec,backendName:"webgl",kernelFunc:Ree},Pee="return float(a != b);",nA=mn({opSnippet:Pee,cpuKernelImpl:u9,dtype:"bool"}),Oee={kernelName:Nu,backendName:"webgl",kernelFunc:nA};function Td(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return aa({inputs:{x:r.complexTensorInfos.real},backend:n})}var Lee={kernelName:$m,backendName:"webgl",kernelFunc:Td},zee="return float(int(x));";function Wee(e,t){let n=new rr(e.shape,zee),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function tv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return aa({inputs:{x:r},backend:n});let i=Nt(r.shape),o=tv({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Es({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Td({inputs:{input:r},backend:n}),o=tv({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=aa({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(n.shouldExecuteOnCPU([r])){let i=n.texData.get(r.dataId).values,[o,l,u]=UJ(i,r.shape,r.dtype,s);return n.makeTensorInfo(o,l,u)}if(s==="int32")return Wee(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=nA({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 Bee={kernelName:Di,backendName:"webgl",kernelFunc:tv},YI="return ceil(x);",Vee=Ye({opSnippet:YI,packedOpSnippet:YI,cpuKernelImpl:GJ}),Uee={kernelName:Ri,backendName:"webgl",kernelFunc:Vee},Gee=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { float value = getAAtOutCoords(); @@ -2122,7 +2126,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam setOutput(clamp(value, minVal, maxVal)); } - `}},Nee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + `}},Hee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { vec4 value = getAAtOutCoords(); @@ -2133,7 +2137,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function Tee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;G().getBool("WEBGL_PACK_CLIP")?o=new Nee(r.shape):o=new See(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var Cee={kernelName:ys,backendName:"webgl",kernelFunc:Tee},_ee=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + `}};function qee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;G().getBool("WEBGL_PACK_CLIP")?o=new Hee(r.shape):o=new Gee(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var jee={kernelName:ys,backendName:"webgl",kernelFunc:qee},Kee=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2146,7 +2150,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function KI(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Eee(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new _ee(a.shape),i=[KI(a,r.complexTensorInfos.real),KI(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var Aee={kernelName:Ac,backendName:"webgl",kernelFunc:Eee},Fee=class{constructor(e){this.outputShape=[],this.outputShape=N.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${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;ms===a?`${r} - ${n}`:r).join()}function zf(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return na({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Dee={kernelName:Nm,backendName:"webgl",kernelFunc:zf};function nc(e,t,n){let a=e[0].dtype;if(a==="complex64"){let h=e.map(y=>Td({inputs:{input:y},backend:n})),m=e.map(y=>zf({inputs:{input:y},backend:n})),f=nc(h,t,n),g=nc(m,t,n),b=_s({inputs:{real:f,imag:g},backend:n});return h.forEach(y=>n.disposeIntermediateTensorInfo(y)),m.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),b}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let h=e.map(w=>{let I=[-1,v.sizeFromShape(w.shape.slice(t))];return ce({inputs:{x:w},backend:n,attrs:{shape:I}})}),m=h.map(w=>({vals:n.readSync(w.dataId),shape:w.shape})),f=N.computeOutShape(h.map(w=>w.shape),1),g=h[0].shape[0]===1,b=NJ(m,f,a,g),y=N.computeOutShape(e.map(w=>w.shape),t),x=n.makeTensorInfo(y,a,b);return h.forEach(w=>n.disposeIntermediateTensorInfo(w)),x}let s=e.filter(h=>v.sizeFromShape(h.shape)>0),i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new rr(e[0].shape,Xr):new es(e[0].shape,Xr);return n.runWebGLProgram(h,e,a)}let o=G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let f=0;fm.shape),t);return n.runWebGLProgram(h,s,a)}let{tensors2D:l,outShape:u}=Ree(s,t,n),p=new Fee(l.map(h=>h.shape)),d=n.runWebGLProgram(p,l,a);l.forEach(h=>n.disposeIntermediateTensorInfo(h));let c=ce({inputs:{x:d},attrs:{shape:u},backend:n});return n.disposeIntermediateTensorInfo(d),c}function Ree(e,t,n){let a=N.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ce({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function qE(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);N.assertParamsConsistent(i,s);let o=N.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?na({inputs:{x:l[0]},backend:n}):nc(l,s,n)}var Mee={kernelName:Ql,backendName:"webgl",kernelFunc:qE},jE=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,u=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,b=f?2:3,y=f?3:1,x="",w="";n&&(a?x=`float activation(float a) { + `}};function Ch(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function Wf(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return aa({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Qee={kernelName:Tm,backendName:"webgl",kernelFunc:Wf};function nc(e,t,n){let a=e[0].dtype;if(a==="complex64"){let h=e.map(y=>Td({inputs:{input:y},backend:n})),m=e.map(y=>Wf({inputs:{input:y},backend:n})),f=nc(h,t,n),g=nc(m,t,n),b=Es({inputs:{real:f,imag:g},backend:n});return h.forEach(y=>n.disposeIntermediateTensorInfo(y)),m.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),b}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let h=e.map(w=>{let I=[-1,v.sizeFromShape(w.shape.slice(t))];return ce({inputs:{x:w},backend:n,attrs:{shape:I}})}),m=h.map(w=>({vals:n.readSync(w.dataId),shape:w.shape})),f=N.computeOutShape(h.map(w=>w.shape),1),g=h[0].shape[0]===1,b=HJ(m,f,a,g),y=N.computeOutShape(e.map(w=>w.shape),t),x=n.makeTensorInfo(y,a,b);return h.forEach(w=>n.disposeIntermediateTensorInfo(w)),x}let s=e.filter(h=>v.sizeFromShape(h.shape)>0),i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new rr(e[0].shape,Yr):new ts(e[0].shape,Yr);return n.runWebGLProgram(h,e,a)}let o=G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let f=0;fm.shape),t);return n.runWebGLProgram(h,s,a)}let{tensors2D:l,outShape:u}=ete(s,t,n),p=new Zee(l.map(h=>h.shape)),d=n.runWebGLProgram(p,l,a);l.forEach(h=>n.disposeIntermediateTensorInfo(h));let c=ce({inputs:{x:d},attrs:{shape:u},backend:n});return n.disposeIntermediateTensorInfo(d),c}function ete(e,t,n){let a=N.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ce({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function aA(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);N.assertParamsConsistent(i,s);let o=N.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?aa({inputs:{x:l[0]},backend:n}):nc(l,s,n)}var tte={kernelName:eu,backendName:"webgl",kernelFunc:aA},rA=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,u=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,b=f?2:3,y=f?3:1,x="",w="";n&&(a?x=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?x=`float activation(float a) { @@ -2326,7 +2330,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam ${w} setOutput(result); } - `}},Pee=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` + `}},nte=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${r}, ${s}, ${i}); const ivec3 pads = ivec3(${t}, ${n}, ${a}); @@ -2414,7 +2418,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(dotProd); } - `}},KE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,d=` + `}},sA=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,d=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let f=0;f=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function XE({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),p=n.inChannels,d=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,b=[];if(s!=null){let y=pm(s.shape,h);y!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:y}}),b.push(s))}if(r!=null){let y=pm(r.shape,h);y!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:y}}),b.push(r))}if(!((d===1||c===1)&&p>BE)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let y=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,y,n.inChannels],dtype:e.dtype},w=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(wc(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(I);let T=um({a:x,b:I,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(T.dataId);v.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=w,C.shape=n.outShape,g=na({inputs:{x:T},backend:a}),g.shape=n.outShape,b.push(T)}else{let y=n.outHeight*n.outWidth,x=ce({inputs:{x:e},backend:a,attrs:{shape:h?[n.batchSize,y,n.inChannels]:[n.batchSize,n.inChannels,y]}}),w=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=um({a:h?x:w,b:h?w:x,transposeA:!h,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ce({inputs:{x:I},backend:a,attrs:{shape:n.outShape}}),b.push(x),b.push(w),b.push(I)}for(let y of b)a.disposeIntermediateTensorInfo(y);return g}function YE({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,b=[n.batchSize,f,g],y=!0,x=!1,w=[];if(s!=null){let K=pm(s.shape,m);K!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:K}}),w.push(s))}if(r!=null){let K=pm(r.shape,m);K!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:K}}),w.push(r))}let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});w.push(I);let T=new Oee(b,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],E=a.runWebGLProgram(T,[e],"float32",C),F=ce({inputs:{x:E},backend:a,attrs:{shape:b}});w.push(E),w.push(F);let D=r!=null,$=s!=null,S=o==="leakyrelu",M=o?kc(o,!0):null,B=new WE(m?F.shape:I.shape,m?I.shape:F.shape,m?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,x,D,M,$,S),U=m?[F,I]:[I,F];if(r&&U.push(r),$&&U.push(s),S){let K=a.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));U.push(K),w.push(K)}let H=a.runWebGLProgram(B,U,"float32"),j=ce({inputs:{x:H},backend:a,attrs:{shape:n.outShape}});w.push(H);for(let K of w)a.disposeIntermediateTensorInfo(K);return j}function Lee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=XE({x:r,filter:s,convInfo:c,backend:n});else if(c.strideWidth<=2&&d==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let f=new KE(c),g=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];h=n.runWebGLProgram(f,[r,s],"float32",g)}else if(G().getBool("WEBGL_CONV_IM2COL"))h=YE({x:r,filter:s,convInfo:c,backend:n});else{let f=new jE(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ce({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var zee={kernelName:Di,backendName:"webgl",kernelFunc:Lee},Wee=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 pm(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function iA({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),p=n.inChannels,d=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,b=[];if(s!=null){let y=pm(s.shape,h);y!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:y}}),b.push(s))}if(r!=null){let y=pm(r.shape,h);y!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:y}}),b.push(r))}if(!((d===1||c===1)&&p>JE)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let y=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,y,n.inChannels],dtype:e.dtype},w=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(wc(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(I);let T=um({a:x,b:I,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(T.dataId);v.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=w,C.shape=n.outShape,g=aa({inputs:{x:T},backend:a}),g.shape=n.outShape,b.push(T)}else{let y=n.outHeight*n.outWidth,x=ce({inputs:{x:e},backend:a,attrs:{shape:h?[n.batchSize,y,n.inChannels]:[n.batchSize,n.inChannels,y]}}),w=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=um({a:h?x:w,b:h?w:x,transposeA:!h,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ce({inputs:{x:I},backend:a,attrs:{shape:n.outShape}}),b.push(x),b.push(w),b.push(I)}for(let y of b)a.disposeIntermediateTensorInfo(y);return g}function oA({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,b=[n.batchSize,f,g],y=!0,x=!1,w=[];if(s!=null){let K=pm(s.shape,m);K!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:K}}),w.push(s))}if(r!=null){let K=pm(r.shape,m);K!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:K}}),w.push(r))}let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});w.push(I);let T=new ate(b,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],E=a.runWebGLProgram(T,[e],"float32",C),F=ce({inputs:{x:E},backend:a,attrs:{shape:b}});w.push(E),w.push(F);let D=r!=null,$=s!=null,S=o==="leakyrelu",M=o?kc(o,!0):null,B=new ZE(m?F.shape:I.shape,m?I.shape:F.shape,m?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,x,D,M,$,S),U=m?[F,I]:[I,F];if(r&&U.push(r),$&&U.push(s),S){let K=a.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));U.push(K),w.push(K)}let H=a.runWebGLProgram(B,U,"float32"),j=ce({inputs:{x:H},backend:a,attrs:{shape:n.outShape}});w.push(H);for(let K of w)a.disposeIntermediateTensorInfo(K);return j}function rte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=iA({x:r,filter:s,convInfo:c,backend:n});else if(c.strideWidth<=2&&d==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let f=new sA(c),g=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];h=n.runWebGLProgram(f,[r,s],"float32",g)}else if(G().getBool("WEBGL_CONV_IM2COL"))h=oA({x:r,filter:s,convInfo:c,backend:n});else{let f=new rA(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ce({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var ste={kernelName:Mi,backendName:"webgl",kernelFunc:rte},ite=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; @@ -2694,7 +2698,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(dotProd); } - `}},Bee=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,u=s?2:3,p=s?3:1;this.userCode=` + `}},ote=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,u=s?2:3,p=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -2747,7 +2751,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(dotProd); } - `}},Vee=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` + `}},lte=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2789,7 +2793,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(dotProd); } - `}},Uee=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=` + `}},ute=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${o}, ${l}, ${u}); void main() { @@ -2846,7 +2850,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(dotProd); } - `}};function Gee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new Wee(c);return n.runWebGLProgram(h,[r,s],"float32")}var Hee={kernelName:xm,backendName:"webgl",kernelFunc:Gee},qee=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.filterHeight,n=e.filterWidth,a=t-1-e.padInfo.top,r=n-1-e.padInfo.left;this.userCode=` + `}};function pte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new ite(c);return n.runWebGLProgram(h,[r,s],"float32")}var cte={kernelName:vm,backendName:"webgl",kernelFunc:pte},dte=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.filterHeight,n=e.filterWidth,a=t-1-e.padInfo.top,r=n-1-e.padInfo.left;this.userCode=` const ivec2 pads = ivec2(${a}, ${r}); void main() { @@ -2920,17 +2924,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(result); } - `}};function jee(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=N.convertConv2DDataFormat(u),c=N.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d);if(G().getBool("WEBGL_PACK")&&d==="channelsLast"){let h=[[c.strideHeight,c.strideWidth]],m=new qee(c);return n.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new Bee(c);return n.runWebGLProgram(h,[r,s],"float32")}}var Kee={kernelName:Ri,backendName:"webgl",kernelFunc:jee};function Xee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=N.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new Pee(u);return n.runWebGLProgram(p,[r,s],"float32")}var Yee={kernelName:Mi,backendName:"webgl",kernelFunc:Xee};function Zee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=N.computeConv3DInfo(r.shape,l,i,1,o),p=new Vee(u);return n.runWebGLProgram(p,[r,s],"float32")}var Jee={kernelName:eu,backendName:"webgl",kernelFunc:Zee};function Qee(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=N.computeConv3DInfo(l,s.shape,o,1,i),p=new Uee(u);return n.runWebGLProgram(p,[r,s],"float32")}var ete={kernelName:tu,backendName:"webgl",kernelFunc:Qee},tte=cp+` + `}};function hte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=N.convertConv2DDataFormat(u),c=N.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d);if(G().getBool("WEBGL_PACK")&&d==="channelsLast"){let h=[[c.strideHeight,c.strideWidth]],m=new dte(c);return n.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new ote(c);return n.runWebGLProgram(h,[r,s],"float32")}}var mte={kernelName:Pi,backendName:"webgl",kernelFunc:hte};function fte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=N.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new nte(u);return n.runWebGLProgram(p,[r,s],"float32")}var gte={kernelName:Oi,backendName:"webgl",kernelFunc:fte};function bte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=N.computeConv3DInfo(r.shape,l,i,1,o),p=new lte(u);return n.runWebGLProgram(p,[r,s],"float32")}var yte={kernelName:tu,backendName:"webgl",kernelFunc:bte};function xte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=N.computeConv3DInfo(l,s.shape,o,1,i),p=new ute(u);return n.runWebGLProgram(p,[r,s],"float32")}var vte={kernelName:nu,backendName:"webgl",kernelFunc:xte},wte=cp+` return cos(x); -`,nte=` +`,kte=` vec4 result = cos(x); bvec4 isNaN = isnan(x); - ${qo} + ${Ko} return result; -`,ate=Ye({opSnippet:tte,packedOpSnippet:nte}),rte={kernelName:Pi,backendName:"webgl",kernelFunc:ate},ste=` +`,Ite=Ye({opSnippet:wte,packedOpSnippet:kte}),Ste={kernelName:Li,backendName:"webgl",kernelFunc:Ite},Nte=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,ite=Ye({opSnippet:ste}),ote={kernelName:Oi,backendName:"webgl",kernelFunc:ite},lte=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,w]=d>1?[`${(o-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` +`,Tte=Ye({opSnippet:Nte}),Cte={kernelName:zi,backendName:"webgl",kernelFunc:Tte},_te=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,w]=d>1?[`${(o-1)/(d-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(${y}); void main() { @@ -2991,20 +2995,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam setOutput(newValue); } } - `}},ute=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new lte(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},pte={kernelName:au,backendName:"webgl",kernelFunc:ute},Sc;(function(e){e.Prod="*",e.Sum="+"})(Sc||(Sc={}));var XI=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===Sc.Prod?"1.0":"0.0",i=n?s:`getX(${YI(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` + `}},Ete=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new _te(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},Ate={kernelName:ru,backendName:"webgl",kernelFunc:Ete},Sc;(function(e){e.Prod="*",e.Sum="+"})(Sc||(Sc={}));var JI=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===Sc.Prod?"1.0":"0.0",i=n?s:`getX(${QI(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` void main() { ${ct(r)} coords = getOutputCoords(); - int end = ${ZI(r,"coords",this.op)}; + int end = ${eS(r,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${l}) { int idx = ${u}; - ${ZI(r,"coords",this.op)} = idx; - val ${this.op}= getX(${YI(r,"coords",this.op)}); + ${eS(r,"coords",this.op)} = idx; + val ${this.op}= getX(${QI(r,"coords",this.op)}); } setOutput(val); } - `}};function YI(e,t,n){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 new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function ZI(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function ZE(e,t,n,a,r,s){let i=t.shape.length,o=N.getAxesPermutation([a],i),l=t;o!=null&&(l=Sn({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=N.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=na({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new XI(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new XI(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=N.getUndoAxesPermutation(o),h=Sn({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function cte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return ZE(Sc.Prod,r,n,s,i,o)}var dte={kernelName:nu,backendName:"webgl",kernelFunc:cte};function hte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return ZE(Sc.Sum,r,n,s,i,o)}var mte={kernelName:Li,backendName:"webgl",kernelFunc:hte};function fte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=AE(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=kJ(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var gte={kernelName:Fc,backendName:"webgl",kernelFunc:fte},bte=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` + `}};function QI(e,t,n){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 new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function eS(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function lA(e,t,n,a,r,s){let i=t.shape.length,o=N.getAxesPermutation([a],i),l=t;o!=null&&(l=Sn({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=N.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=aa({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new JI(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new JI(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=N.getUndoAxesPermutation(o),h=Sn({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function Fte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return lA(Sc.Prod,r,n,s,i,o)}var $te={kernelName:au,backendName:"webgl",kernelFunc:Fte};function Dte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return lA(Sc.Sum,r,n,s,i,o)}var Rte={kernelName:Wi,backendName:"webgl",kernelFunc:Dte};function Mte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=BE(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=VJ(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Pte={kernelName:Fc,backendName:"webgl",kernelFunc:Mte},Ote=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]; @@ -3023,7 +3027,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={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 yte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new bte(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var xte={kernelName:ru,backendName:"webgl",kernelFunc:yte},JE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`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 Lte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new Ote(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var zte={kernelName:su,backendName:"webgl",kernelFunc:Lte},uA=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?l=`float activation(float a) { @@ -3076,7 +3080,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam ${u} setOutput(result); } - `}},QE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=` + `}},pA=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=N.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;G().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new QE(d):c=new JE(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var wte={kernelName:zi,backendName:"webgl",kernelFunc:vte},kte=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 Wte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a,p=l;p==null&&(p=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=N.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;G().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new pA(d):c=new uA(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var Bte={kernelName:Bi,backendName:"webgl",kernelFunc:Wte},Vte=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; @@ -3300,7 +3304,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(dotProd); } - `}},Ite=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=` + `}},Ute=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}, ${i}); void main() { @@ -3345,13 +3349,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(dotProd); } - `}};function Ste(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=N.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new kte(d);return n.runWebGLProgram(c,[r,s],"float32")}var Nte={kernelName:vm,backendName:"webgl",kernelFunc:Ste};function Tte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=N.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new Ite(d);return n.runWebGLProgram(c,[r,s],"float32")}var Cte={kernelName:wm,backendName:"webgl",kernelFunc:Tte},_te=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function Gte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=N.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new Vte(d);return n.runWebGLProgram(c,[r,s],"float32")}var Hte={kernelName:wm,backendName:"webgl",kernelFunc:Gte};function qte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=N.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new Ute(d);return n.runWebGLProgram(c,[r,s],"float32")}var jte={kernelName:km,backendName:"webgl",kernelFunc:qte},Kte=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function Ete(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=v.sizeFromShape(a.shape),i=ce({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new _te(s),l=n.runWebGLProgram(o,[i],i.dtype),u=ce({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var Ate={kernelName:$c,backendName:"webgl",kernelFunc:Ete},Fte=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:u}=e,{top:p,left:d}=a;this.userCode=` + `}};function Xte(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=v.sizeFromShape(a.shape),i=ce({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new Kte(s),l=n.runWebGLProgram(o,[i],i.dtype),u=ce({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var Yte={kernelName:$c,backendName:"webgl",kernelFunc:Xte},Zte=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:d}=a;this.userCode=` const ivec2 strides = ivec2(${r}, ${s}); const ivec2 pads = ivec2(${p}, ${d}); const float neg_infinity = -3.4e38; @@ -3389,7 +3393,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam float result = curVal; setOutput(result); } - `}};function $te(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=N.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new Fte(u);p=n.runWebGLProgram(d,[r,s],"float32");let c=ce({inputs:{x:p},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(p),c}var Dte={kernelName:Wi,backendName:"webgl",kernelFunc:$te};function Rte(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(r,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=N.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f=0&&(c=Lf({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var Mte={kernelName:km,backendName:"webgl",kernelFunc:Rte},Pte="return (x >= 0.0) ? x : (exp(x) - 1.0);",Ote=` + `}};function Jte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=N.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new Zte(u);p=n.runWebGLProgram(d,[r,s],"float32");let c=ce({inputs:{x:p},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(p),c}var Qte={kernelName:Vi,backendName:"webgl",kernelFunc:Jte};function ene(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(r,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=N.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f=0&&(c=zf({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var tne={kernelName:Im,backendName:"webgl",kernelFunc:ene},nne="return (x >= 0.0) ? x : (exp(x) - 1.0);",ane=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3398,12 +3402,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,Lte=Ye({opSnippet:Pte,packedOpSnippet:Ote}),zte={kernelName:Vi,backendName:"webgl",kernelFunc:Lte},Wte="return (b >= 0.0) ? a : a * (b + 1.0);",Bte=` +`,rne=Ye({opSnippet:nne,packedOpSnippet:ane}),sne={kernelName:Gi,backendName:"webgl",kernelFunc:rne},ine="return (b >= 0.0) ? a : a * (b + 1.0);",one=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,Vte=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nd(Bte,a.shape,r.shape):new Ul(Wte,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},Ute={kernelName:su,backendName:"webgl",kernelFunc:Vte},Gte=` +`,lne=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nd(one,a.shape,r.shape):new Gl(ine,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},une={kernelName:iu,backendName:"webgl",kernelFunc:lne},pne=` return vec4(equal(a, b)); -`,Hte="return float(a == b);",qte=mn({opSnippet:Hte,packedOpSnippet:Gte,dtype:"bool",cpuKernelImpl:TJ}),jte={kernelName:ou,backendName:"webgl",kernelFunc:qte},Kte=` +`,cne="return float(a == b);",dne=mn({opSnippet:cne,packedOpSnippet:pne,dtype:"bool",cpuKernelImpl:qJ}),hne={kernelName:lu,backendName:"webgl",kernelFunc:dne},mne=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -3418,9 +3422,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={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)); -`,Xte=Ye({opSnippet:Kte}),Yte={kernelName:iu,backendName:"webgl",kernelFunc:Xte},Zte=cp+` +`,fne=Ye({opSnippet:mne}),gne={kernelName:ou,backendName:"webgl",kernelFunc:fne},bne=cp+` return exp(x); -`,Jte=` +`,yne=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -3429,7 +3433,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam result.a = isNaN.a ? x.a : result.a; return result; -`,eA=Ye({opSnippet:Zte,packedOpSnippet:Jte,cpuKernelImpl:CJ,dtype:"float32"}),Qte={kernelName:Ui,backendName:"webgl",kernelFunc:eA};function tv(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&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ce({inputs:{x:s},backend:a,attrs:{shape:o}})}var ene={kernelName:lu,backendName:"webgl",kernelFunc:tv},JI="return exp(x) - 1.0;",tne=Ye({opSnippet:JI,packedOpSnippet:JI,cpuKernelImpl:_J}),nne={kernelName:Gi,backendName:"webgl",kernelFunc:tne},QI=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 ${e}.`);this.userCode=` +`,cA=Ye({opSnippet:bne,packedOpSnippet:yne,cpuKernelImpl:jJ,dtype:"float32"}),xne={kernelName:Hi,backendName:"webgl",kernelFunc:cA};function nv(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ce({inputs:{x:s},backend:a,attrs:{shape:o}})}var vne={kernelName:uu,backendName:"webgl",kernelFunc:nv},tS="return exp(x) - 1.0;",wne=Ye({opSnippet:tS,packedOpSnippet:tS,cpuKernelImpl:KJ}),kne={kernelName:qi,backendName:"webgl",kernelFunc:wne},nS=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) { @@ -3462,12 +3466,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function tA(e,t,n){let a=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ce({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new QI("real",l,t),p=new QI("imag",l,t),d=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=_s({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=ce({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function ane(e){let{inputs:t,backend:n}=e,{input:a}=t;return tA(a,!1,n)}var rne={kernelName:Im,backendName:"webgl",kernelFunc:ane},sne=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function dA(e,t,n){let a=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ce({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new nS("real",l,t),p=new nS("imag",l,t),d=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Es({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=ce({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function Ine(e){let{inputs:t,backend:n}=e,{input:a}=t;return dA(a,!1,n)}var Sne={kernelName:Sm,backendName:"webgl",kernelFunc:Ine},Nne=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function Cd(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new sne(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var ine={kernelName:Dc,backendName:"webgl",kernelFunc:Cd},one=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `}};function Cd(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new Nne(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Tne={kernelName:Dc,backendName:"webgl",kernelFunc:Cd},Cne=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]; @@ -3481,7 +3485,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(outputValue); } - `}},lne={kernelName:uu,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new one(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},eS="return floor(x);",une=Ye({opSnippet:eS,packedOpSnippet:eS,cpuKernelImpl:EJ}),pne={kernelName:Hi,backendName:"webgl",kernelFunc:une},cne=` + `}},_ne={kernelName:pu,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new Cne(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},aS="return floor(x);",Ene=Ye({opSnippet:aS,packedOpSnippet:aS,cpuKernelImpl:XJ}),Ane={kernelName:ji,backendName:"webgl",kernelFunc:Ene},Fne=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3491,7 +3495,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } else { return NAN; } -`,dne=` +`,$ne=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -3512,7 +3516,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,hne=mn({opSnippet:cne,packedOpSnippet:dne,dtype:"int32"}),mne={kernelName:qi,backendName:"webgl",kernelFunc:hne},fne=class{constructor(e){this.variableNames=["A"];let t=_n(),[n,a]=e;this.outputShape=e,this.userCode=` +`,Dne=mn({opSnippet:Fne,packedOpSnippet:$ne,dtype:"int32"}),Rne={kernelName:Ki,backendName:"webgl",kernelFunc:Dne},Mne=class{constructor(e){this.variableNames=["A"];let t=_n(),[n,a]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3534,7 +3538,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam setOutput(floor(value * 255.0 + 0.5)); } - `}},gne=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=_n(),[n,a]=e;this.outputShape=e,this.userCode=` + `}},Pne=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=_n(),[n,a]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3568,7 +3572,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam ${t.output} = result; } - `}},bne={kernelName:Wh,backendName:"webgl",kernelFunc:yne},fl,lx=G().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function yne(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,l],d=[u,l,s];if(o||i){let f=G().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(fl==null||f!==lx)&&(lx=f,fl=document.createElement("canvas").getContext("2d",{willReadFrequently:lx})),fl.canvas.width=l,fl.canvas.height=u,fl.drawImage(r,0,0,l,u),r=fl.canvas}let c=n.makeTensorInfo(p,"int32");n.texData.get(c.dataId).usage=ca.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=G().getBool("WEBGL_PACK")?new gne(d):new fne(d),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function xne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=N.convertConv2DDataFormat(p),g=N.computeConv2DInfo(r.shape,s.shape,l,d,u,c,!1,f),b,y=[],x=i!=null,w=o!=null,I=h==="leakyrelu",T=()=>{let E=[r,s],F=(D,$)=>{if($==="NCHW"&&D.shape.length===1&&D.shape[0]!==1){let S=ce({inputs:{x:D},backend:n,attrs:{shape:[D.shape[0],1,1]}});return y.push(S),S}return D};if(x&&E.push(F(i,p)),w&&E.push(F(o,p)),I){let D=n.makeTensorInfo([],"float32",v.createScalarValue(m,"float32"));E.push(D),y.push(D)}return E};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=XE({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(g.strideWidth<=2&&f==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let E=h?kc(h,!0):null,F=new KE(g,x,E,w,I),D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],$=T();b=n.runWebGLProgram(F,$,"float32",D)}else if(G().getBool("WEBGL_CONV_IM2COL"))b=YE({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let E=h?kc(h,!1):null,F=new jE(g,x,E,w,I),D=T();b=n.runWebGLProgram(F,D,"float32")}let C=ce({inputs:{x:b},backend:n,attrs:{shape:g.outShape}});return y.push(b),y.forEach(E=>n.disposeIntermediateTensorInfo(E)),C}var vne={kernelName:ri,backendName:"webgl",kernelFunc:xne};function wne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:d,activation:c,leakyreluAlpha:h}=a,m=[],f=p;f==null&&(f=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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-`,Fne=mn({opSnippet:Ene,packedOpSnippet:Ane,cpuKernelImpl:$J,dtype:"bool"}),$ne={kernelName:du,backendName:"webgl",kernelFunc:Fne},Dne="return float(a >= b);",Rne=` +`,Zne=mn({opSnippet:Xne,packedOpSnippet:Yne,cpuKernelImpl:JJ,dtype:"bool"}),Jne={kernelName:hu,backendName:"webgl",kernelFunc:Zne},Qne="return float(a >= b);",eae=` return vec4(greaterThanEqual(a, b)); -`,Mne=mn({opSnippet:Dne,packedOpSnippet:Rne,dtype:"bool",cpuKernelImpl:DJ}),Pne={kernelName:Ki,backendName:"webgl",kernelFunc:Mne};function One(e){let{inputs:t,backend:n}=e,{input:a}=t;return tA(a,!0,n)}var Lne={kernelName:Sm,backendName:"webgl",kernelFunc:One},zne="return float(!isnan(x) && !isinf(x));",Wne=Ye({opSnippet:zne,dtype:"bool"}),Bne={kernelName:Yi,backendName:"webgl",kernelFunc:Wne},Vne="return float(isinf(x));",Une=Ye({opSnippet:Vne,dtype:"bool"}),Gne={kernelName:Zi,backendName:"webgl",kernelFunc:Une},Hne="return float(isnan(x));",qne=Ye({opSnippet:Hne,dtype:"bool"}),jne={kernelName:Ji,backendName:"webgl",kernelFunc:qne},Kne="return float(a < b);",Xne=` +`,tae=mn({opSnippet:Qne,packedOpSnippet:eae,dtype:"bool",cpuKernelImpl:QJ}),nae={kernelName:Yi,backendName:"webgl",kernelFunc:tae};function aae(e){let{inputs:t,backend:n}=e,{input:a}=t;return dA(a,!0,n)}var rae={kernelName:Nm,backendName:"webgl",kernelFunc:aae},sae="return float(!isnan(x) && !isinf(x));",iae=Ye({opSnippet:sae,dtype:"bool"}),oae={kernelName:Ji,backendName:"webgl",kernelFunc:iae},lae="return float(isinf(x));",uae=Ye({opSnippet:lae,dtype:"bool"}),pae={kernelName:Qi,backendName:"webgl",kernelFunc:uae},cae="return float(isnan(x));",dae=Ye({opSnippet:cae,dtype:"bool"}),hae={kernelName:eo,backendName:"webgl",kernelFunc:dae},mae="return float(a < b);",fae=` return vec4(lessThan(a, b)); -`,Yne=mn({opSnippet:Kne,packedOpSnippet:Xne,cpuKernelImpl:RJ,dtype:"bool"}),Zne={kernelName:hu,backendName:"webgl",kernelFunc:Yne},Jne="return float(a <= b);",Qne=` +`,gae=mn({opSnippet:mae,packedOpSnippet:fae,cpuKernelImpl:e9,dtype:"bool"}),bae={kernelName:mu,backendName:"webgl",kernelFunc:gae},yae="return float(a <= b);",xae=` return vec4(lessThanEqual(a, b)); -`,eae=mn({opSnippet:Jne,packedOpSnippet:Qne,cpuKernelImpl:MJ,dtype:"bool"}),tae={kernelName:mu,backendName:"webgl",kernelFunc:eae};function nae(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=PJ(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var aae={kernelName:fu,backendName:"webgl",kernelFunc:nae},rae=cp+` +`,vae=mn({opSnippet:yae,packedOpSnippet:xae,cpuKernelImpl:t9,dtype:"bool"}),wae={kernelName:fu,backendName:"webgl",kernelFunc:vae};function kae(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=n9(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var Iae={kernelName:gu,backendName:"webgl",kernelFunc:kae},Sae=cp+` return x < 0.0 ? 0./0. : log(x); -`,sae=` +`,Nae=` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); @@ -3608,18 +3612,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; -`,iae=Ye({opSnippet:rae,packedOpSnippet:sae,cpuKernelImpl:OJ}),oae={kernelName:eo,backendName:"webgl",kernelFunc:iae},lae=cp+` +`,Tae=Ye({opSnippet:Sae,packedOpSnippet:Nae,cpuKernelImpl:a9}),Cae={kernelName:no,backendName:"webgl",kernelFunc:Tae},_ae=cp+` return log(1.0 + x); -`,uae=Ye({opSnippet:lae}),pae={kernelName:to,backendName:"webgl",kernelFunc:uae},cae="return float(a >= 1.0 && b >= 1.0);",dae=` +`,Eae=Ye({opSnippet:_ae}),Aae={kernelName:ao,backendName:"webgl",kernelFunc:Eae},Fae="return float(a >= 1.0 && b >= 1.0);",$ae=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,hae=mn({opSnippet:cae,packedOpSnippet:dae,dtype:"bool"}),mae={kernelName:gu,backendName:"webgl",kernelFunc:hae},fae="return float(!(x >= 1.0));",gae=Ye({opSnippet:fae}),bae={kernelName:bu,backendName:"webgl",kernelFunc:gae},yae="return float(a >= 1.0 || b >= 1.0);",xae=` +`,Dae=mn({opSnippet:Fae,packedOpSnippet:$ae,dtype:"bool"}),Rae={kernelName:bu,backendName:"webgl",kernelFunc:Dae},Mae="return float(!(x >= 1.0));",Pae=Ye({opSnippet:Mae}),Oae={kernelName:yu,backendName:"webgl",kernelFunc:Pae},Lae="return float(a >= 1.0 || b >= 1.0);",zae=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,vae=mn({opSnippet:yae,packedOpSnippet:xae,dtype:"bool"}),wae={kernelName:yu,backendName:"webgl",kernelFunc:vae},kae=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` +`,Wae=mn({opSnippet:Lae,packedOpSnippet:zae,dtype:"bool"}),Bae={kernelName:xu,backendName:"webgl",kernelFunc:Wae},Vae=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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]; @@ -3638,7 +3642,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam float val = x * ${o}; setOutput(val); } - `}},Iae=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` + `}},Uae=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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; @@ -3700,7 +3704,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam vec4 result = xAtOutputCoords * ${o}; setOutput(result); } - `}},Sae=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=G().getBool("WEBGL_PACK_NORMALIZATION")?new Iae(r.shape,s,i,o,l):new kae(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},Nae={kernelName:no,backendName:"webgl",kernelFunc:Sae},Tae=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=` + `}},Gae=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=G().getBool("WEBGL_PACK_NORMALIZATION")?new Uae(r.shape,s,i,o,l):new Vae(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},Hae={kernelName:ro,backendName:"webgl",kernelFunc:Gae},qae=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]; @@ -3755,16 +3759,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(result); } - `}},Cae=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new Tae(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},_ae={kernelName:xu,backendName:"webgl",kernelFunc:Cae};function Eae(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=jo(i,e.dtype,"max",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function aA(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let y=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new qae(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},Kae={kernelName:vu,backendName:"webgl",kernelFunc:jae};function Xae(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Xo(i,e.dtype,"max",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function mA(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let y=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=N.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return na({inputs:{x:r},backend:n});let d=new Ic(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Pae={kernelName:so,backendName:"webgl",kernelFunc:Mae};function Oae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=N.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new j1(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var Lae={kernelName:vu,backendName:"webgl",kernelFunc:Oae},zae=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=` +`,Qae=mn({opSnippet:Zae,packedOpSnippet:Jae,cpuKernelImpl:s9}),ere={kernelName:io,backendName:"webgl",kernelFunc:Qae};function tre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ip(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=N.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return aa({inputs:{x:r},backend:n});let d=new Ic(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var nre={kernelName:oo,backendName:"webgl",kernelFunc:tre};function are(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=N.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new X1(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var rre={kernelName:wu,backendName:"webgl",kernelFunc:are},sre=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}, ${o}); void main() { @@ -3810,7 +3814,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(dotProd); } - `}},Wae=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,d=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` + `}},ire=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,d=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${p}, ${d}, ${c}); void main() { @@ -3874,16 +3878,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam } setOutput(dotProd); } - `}};function Bae(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=N.computePool3DInfo(i.shape,o,l,d,u,p),h=new j1(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new Wae(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var Vae={kernelName:Rc,backendName:"webgl",kernelFunc:Bae};function Uae(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;ip([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=N.computePool2DInfo(o.shape,l,u,1,p,d),h=!0,m=new Ic(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new zae(c),b=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),b}var Gae={kernelName:Tm,backendName:"webgl",kernelFunc:Uae};function Hae(e,t,n,a){let r=new Ic(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Ic(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var qae={kernelName:Cm,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];v.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=N.computePool2DInfo(a.shape,r,s,u,i),[d,c]=Hae(a,o,p,l);return[d,c]}};function jae(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=jo(i,"float32","mean",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var Kae={kernelName:io,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=v.parseAxisParam(s,a.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,w=new Array(o);for(let C=0;C{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];v.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=N.computePool2DInfo(a.shape,r,s,u,i),[d,c]=cre(a,o,p,l);return[d,c]}};function hre(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Xo(i,"float32","mean",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var mre={kernelName:lo,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=v.parseAxisParam(s,a.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,w=new Array(o);for(let C=0;Cu[0]+e[p]+u[1]);let a=e.length,r=ct(a),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=` +`,xre=mn({opSnippet:bre,packedOpSnippet:yre,cpuKernelImpl:i9}),vre={kernelName:po,backendName:"webgl",kernelFunc:xre},wre=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,p)=>u[0]+e[p]+u[1]);let a=e.length,r=ct(a),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).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}; @@ -3912,7 +3916,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam ${r} coords = outC - start; setOutput(getX(${o})); } - `}},nre=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=ct(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=In("rc",a),l=In("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,c="";if(a===1){let h=` + `}},kre=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=ct(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=In("rc",a),l=In("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,c="";if(a===1){let h=` ${r} source = rc; if (source < start) { source = start * 2 - source - ${d}; @@ -3968,13 +3972,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam ${c} setOutput(result); } - `}},are=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new nre(a.shape,r,s):new tre(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},rre={kernelName:uo,backendName:"webgl",kernelFunc:are},sre=`if (b == 0.0) return NAN; - return mod(a, b);`,ire=` + `}},Ire=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new kre(a.shape,r,s):new wre(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},Sre={kernelName:co,backendName:"webgl",kernelFunc:Ire},Nre=`if (b == 0.0) return NAN; + return mod(a, b);`,Tre=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - `+qo+` + `+Ko+` return result; -`,ore=mn({opSnippet:sre,packedOpSnippet:ire}),lre={kernelName:wu,backendName:"webgl",kernelFunc:ore},ure=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` +`,Cre=mn({opSnippet:Nre,packedOpSnippet:Tre}),_re={kernelName:ku,backendName:"webgl",kernelFunc:Cre},Ere=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -3994,11 +3998,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,BQ=Ye({opSnippet:WQ}),VQ={kernelNam // If no other event happened, last event happened. setOutput(float(${t-1})); } - `}},pre=` + `}},Are=` if (a == b) { return 1.0; }; -return a / b;`,cre=` +return a / b;`,Fre=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -4016,9 +4020,9 @@ return a / b;`,cre=` } return result; -`,rA=mn({opSnippet:pre,packedOpSnippet:cre,checkOutOfBounds:!0}),dre={kernelName:Bi,backendName:"webgl",kernelFunc:rA},tS="return a - b;",sA=mn({opSnippet:tS,packedOpSnippet:tS,supportsComplex:!0,cpuKernelImpl:o9}),hre={kernelName:Ro,backendName:"webgl",kernelFunc:sA};function iA(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=v.parseAxisParam([s],r.shape),o=aA({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=ce({inputs:{x:o},backend:n,attrs:{shape:l}}),p=sA({inputs:{a:r,b:u},backend:n}),d=eA({inputs:{x:p},backend:n}),c=Lf({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=ce({inputs:{x:c},backend:n,attrs:{shape:l}}),m=rA({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var mre={kernelName:$o,backendName:"webgl",kernelFunc:iA};function fre(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:iA({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new ure(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var gre={kernelName:ku,backendName:"webgl",kernelFunc:fre},bre=Ma+` +`,fA=mn({opSnippet:Are,packedOpSnippet:Fre,checkOutOfBounds:!0}),$re={kernelName:Ui,backendName:"webgl",kernelFunc:fA},rS="return a - b;",gA=mn({opSnippet:rS,packedOpSnippet:rS,supportsComplex:!0,cpuKernelImpl:C9}),Dre={kernelName:Po,backendName:"webgl",kernelFunc:gA};function bA(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=v.parseAxisParam([s],r.shape),o=mA({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=ce({inputs:{x:o},backend:n,attrs:{shape:l}}),p=gA({inputs:{a:r,b:u},backend:n}),d=cA({inputs:{x:p},backend:n}),c=zf({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=ce({inputs:{x:c},backend:n,attrs:{shape:l}}),m=fA({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var Rre={kernelName:Ro,backendName:"webgl",kernelFunc:bA};function Mre(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:bA({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new Ere(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var Pre={kernelName:Iu,backendName:"webgl",kernelFunc:Mre},Ore=Ma+` return -x; -`,yre=` +`,Lre=` vec4 result = -x; bvec4 isNaN = isnan(x); @@ -4028,14 +4032,14 @@ return a / b;`,cre=` result.a = isNaN.a ? x.a : result.a; return result; -`;function xre(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=VJ(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new es(a.shape,yre):r=new rr(a.shape,bre),n.runWebGLProgram(r,[a],a.dtype)}var vre={kernelName:Iu,backendName:"webgl",kernelFunc:xre},wre=hr.nonMaxSuppressionV3Impl;function kre(e){N.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,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=wre(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Ire={kernelName:Nu,backendName:"webgl",kernelFunc:kre},Sre=hr.nonMaxSuppressionV4Impl;function Nre(e){N.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:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=Sre(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Tre={kernelName:Tu,backendName:"webgl",kernelFunc:Nre},Cre=hr.nonMaxSuppressionV5Impl;function _re(e){N.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:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:b}=Cre(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var Ere={kernelName:Cu,backendName:"webgl",kernelFunc:_re},Are=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` +`;function zre(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=l9(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ts(a.shape,Lre):r=new rr(a.shape,Ore),n.runWebGLProgram(r,[a],a.dtype)}var Wre={kernelName:Su,backendName:"webgl",kernelFunc:zre},Bre=hr.nonMaxSuppressionV3Impl;function Vre(e){N.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,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=Bre(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Ure={kernelName:Tu,backendName:"webgl",kernelFunc:Vre},Gre=hr.nonMaxSuppressionV4Impl;function Hre(e){N.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:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=Gre(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var qre={kernelName:Cu,backendName:"webgl",kernelFunc:Hre},jre=hr.nonMaxSuppressionV5Impl;function Kre(e){N.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:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:b}=jre(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var Xre={kernelName:_u,backendName:"webgl",kernelFunc:Kre},Yre=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))); } - `}},Fre=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=v.sizeFromShape(r.shape),p=new Are(u,i,o,l),d=ce({inputs:{x:r},backend:n,attrs:{shape:[u]}}),c=n.runWebGLProgram(p,[d],s);n.disposeIntermediateTensorInfo(d);let h=[...r.shape,i],m=ce({inputs:{x:c},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(c),m},$re={kernelName:co,backendName:"webgl",kernelFunc:Fre};function cm(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Td({inputs:{input:a},backend:n}),s=cm({inputs:{x:r},backend:n}),i=zf({inputs:{input:a},backend:n}),o=cm({inputs:{x:i},backend:n}),l=_s({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Cd({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var Dre={kernelName:ju,backendName:"webgl",kernelFunc:cm};function oA(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=Td({inputs:{input:a},backend:n}),s=oA({inputs:{x:r},backend:n}),i=zf({inputs:{input:a},backend:n}),o=cm({inputs:{x:i},backend:n}),l=_s({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Cd({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var Rre={kernelName:_u,backendName:"webgl",kernelFunc:oA};function Mre(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return tv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=tv({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=qE({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var Pre={kernelName:Eu,backendName:"webgl",kernelFunc:Mre},Ore=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=ct(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` + `}},Zre=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=v.sizeFromShape(r.shape),p=new Yre(u,i,o,l),d=ce({inputs:{x:r},backend:n,attrs:{shape:[u]}}),c=n.runWebGLProgram(p,[d],s);n.disposeIntermediateTensorInfo(d);let h=[...r.shape,i],m=ce({inputs:{x:c},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(c),m},Jre={kernelName:mo,backendName:"webgl",kernelFunc:Zre};function cm(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Td({inputs:{input:a},backend:n}),s=cm({inputs:{x:r},backend:n}),i=Wf({inputs:{input:a},backend:n}),o=cm({inputs:{x:i},backend:n}),l=Es({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Cd({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var Qre={kernelName:Ku,backendName:"webgl",kernelFunc:cm};function yA(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=Td({inputs:{input:a},backend:n}),s=yA({inputs:{x:r},backend:n}),i=Wf({inputs:{input:a},backend:n}),o=cm({inputs:{x:i},backend:n}),l=Es({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Cd({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var ese={kernelName:Eu,backendName:"webgl",kernelFunc:yA};function tse(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return nv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=nv({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=aA({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var nse={kernelName:Au,backendName:"webgl",kernelFunc:tse},ase=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=ct(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -4060,7 +4064,7 @@ return a / b;`,cre=` setOutput(getX(${o})); } } - `}},Lre=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ct(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=In("rc",a),l=In("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${o[a-1]} += 1; + `}},rse=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ct(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=In("rc",a),l=In("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${o[a-1]} += 1; if(${u}) { `,a===1?"":`} rc = outputLoc; @@ -4084,7 +4088,7 @@ return a / b;`,cre=` ${h} setOutput(result); } - `}},lA=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return Cd({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Lre(r.shape,s,i):new Ore(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},zre={kernelName:ho,backendName:"webgl",kernelFunc:lA},Wre=` + `}},xA=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return Cd({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rse(r.shape,s,i):new ase(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},sse={kernelName:fo,backendName:"webgl",kernelFunc:xA},ise=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4093,7 +4097,7 @@ return a / b;`,cre=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,Bre=` +`,ose=` // 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); @@ -4109,11 +4113,11 @@ return a / b;`,cre=` bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); - `+qo+` + `+Ko+` return result; -`,Vre=mn({opSnippet:Wre,packedOpSnippet:Bre}),Ure={kernelName:mo,backendName:"webgl",kernelFunc:Vre};function Gre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,d=N.getAxesPermutation(p,o),c=r;d!=null&&(c=Sn({inputs:{x:r},backend:n,attrs:{perm:d}}),p=N.getInnerMostAxes(p.length,o),l.push(c)),N.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:b}=GJ(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,b,f)}else{let[m,f]=N.computeOutAndReduceShapes(c.shape,p),g=v.sizeFromShape(f),b=ce({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),y=Dm(r.dtype),x=jo(b,y,"prod",n);h=ce({inputs:{x},backend:n,attrs:{shape:m}}),l.push(b),l.push(x)}if(i){l.push(h);let m=N.expandShapeToKeepDim(h.shape,u);h=ce({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var Hre={kernelName:go,backendName:"webgl",kernelFunc:Gre};function qre(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(b=>n.readSync(b.dataId)),u=r.map(b=>b.shape),p=n.readSync(s.dataId),d=n.readSync(i.dataId),[c,h,m]=HJ(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(b=>n.makeTensorInfo([b.length],"int32",b)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var jre={kernelName:_m,backendName:"webgl",kernelFunc:qre};function Kre(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=qJ(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var Xre={kernelName:Em,backendName:"webgl",kernelFunc:Kre};function Yre(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=n.readSync(i.dataId),c=o.map(g=>n.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=jJ(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var Zre={kernelName:Am,backendName:"webgl",kernelFunc:Yre},uA=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=KJ(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},Jre={kernelName:Mc,backendName:"webgl",kernelFunc:uA},Qre="return 1.0 / x;",ese=Ye({opSnippet:Qre}),tse={kernelName:bo,backendName:"webgl",kernelFunc:ese},nse=Ma+` +`,lse=mn({opSnippet:ise,packedOpSnippet:ose}),use={kernelName:go,backendName:"webgl",kernelFunc:lse};function pse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,d=N.getAxesPermutation(p,o),c=r;d!=null&&(c=Sn({inputs:{x:r},backend:n,attrs:{perm:d}}),p=N.getInnerMostAxes(p.length,o),l.push(c)),N.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:b}=p9(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,b,f)}else{let[m,f]=N.computeOutAndReduceShapes(c.shape,p),g=v.sizeFromShape(f),b=ce({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),y=Rm(r.dtype),x=Xo(b,y,"prod",n);h=ce({inputs:{x},backend:n,attrs:{shape:m}}),l.push(b),l.push(x)}if(i){l.push(h);let m=N.expandShapeToKeepDim(h.shape,u);h=ce({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var cse={kernelName:yo,backendName:"webgl",kernelFunc:pse};function dse(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(b=>n.readSync(b.dataId)),u=r.map(b=>b.shape),p=n.readSync(s.dataId),d=n.readSync(i.dataId),[c,h,m]=c9(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(b=>n.makeTensorInfo([b.length],"int32",b)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var hse={kernelName:Em,backendName:"webgl",kernelFunc:dse};function mse(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=d9(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var fse={kernelName:Am,backendName:"webgl",kernelFunc:mse};function gse(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=n.readSync(i.dataId),c=o.map(g=>n.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=h9(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var bse={kernelName:Fm,backendName:"webgl",kernelFunc:gse},vA=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=m9(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},yse={kernelName:Mc,backendName:"webgl",kernelFunc:vA},xse="return 1.0 / x;",vse=Ye({opSnippet:xse}),wse={kernelName:xo,backendName:"webgl",kernelFunc:vse},kse=Ma+` return (x < 0.0) ? 0.0 : x; -`,ase=` +`,Ise=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4123,9 +4127,9 @@ return a / b;`,cre=` result.a = isNaN.a ? x.a : result.a; return result; -`,rse=Ye({opSnippet:nse,packedOpSnippet:ase}),sse={kernelName:yo,backendName:"webgl",kernelFunc:rse},ise=Ma+` +`,Sse=Ye({opSnippet:kse,packedOpSnippet:Ise}),Nse={kernelName:vo,backendName:"webgl",kernelFunc:Sse},Tse=Ma+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,ose=` +`,Cse=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4135,7 +4139,7 @@ return a / b;`,cre=` result.a = isNaN.a ? x.a : result.a; return result; -`,lse=Ye({opSnippet:ise,packedOpSnippet:ose}),use={kernelName:wo,backendName:"webgl",kernelFunc:lse},pse=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,_se=Ye({opSnippet:Tse,packedOpSnippet:Cse}),Ese={kernelName:Io,backendName:"webgl",kernelFunc:_se},Ase=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4168,7 +4172,7 @@ return a / b;`,cre=` setOutput(newValue); } - `}},cse=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},Fse=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/p[0]}, ${u[1]/p[1]}, @@ -4245,7 +4249,7 @@ return a / b;`,cre=` setOutput(newValue); } - `}};function dse(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new cse(r.shape,l,u,s,i):new pse(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var hse={kernelName:vo,backendName:"webgl",kernelFunc:dse},mse=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],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + `}};function $se(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Fse(r.shape,l,u,s,i):new Ase(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var Dse={kernelName:ko,backendName:"webgl",kernelFunc:$se},Rse=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],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4326,7 +4330,7 @@ return a / b;`,cre=` setOutput(accumulator); } - `}};function fse(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new mse(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var gse={kernelName:$u,backendName:"webgl",kernelFunc:fse},bse=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function Mse(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Rse(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Pse={kernelName:Du,backendName:"webgl",kernelFunc:Mse},Ose=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4348,7 +4352,7 @@ return a / b;`,cre=` setOutput(newValue); } - `}},yse=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},Lse=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/p[0]}, ${u[1]/p[1]}, @@ -4389,7 +4393,7 @@ return a / b;`,cre=` setOutput(newValue); } - `}};function xse(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new yse(r.shape,l,u,s,i):new bse(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var vse={kernelName:xo,backendName:"webgl",kernelFunc:xse},wse=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + `}};function zse(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Lse(r.shape,l,u,s,i):new Ose(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var Wse={kernelName:wo,backendName:"webgl",kernelFunc:zse},Bse=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4459,7 +4463,7 @@ return a / b;`,cre=` setOutput(accumulator); } - `}};function kse(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new wse(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Ise={kernelName:Fu,backendName:"webgl",kernelFunc:kse},Sse=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` + `}};function Vse(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Bse(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Use={kernelName:$u,backendName:"webgl",kernelFunc:Vse},Gse=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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)); @@ -4469,7 +4473,7 @@ return a / b;`,cre=` ${s} coords = getOutputCoords(); setOutput(getX(${r})); } - `}},Nse=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-${n} tensor is not yet supported`);this.outputShape=e;let a=In("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ct(n);n===1?this.userCode=` + `}},Hse=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let a=In("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ct(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -4497,7 +4501,7 @@ return a / b;`,cre=` } setOutput(result); } - `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((b,y)=>c(y,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function Tse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return na({inputs:{x:r},backend:n});let l=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Nse(r.shape,o):new Sse(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var Cse={kernelName:ko,backendName:"webgl",kernelFunc:Tse},_se=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` + `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((b,y)=>c(y,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function qse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return aa({inputs:{x:r},backend:n});let l=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Hse(r.shape,o):new Gse(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var jse={kernelName:So,backendName:"webgl",kernelFunc:qse},Kse=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` vec3 fill = vec3(${t.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -4516,7 +4520,7 @@ return a / b;`,cre=` } setOutput(outputValue); } - `}},Ese={kernelName:Ku,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new 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l=ct(r.length),u=ct(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides";this.userCode=` +`,Zse=Ye({opSnippet:Yse}),Jse={kernelName:No,backendName:"webgl",kernelFunc:Zse},Qse="return inversesqrt(x);",eie=Ye({opSnippet:Qse,cpuKernelImpl:f9}),tie={kernelName:To,backendName:"webgl",kernelFunc:eie},Y1=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let l=ct(r.length),u=ct(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides";this.userCode=` ${l} strides = ${l}(${r}); void main() { @@ -4551,7 +4555,7 @@ return a / b;`,cre=` } setOutput(mix(${f}, sum, float(found))); } - `}},Pse=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=ct(r.length),u=ct(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides",b=t>1?"strides[j + 1]":"strides";this.userCode=` + `}},nie=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=ct(r.length),u=ct(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides",b=t>1?"strides[j + 1]":"strides";this.userCode=` ${l} strides = ${l}(${r}); void main() { @@ -4588,7 +4592,7 @@ return a / b;`,cre=` } setOutput(mix(${f}, sum, found)); } - `}};function Ose(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=N.calculateShapes(s,r,i),c=[d/u,u];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=ce({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=ce({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g;G().getBool("WEBGL_PACK")?g=new Pse(l,o,h.shape.length,m.shape.length,p,c):g=new K1(l,o,h.shape.length,m.shape.length,p,c);let b=n.runWebGLProgram(g,[m,h,f],m.dtype),y=ce({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(f),y}var Lse={kernelName:Du,backendName:"webgl",kernelFunc:Ose},zse=class{constructor(e,t,n,a){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=G().getNumber("WEBGL_VERSION")===2?r:s,o=a==="left"?"<":"<=";this.userCode=` + `}};function aie(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=N.calculateShapes(s,r,i),c=[d/u,u];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=ce({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=ce({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g;G().getBool("WEBGL_PACK")?g=new nie(l,o,h.shape.length,m.shape.length,p,c):g=new Y1(l,o,h.shape.length,m.shape.length,p,c);let b=n.runWebGLProgram(g,[m,h,f],m.dtype),y=ce({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(f),y}var rie={kernelName:Ru,backendName:"webgl",kernelFunc:aie},sie=class{constructor(e,t,n,a){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=G().getNumber("WEBGL_VERSION")===2?r:s,o=a==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; @@ -4613,7 +4617,7 @@ return a / b;`,cre=` setOutput(float(findBound(batch, value))); } - `}};function Wse(e){let{inputs:t,backend:n,attrs:a}=e,{sortedSequence:r,values:s}=t,{side:i}=a,o=new zse(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return n.runWebGLProgram(o,[r,s],"int32",l)}var Bse={kernelName:Mu,backendName:"webgl",kernelFunc:Wse},Vse=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 u=0;u4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,qse=Ye({opSnippet:Hse}),jse={kernelName:No,backendName:"webgl",kernelFunc:qse},Kse=cp+` +`,die=Ye({opSnippet:cie}),hie={kernelName:Co,backendName:"webgl",kernelFunc:die},mie=cp+` return 1.0 / (1.0 + exp(-1.0 * x)); -`,Xse=` +`,fie=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -4641,20 +4645,20 @@ return a / b;`,cre=` result.a = isNaN.a ? x.a : result.a; return result; 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Die={kernelName:Wc,backendName:"webgl",kernelFunc:$ie};function Rie({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Ma+` + ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=UE(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(p,a.dtype,u)}var Wie={kernelName:Lc,backendName:"webgl",kernelFunc:zie};function Bie(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,sliceSize:p,strides:d,outputSize:c}=N.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let b=n.bufferSync(r),y=n.bufferSync(s),x=v.decodeString(n.readSync(i.dataId)[0]),w=g9(b,y,o,c,p,u,l,d,x,h);return n.makeTensorInfo(o,w.dtype,w.values)}let m=new Y1(u,l,r.shape.length,s.shape.length,d,[c,1],h),f=n.runWebGLProgram(m,[s,r,i],s.dtype),g=ce({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),g}var Vie={kernelName:Uu,backendName:"webgl",kernelFunc:Bie};function 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Qie={kernelName:Wc,backendName:"webgl",kernelFunc:Jie};function eoe({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Ma+` return x > 0.0 ? 1.0 : float(${t.alpha}); - `,s=new rr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var Mie={kernelName:vs,backendName:"webgl",kernelFunc:Rie},Pie=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=ct(n.length),s=ct(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` + `,s=new rr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var toe={kernelName:vs,backendName:"webgl",kernelFunc:eoe},noe=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=ct(n.length),s=ct(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * 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toe={kernelName:xs,backendName:"webgl",kernelFunc:pA},noe=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` + `}};function voe(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(d=>v.decodeString(d)):o,u=Le(r.shape,r.dtype,l),p=_9(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new xoe(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var woe={kernelName:xs,backendName:"webgl",kernelFunc:wA},koe=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4740,7 +4744,7 @@ return a / b;`,cre=` setOutput(float(i1)); } } - `}},aoe=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` + `}},Ioe=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); @@ -4774,7 +4778,7 @@ return a / b;`,cre=` setOutput(x0 >= x1 ? float(i0) : float(i1)); } - `}};function Gs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function rS(e){let t=1;for(;tl){let F=n.readSync(r.dataId),[D,$]=u9(F,u,r.dtype,s,i);return[n.makeTensorInfo(D.shape,D.dtype,D.values),n.makeTensorInfo($.shape,$.dtype,$.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(p===1)return[r,Cd({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),c=d!==null&&d.isPacked,h=c?n.unpackTensor(r):r,m=v.sizeFromShape(u)/p,f=ce({inputs:{x:h},attrs:{shape:[m,p]},backend:n});c&&Gs(n,h);let g=rS(s),b=rS(p),y=null,x=()=>y===null?[f,f]:[f,y],w=(F,D,$)=>{let S=x(),M=new noe($),B=[[p],[y===null?1:0],[Number.NEGATIVE_INFINITY],[F],[D]],U=y;y=n.runWebGLProgram(M,S,"int32",B),Gs(n,U)};for(let F=1;F=1;$/=2)w(D,$,[m,b])}for(let F=b;F>g;F/=2){let D=x(),$=new aoe([m,F/2]),S=[[p],[y===null?1:0],[g]],M=y;y=n.runWebGLProgram($,D,"int32",S),Gs(n,M);let B=g/2,U=B*2;for(let H=B;H>=1;H/=2)w(U,H,y.shape)}let I=y;y=dp({inputs:{x:y},backend:n,attrs:{begin:0,size:[m,s]}}),Gs(n,I);let T=nA({inputs:{x:f,indices:y},backend:n,attrs:{axis:1,batchDims:1}});Gs(n,f);let C=u.slice(0,-1);C.push(s),I=y,y=ce({inputs:{x:y},attrs:{shape:C},backend:n}),Gs(n,I);let E=T;return T=ce({inputs:{x:T},attrs:{shape:C},backend:n}),Gs(n,E),[T,y]}var soe={kernelName:Gu,backendName:"webgl",kernelFunc:roe},ioe=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` + `}};function Hs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function oS(e){let t=1;for(;tl){let F=n.readSync(r.dataId),[D,$]=E9(F,u,r.dtype,s,i);return[n.makeTensorInfo(D.shape,D.dtype,D.values),n.makeTensorInfo($.shape,$.dtype,$.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(p===1)return[r,Cd({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),c=d!==null&&d.isPacked,h=c?n.unpackTensor(r):r,m=v.sizeFromShape(u)/p,f=ce({inputs:{x:h},attrs:{shape:[m,p]},backend:n});c&&Hs(n,h);let g=oS(s),b=oS(p),y=null,x=()=>y===null?[f,f]:[f,y],w=(F,D,$)=>{let S=x(),M=new koe($),B=[[p],[y===null?1:0],[Number.NEGATIVE_INFINITY],[F],[D]],U=y;y=n.runWebGLProgram(M,S,"int32",B),Hs(n,U)};for(let F=1;F=1;$/=2)w(D,$,[m,b])}for(let F=b;F>g;F/=2){let D=x(),$=new Ioe([m,F/2]),S=[[p],[y===null?1:0],[g]],M=y;y=n.runWebGLProgram($,D,"int32",S),Hs(n,M);let B=g/2,U=B*2;for(let H=B;H>=1;H/=2)w(U,H,y.shape)}let I=y;y=dp({inputs:{x:y},backend:n,attrs:{begin:0,size:[m,s]}}),Hs(n,I);let T=hA({inputs:{x:f,indices:y},backend:n,attrs:{axis:1,batchDims:1}});Hs(n,f);let C=u.slice(0,-1);C.push(s),I=y,y=ce({inputs:{x:y},attrs:{shape:C},backend:n}),Hs(n,I);let E=T;return T=ce({inputs:{x:T},attrs:{shape:C},backend:n}),Hs(n,E),[T,y]}var Noe={kernelName:Hu,backendName:"webgl",kernelFunc:Soe},Toe=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; if(${o} == 2) { @@ -4886,7 +4890,7 @@ return a / b;`,cre=` } setOutput(outputValue); } - `}};function ooe(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[p,d,c,h]=r.shape,[m,f]=u!=null?u:[d,c],g=[p,m,f,h],b=new ioe(d,c,i,o,l,g);return n.runWebGLProgram(b,[r,s],"float32")}var loe={kernelName:Hu,backendName:"webgl",kernelFunc:ooe};function uoe(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;ip(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as 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Jle(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,b=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),y=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),w=t.dataIdMap.get(f.dataId).id;return DA(g,s,i==="NHWC"?1:0,b,r.shape.length-1,y,x,m.length,w),f}var Qle={kernelName:ru,backendName:"wasm",setupFunc:Zle,kernelFunc:Jle},RA;function eue(e){RA=e.wasm.cwrap(zi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function tue(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:u,pad:p,dimRoundingMode:d}=n,c=u==null?[1,1]:u,h=N.computeConv2DInfo(r.shape,s.shape,l,c,p,d,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,x=h.padInfo.left,w=h.dilationHeight,I=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,E=h.inChannels,F=h.outChannels,D=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 $=a.makeOutput(h.outShape,"float32"),S=a.dataIdMap.get($.dataId).id;return RA(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,b,y,x,D,w,I,T,C,E,F,S),$}var nue={kernelName:zi,backendName:"wasm",setupFunc:eue,kernelFunc:tue},MA;function aue(e){MA=e.wasm.cwrap("Diag",null,["number","number","number","number"])}function rue(e){let{inputs:t,backend:n}=e,{x:a}=t,r=v.sizeFromShape(a.shape),s=n.makeOutput([...a.shape,...a.shape],a.dtype);return MA(n.dataIdMap.get(a.dataId).id,et[a.dtype],r,n.dataIdMap.get(s.dataId).id),s}var sue={kernelName:$c,backendName:"wasm",setupFunc:aue,kernelFunc:rue},PA;function iue(e){PA=e.wasm.cwrap(Wi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function oue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;if(r.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${r.dtype} and ${s.dtype}`);let u=N.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=n.makeOutput(u.outShape,r.dtype);return PA(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(p.dataId).id,et[r.dtype],u.batchSize,u.inChannels,u.inHeight,u.inWidth,u.outHeight,u.outWidth,u.strideHeight,u.strideWidth,u.dilationHeight,u.dilationWidth,u.filterHeight,u.filterWidth,u.padInfo.top,u.padInfo.left),p}var lue={kernelName:Wi,backendName:"wasm",setupFunc:iue,kernelFunc:oue},OA;function uue(e){OA=e.wasm.cwrap(Nl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function pue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=a;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=N.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(s.shape,s.dtype);return OA(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,et[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var cue={kernelName:Nl,backendName:"wasm",setupFunc:uue,kernelFunc:pue},LA;function due(e){LA=e.wasm.cwrap(Sl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function hue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=a;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=N.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(r.shape,r.dtype);return LA(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,et[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var mue={kernelName:Sl,backendName:"wasm",setupFunc:due,kernelFunc:hue},fue=Ze(Vi),zA;function gue(e){zA=e.wasm.cwrap(su,null,["number","number","number"])}function bue(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=n.makeOutput(r.shape,"float32"),i=o=>n.dataIdMap.get(o.dataId).id;return zA(i(r),i(a),i(s)),s}var yue={kernelName:su,backendName:"wasm",setupFunc:gue,kernelFunc:bue},xue=!1,vue=on(ou,xue,"bool"),wue=Ze(Ui,"float32");function av(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Wn({inputs:{x:r},backend:a,attrs:{shape:o}})}var kue={kernelName:lu,backendName:"wasm",kernelFunc:av},Iue=Ze(Gi,"float32");function WA(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 Sue={kernelName:Dc,backendName:"wasm",kernelFunc:WA},BA;function Nue(e){BA=e.wasm.cwrap(uu,null,["number","number","number","number","number","number"])}function Tue(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,u,p]=a.shape;return BA(s,o,l,u,p,i),r}var Cue={kernelName:uu,backendName:"wasm",kernelFunc:Tue,setupFunc:Nue},_ue=Ze(Hi),Eue=!1,Aue=on(qi,Eue),VA;function Fue(e){VA=e.wasm.cwrap(ji,null,["number","number","number","number","number","number","number"])}function $ue(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return VA(p,d,c,h,m,r,g),f}var Due={kernelName:ji,backendName:"wasm",setupFunc:Fue,kernelFunc:$ue},UA;function Rue(e){UA=e.wasm.cwrap(ri,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 Mue(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=Nc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return UA(b,j,K,Z,y,I,T,w,C,E,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Pue={kernelName:ri,backendName:"wasm",setupFunc:Rue,kernelFunc:Mue},GA;function Oue(e){GA=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 Lue(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=Nc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return GA(b,j,K,Z,y,I,T,w,C,E,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var zue={kernelName:si,backendName:"wasm",setupFunc:Oue,kernelFunc:Lue},HA;function Wue(e){HA=e.wasm.cwrap(cu,null,["number","number","number","number","number","number","array","number"])}function Bue(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Mw.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let p=r.shape,d=p[p.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return HA(c,et[a.dtype],h,i,d,o,m,f),u}var Vue={kernelName:cu,backendName:"wasm",setupFunc:Wue,kernelFunc:Bue},qA;function Uue(e){qA=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Gue(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=v.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let C=0;C=0,()=>`GatherV2: the index value ${E} is not in [0, ${p-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Wn({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),m=Wn({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let b=c.shape.length-1,y=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,w=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(v.computeStrides(c.shape)).buffer),T=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer);return qA(y,et[r.dtype],I,b,x,d.batchSize,T,w),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var Hue={kernelName:pu,backendName:"wasm",setupFunc:Uue,kernelFunc:Gue},que=!1,jue=on(du,que,"bool"),Kue=!1,Xue=on(Ki,Kue,"bool"),Yue=Ze(Yi,"bool"),Zue=Ze(Zi,"bool"),Jue=Ze(Ji,"bool"),jA;function Que(e){jA=e.wasm.cwrap(Qi,null,["number","number","number","number"])}function epe(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;jA(r,et[t.dtype],n,i)}return s}var tpe={kernelName:Qi,backendName:"wasm",setupFunc:Que,kernelFunc:epe},npe=!1,ape=on(hu,npe,"bool"),rpe=!1,spe=on(mu,rpe,"bool"),KA;function ipe(e){KA=e.wasm.cwrap(fu,null,["number","number","number","number"])}function ope(e){let{attrs:t,backend:n}=e,{start:a,stop:r,num:s}=t,i=Math.floor(s),o=n.makeOutput([i],"float32");return KA(n.dataIdMap.get(o.dataId).id,a,r,i),o}var lpe={kernelName:fu,backendName:"wasm",setupFunc:ipe,kernelFunc:ope},upe=Ze(eo),ppe=Ze(to),cpe=!1,dpe=on(gu,cpe,"bool"),hpe=Ze(bu),mpe=!1,fpe=on(yu,mpe,"bool"),gpe=!1,bpe=on(SS,gpe,"bool"),XA;function ype(e){XA=e.wasm.cwrap(no,null,["number","number","number","number","number","number","number"])}function xpe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=n.makeOutput(r.shape,r.dtype);return XA(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var vpe={kernelName:no,backendName:"wasm",setupFunc:ype,kernelFunc:xpe},YA;function wpe(e){YA=e.wasm.cwrap(xu,null,["number","number","number","number","number","number","number","number","number"])}function kpe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let d=n.makeOutput(r.shape,r.dtype);return YA(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,i.shape[3],o,l,u,p),d}var Ipe={kernelName:xu,backendName:"wasm",setupFunc:wpe,kernelFunc:kpe},ZA;function Spe(e){ZA=e.wasm.cwrap(ao,null,["number","number","number","number"])}function Npe(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:u,axes:p,originalAxes:d,inputWasTransposed:c}=Es(i,r,t);if(c){let y=t.dataIdMap.get(u.dataId).id;l=u,o=y}let h=l.shape.length;N.assertAxesAreInnerMostDims("max",p,h);let[m,f]=N.computeOutAndReduceShapes(l.shape,p),g=v.sizeFromShape(f),b=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let y=t.dataIdMap.get(b.dataId).id;ZA(o,et[i.dtype],g,y)}if(c&&t.disposeData(u.dataId),s){let y=N.expandShapeToKeepDim(b.shape,d);b.shape=y}return b}var Tpe={kernelName:ao,backendName:"wasm",setupFunc:Spe,kernelFunc:Npe},Cpe=!1,_pe=on(ro,Cpe),JA;function Epe(e){JA=e.wasm.cwrap(so,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ape(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=N.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,b=p.dilationHeight,y=p.dilationWidth,x=p.strideHeight,w=p.strideWidth,I=p.inChannels,T=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(p.outShape,"float32"),E=a.dataIdMap.get(C.dataId).id;return JA(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,b,y,x,w,I,T,E),C}var Fpe={kernelName:so,backendName:"wasm",setupFunc:Epe,kernelFunc:Ape},QA;function $pe(e){QA=e.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Dpe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=N.computePool3DInfo(r.shape,s,i,1,o,l,u),d=n.makeOutput(p.outShape,r.dtype);return QA(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),d}var Rpe={kernelName:vu,backendName:"wasm",setupFunc:$pe,kernelFunc:Dpe},eF;function Mpe(e){eF=e.wasm.cwrap("MaxPool3DGrad",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 Ppe(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a,p=N.computePool3DInfo(s.shape,i,o,1,l,u),d=n.makeOutput(s.shape,s.dtype);return eF(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),d}var Ope={kernelName:Rc,backendName:"wasm",setupFunc:Mpe,kernelFunc:Ppe},tF;function Lpe(e){tF=e.wasm.cwrap(io,null,["number, number, number"])}function zpe(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,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Es(i,r,t),m=d;if(h){let w=t.dataIdMap.get(p.dataId).id;w!==o&&(u=p,l=w,m=N.getInnerMostAxes(m.length,u.shape.length))}N.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=N.computeOutAndReduceShapes(u.shape,m),b=v.sizeFromShape(g),y=u;u.dtype!=="float32"&&(y=As({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(y.dataId).id);let x=t.makeOutput(f,"float32");if(v.sizeFromShape(u.shape)!==0){let w=t.dataIdMap.get(x.dataId).id;tF(l,b,w)}if(h&&t.disposeData(p.dataId),s){let w=N.expandShapeToKeepDim(x.shape,c);x.shape=w}return u.dtype!=="float32"&&t.disposeData(y.dataId),x}var Wpe={kernelName:io,backendName:"wasm",setupFunc:Lpe,kernelFunc:zpe},nF;function Bpe(e){nF=e.wasm.cwrap(oo,null,["number","number","number","number"])}function Vpe(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,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Es(i,r,t);if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x)}let m=u.shape.length;N.assertAxesAreInnerMostDims("min",d,m);let[f,g]=N.computeOutAndReduceShapes(u.shape,d),b=v.sizeFromShape(g),y=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;nF(l,et[i.dtype],b,x)}if(h&&t.disposeData(p.dataId),s){let x=N.expandShapeToKeepDim(y.shape,c);y.shape=x}return y}var Upe={kernelName:oo,backendName:"wasm",setupFunc:Bpe,kernelFunc:Vpe},Gpe=!1,Hpe=on(lo,Gpe),rv;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(rv||(rv={}));var aF;function qpe(e){aF=e.wasm.cwrap(uo,null,["number","array","number","number","array","array","number","number"])}function jpe(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode: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,u=new Uint8Array(new Int32Array(t.shape).buffer),p=a.map(m=>m[0]),d=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new 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iF(n.dataIdMap.get(l.dataId).id,u,p,s,i,n.dataIdMap.get(d.dataId).id),o||n.disposeData(l.dataId),d}var Qpe={kernelName:ku,backendName:"wasm",setupFunc:Zpe,kernelFunc:Jpe},ece=!0,tce=on(po,ece),nce=Ze(Iu);function X1(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 oF;function ace(e){oF=e.wasm.cwrap(Nu,"number",["number","number","number","number","number"])}function rce(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=oF(u,p,s,r,i),{pSelectedIndices:c,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=X1(t,d);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",c)}var sce={kernelName:Nu,backendName:"wasm",setupFunc:ace,kernelFunc:rce},lF;function ice(e){lF=e.wasm.cwrap(Tu,"number",["number","number","number","number","number","bool"])}function oce(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=lF(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=X1(t,c);t.wasm._free(f);let b=t.makeOutput([m],"int32",h),y=t.makeOutput([],"int32",g);return[b,y]}var lce={kernelName:Tu,backendName:"wasm",setupFunc:ice,kernelFunc:oce},uF;function uce(e){uF=e.wasm.cwrap(Cu,"number",["number","number","number","number","number","number"])}function pce(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=uF(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=X1(t,c);t.wasm._free(g);let b=t.makeOutput([m],"int32",h),y=t.makeOutput([m],"float32",f);return[b,y]}var cce={kernelName:Cu,backendName:"wasm",setupFunc:uce,kernelFunc:pce},dce=!1,hce=on(Su,dce,"bool"),pF;function mce(e){pF=e.wasm.cwrap(co,null,["number","number","number","number","number"])}function fce(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=n.makeOutput([...r.shape,i],s),p=n.dataIdMap.get(u.dataId).id,d=n.dataIdMap.get(r.dataId).id;return pF(d,i,o,l,p),u}var gce={kernelName:co,backendName:"wasm",setupFunc:mce,kernelFunc:fce};function bce(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var yce={kernelName:_u,backendName:"wasm",kernelFunc:bce};function xce(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return av({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching 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dF={kernelName:ho,backendName:"wasm",kernelFunc:kce,setupFunc:wce},Ice=!1,Sce=on(mo,Ice),hF;function Nce(e){hF=e.wasm.cwrap(fo,null,["number","number","number"])}function Tce(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=s,l=a,u=l;l.dtype!=="float32"&&(u=As({backend:n,inputs:{x:a},attrs:{dtype:"float32"}}),o=n.dataIdMap.get(u.dataId).id);let p=n.makeOutput(a.shape,"float32"),d=n.dataIdMap.get(p.dataId).id;return hF(o,i,d),l.dtype!=="float32"&&n.disposeData(u.dataId),p}var Cce={kernelName:fo,backendName:"wasm",setupFunc:Nce,kernelFunc:Tce},mF;function _ce(e){mF=e.wasm.cwrap(go,null,["number","number","number","number"])}function Ece(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,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Es(i,r,t),m=d;if(h){let 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zce(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,[p,d,c,h]=r.shape,m=[p,l,u,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=As({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let b=f.id,y=t.makeOutput(m,"float32");if(v.sizeFromShape(r.shape)===0)return y;let x=t.dataIdMap.get(y.dataId).id;return fF(b,p,d,c,h,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),y}var Wce={kernelName:vo,backendName:"wasm",setupFunc:Lce,kernelFunc:zce},gF;function Bce(e){gF=e.wasm.cwrap($u,null,["number","number","number","array","array","boolean"])}function Vce(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=n.makeOutput(r.shape,"float32"),l=n.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=As({backend:n,inputs:{x:r},attrs:{dtype:"float32"}}),l=n.dataIdMap.get(u.dataId)),gF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(o.dataId).id,new 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et;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(et||(et={}));var Nc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(Nc||(Nc={}));var kA;function Ooe(e){kA=e.wasm.cwrap(si,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Loe(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank 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Woe=Je(ql),Boe=Je(Ii),Voe=Je(Si);function ln(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:p}=l,d=o.dataIdMap.get(u.dataId).id,c=o.dataIdMap.get(p.dataId).id,h=n!=null?n:u.dtype,m=N.assertAndGetBroadcastShape(u.shape,p.shape),f=o.makeOutput(m,h);if(v.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),b=new Uint8Array(new Int32Array(p.shape).buffer),y=o.dataIdMap.get(f.dataId).id;return a(d,g,u.shape.length,c,b,p.shape.length,et[u.dtype],y),f}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var Uoe=!0,Goe=ln(bs,Uoe),IA;function Hoe(e){IA=e.wasm.cwrap(Ni,null,["array","number","number","number"])}function qoe(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new 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Ale={kernelName:Jl,backendName:"wasm",kernelFunc:Ele},$A;function Fle(e){$A=e.wasm.cwrap(Ql,null,["number","number","boolean","number","number","number"])}function $le(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,weights:s}=n,{size:i}=a,o=s.shape.reduce((d,c)=>d*c,1)!==0,l=r.shape.length===1?[i]:[r.shape[0],i],u=t.makeOutput(l,s.dtype);function p(d){return t.dataIdMap.get(d.dataId).id}return $A(p(r),i,o,p(s),et[s.dtype],p(u)),u}var Dle={kernelName:Ql,backendName:"wasm",setupFunc:Fle,kernelFunc:$le};function Rle(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.typedArrayFromHeap(a),i=n.typedArrayFromHeap(r),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeOutput([o.length],"int32",void 0,new Int32Array(o))}var Mle={kernelName:Ec,backendName:"wasm",kernelFunc:Rle};function Fs(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var Ple={kernelName:Di,backendName:"wasm",kernelFunc:Fs},Ole=Je(Ri),DA;function Lle(e){DA=e.wasm.cwrap(ys,null,["number","number","number","number"])}function zle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return DA(o,s,i,u),l}var Wle={kernelName:ys,backendName:"wasm",setupFunc:Lle,kernelFunc:zle};function RA(e){let{inputs:t,backend:n}=e,a=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=t.map(h=>h.shape);N.assertParamsConsistent(r,a);let s=N.computeOutShape(t.map(h=>h.shape),a),i=t.filter(h=>v.sizeFromShape(h.shape)>0);if(i.length===1)return Bf({inputs:{x:i[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(v.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(x=>{let w=[-1,v.sizeFromShape(x.shape.slice(a))];return 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b=N.getUndoAxesPermutation(u);g=fs({inputs:{x:c},attrs:{perm:b},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var pue={kernelName:au,backendName:"wasm",setupFunc:lue,kernelFunc:uue},VA;function cue(e){VA=e.wasm.cwrap(Wi,null,["number","number","number","number","number","number"])}function due(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),p=r;u!==null&&(p=fs({inputs:{x:r},attrs:{perm:u},backend:n}));let d=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumsum",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;VA(m,i?1:0,o?1:0,h,f,et[r.dtype]);let g=c;if(u!==null){let b=N.getUndoAxesPermutation(u);g=fs({inputs:{x:c},attrs:{perm:b},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var hue={kernelName:Wi,backendName:"wasm",setupFunc:cue,kernelFunc:due},UA;function mue(e){UA=e.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function fue(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,weights:s}=n,{size:i,binaryOutput:o}=a,l=s.shape.reduce((c,h)=>c*h,1)!==0,u=r.shape.length===1?[i]:[r.shape[0],i],p=t.makeOutput(u,s.dtype);function d(c){return t.dataIdMap.get(c.dataId).id}return UA(d(r),new Uint8Array(new Int32Array(r.shape).buffer),r.shape.length,i,l,d(s),et[s.dtype],o,d(p)),p}var gue={kernelName:Fc,backendName:"wasm",setupFunc:mue,kernelFunc:fue},GA;function bue(e){GA=e.wasm.cwrap(su,null,["number","number","number","array","number","array","array","number","number"])}function yue(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,b=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),y=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),w=t.dataIdMap.get(f.dataId).id;return GA(g,s,i==="NHWC"?1:0,b,r.shape.length-1,y,x,m.length,w),f}var xue={kernelName:su,backendName:"wasm",setupFunc:bue,kernelFunc:yue},HA;function vue(e){HA=e.wasm.cwrap(Bi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function wue(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:u,pad:p,dimRoundingMode:d}=n,c=u==null?[1,1]:u,h=N.computeConv2DInfo(r.shape,s.shape,l,c,p,d,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,x=h.padInfo.left,w=h.dilationHeight,I=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,E=h.inChannels,F=h.outChannels,D=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 $=a.makeOutput(h.outShape,"float32"),S=a.dataIdMap.get($.dataId).id;return HA(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,b,y,x,D,w,I,T,C,E,F,S),$}var kue={kernelName:Bi,backendName:"wasm",setupFunc:vue,kernelFunc:wue},qA;function Iue(e){qA=e.wasm.cwrap("Diag",null,["number","number","number","number"])}function Sue(e){let{inputs:t,backend:n}=e,{x:a}=t,r=v.sizeFromShape(a.shape),s=n.makeOutput([...a.shape,...a.shape],a.dtype);return qA(n.dataIdMap.get(a.dataId).id,et[a.dtype],r,n.dataIdMap.get(s.dataId).id),s}var Nue={kernelName:$c,backendName:"wasm",setupFunc:Iue,kernelFunc:Sue},jA;function Tue(e){jA=e.wasm.cwrap(Vi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Cue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;if(r.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. 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Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=N.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(s.shape,s.dtype);return KA(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,et[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var Fue={kernelName:Tl,backendName:"wasm",setupFunc:Eue,kernelFunc:Aue},XA;function $ue(e){XA=e.wasm.cwrap(Nl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Due(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=a;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=N.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(r.shape,r.dtype);return XA(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,et[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var Rue={kernelName:Nl,backendName:"wasm",setupFunc:$ue,kernelFunc:Due},Mue=Je(Gi),YA;function Pue(e){YA=e.wasm.cwrap(iu,null,["number","number","number"])}function Oue(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=n.makeOutput(r.shape,"float32"),i=o=>n.dataIdMap.get(o.dataId).id;return YA(i(r),i(a),i(s)),s}var Lue={kernelName:iu,backendName:"wasm",setupFunc:Pue,kernelFunc:Oue},zue=!1,Wue=ln(lu,zue,"bool"),Bue=Je(Hi,"float32");function rv(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&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Wn({inputs:{x:r},backend:a,attrs:{shape:o}})}var Vue={kernelName:uu,backendName:"wasm",kernelFunc:rv},Uue=Je(qi,"float32");function ZA(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Gue={kernelName:Dc,backendName:"wasm",kernelFunc:ZA},JA;function Hue(e){JA=e.wasm.cwrap(pu,null,["number","number","number","number","number","number"])}function que(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return JA(s,o,l,u,p,i),r}var jue={kernelName:pu,backendName:"wasm",kernelFunc:que,setupFunc:Hue},Kue=Je(ji),Xue=!1,Yue=ln(Ki,Xue),QA;function Zue(e){QA=e.wasm.cwrap(Xi,null,["number","number","number","number","number","number","number"])}function Jue(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return QA(p,d,c,h,m,r,g),f}var Que={kernelName:Xi,backendName:"wasm",setupFunc:Zue,kernelFunc:Jue},eF;function epe(e){eF=e.wasm.cwrap(ii,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function tpe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=Nc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return eF(b,j,K,Z,y,I,T,w,C,E,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var npe={kernelName:ii,backendName:"wasm",setupFunc:epe,kernelFunc:tpe},tF;function ape(e){tF=e.wasm.cwrap(oi,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 rpe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=Nc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return tF(b,j,K,Z,y,I,T,w,C,E,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var spe={kernelName:oi,backendName:"wasm",setupFunc:ape,kernelFunc:rpe},nF;function ipe(e){nF=e.wasm.cwrap(du,null,["number","number","number","number","number","number","array","number"])}function ope(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Pw.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let p=r.shape,d=p[p.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return nF(c,et[a.dtype],h,i,d,o,m,f),u}var lpe={kernelName:du,backendName:"wasm",setupFunc:ipe,kernelFunc:ope},aF;function upe(e){aF=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function ppe(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=v.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let C=0;C=0,()=>`GatherV2: the index value ${E} is not in [0, ${p-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Wn({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),m=Wn({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let b=c.shape.length-1,y=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,w=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(v.computeStrides(c.shape)).buffer),T=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer);return aF(y,et[r.dtype],I,b,x,d.batchSize,T,w),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var cpe={kernelName:cu,backendName:"wasm",setupFunc:upe,kernelFunc:ppe},dpe=!1,hpe=ln(hu,dpe,"bool"),mpe=!1,fpe=ln(Yi,mpe,"bool"),gpe=Je(Ji,"bool"),bpe=Je(Qi,"bool"),ype=Je(eo,"bool"),rF;function xpe(e){rF=e.wasm.cwrap(to,null,["number","number","number","number"])}function vpe(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;rF(r,et[t.dtype],n,i)}return s}var wpe={kernelName:to,backendName:"wasm",setupFunc:xpe,kernelFunc:vpe},kpe=!1,Ipe=ln(mu,kpe,"bool"),Spe=!1,Npe=ln(fu,Spe,"bool"),sF;function Tpe(e){sF=e.wasm.cwrap(gu,null,["number","number","number","number"])}function Cpe(e){let{attrs:t,backend:n}=e,{start:a,stop:r,num:s}=t,i=Math.floor(s),o=n.makeOutput([i],"float32");return sF(n.dataIdMap.get(o.dataId).id,a,r,i),o}var _pe={kernelName:gu,backendName:"wasm",setupFunc:Tpe,kernelFunc:Cpe},Epe=Je(no),Ape=Je(ao),Fpe=!1,$pe=ln(bu,Fpe,"bool"),Dpe=Je(yu),Rpe=!1,Mpe=ln(xu,Rpe,"bool"),Ppe=!1,Ope=ln(_S,Ppe,"bool"),iF;function Lpe(e){iF=e.wasm.cwrap(ro,null,["number","number","number","number","number","number","number"])}function zpe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=n.makeOutput(r.shape,r.dtype);return iF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var Wpe={kernelName:ro,backendName:"wasm",setupFunc:Lpe,kernelFunc:zpe},oF;function Bpe(e){oF=e.wasm.cwrap(vu,null,["number","number","number","number","number","number","number","number","number"])}function Vpe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let d=n.makeOutput(r.shape,r.dtype);return oF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,i.shape[3],o,l,u,p),d}var Upe={kernelName:vu,backendName:"wasm",setupFunc:Bpe,kernelFunc:Vpe},lF;function Gpe(e){lF=e.wasm.cwrap(so,null,["number","number","number","number"])}function Hpe(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:u,axes:p,originalAxes:d,inputWasTransposed:c}=As(i,r,t);if(c){let y=t.dataIdMap.get(u.dataId).id;l=u,o=y}let h=l.shape.length;N.assertAxesAreInnerMostDims("max",p,h);let[m,f]=N.computeOutAndReduceShapes(l.shape,p),g=v.sizeFromShape(f),b=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let y=t.dataIdMap.get(b.dataId).id;lF(o,et[i.dtype],g,y)}if(c&&t.disposeData(u.dataId),s){let y=N.expandShapeToKeepDim(b.shape,d);b.shape=y}return b}var qpe={kernelName:so,backendName:"wasm",setupFunc:Gpe,kernelFunc:Hpe},jpe=!1,Kpe=ln(io,jpe),uF;function Xpe(e){uF=e.wasm.cwrap(oo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ype(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=N.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,b=p.dilationHeight,y=p.dilationWidth,x=p.strideHeight,w=p.strideWidth,I=p.inChannels,T=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(p.outShape,"float32"),E=a.dataIdMap.get(C.dataId).id;return uF(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,b,y,x,w,I,T,E),C}var Zpe={kernelName:oo,backendName:"wasm",setupFunc:Xpe,kernelFunc:Ype},pF;function Jpe(e){pF=e.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qpe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=N.computePool3DInfo(r.shape,s,i,1,o,l,u),d=n.makeOutput(p.outShape,r.dtype);return pF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),d}var ece={kernelName:wu,backendName:"wasm",setupFunc:Jpe,kernelFunc:Qpe},cF;function tce(e){cF=e.wasm.cwrap("MaxPool3DGrad",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 nce(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a,p=N.computePool3DInfo(s.shape,i,o,1,l,u),d=n.makeOutput(s.shape,s.dtype);return cF(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),d}var ace={kernelName:Rc,backendName:"wasm",setupFunc:tce,kernelFunc:nce},dF;function rce(e){dF=e.wasm.cwrap(lo,null,["number, number, number"])}function sce(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,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=As(i,r,t),m=d;if(h){let w=t.dataIdMap.get(p.dataId).id;w!==o&&(u=p,l=w,m=N.getInnerMostAxes(m.length,u.shape.length))}N.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=N.computeOutAndReduceShapes(u.shape,m),b=v.sizeFromShape(g),y=u;u.dtype!=="float32"&&(y=Fs({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(y.dataId).id);let x=t.makeOutput(f,"float32");if(v.sizeFromShape(u.shape)!==0){let w=t.dataIdMap.get(x.dataId).id;dF(l,b,w)}if(h&&t.disposeData(p.dataId),s){let w=N.expandShapeToKeepDim(x.shape,c);x.shape=w}return u.dtype!=="float32"&&t.disposeData(y.dataId),x}var ice={kernelName:lo,backendName:"wasm",setupFunc:rce,kernelFunc:sce},hF;function oce(e){hF=e.wasm.cwrap(uo,null,["number","number","number","number"])}function lce(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,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=As(i,r,t);if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x)}let m=u.shape.length;N.assertAxesAreInnerMostDims("min",d,m);let[f,g]=N.computeOutAndReduceShapes(u.shape,d),b=v.sizeFromShape(g),y=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;hF(l,et[i.dtype],b,x)}if(h&&t.disposeData(p.dataId),s){let x=N.expandShapeToKeepDim(y.shape,c);y.shape=x}return y}var uce={kernelName:uo,backendName:"wasm",setupFunc:oce,kernelFunc:lce},pce=!1,cce=ln(po,pce),sv;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(sv||(sv={}));var mF;function dce(e){mF=e.wasm.cwrap(co,null,["number","array","number","number","array","array","number","number"])}function hce(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode: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,u=new Uint8Array(new Int32Array(t.shape).buffer),p=a.map(m=>m[0]),d=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new 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IF={kernelName:fo,backendName:"wasm",kernelFunc:Vce,setupFunc:Bce},Uce=!1,Gce=ln(go,Uce),SF;function Hce(e){SF=e.wasm.cwrap(bo,null,["number","number","number"])}function qce(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=s,l=a,u=l;l.dtype!=="float32"&&(u=Fs({backend:n,inputs:{x:a},attrs:{dtype:"float32"}}),o=n.dataIdMap.get(u.dataId).id);let p=n.makeOutput(a.shape,"float32"),d=n.dataIdMap.get(p.dataId).id;return SF(o,i,d),l.dtype!=="float32"&&n.disposeData(u.dataId),p}var jce={kernelName:bo,backendName:"wasm",setupFunc:Hce,kernelFunc:qce},NF;function Kce(e){NF=e.wasm.cwrap(yo,null,["number","number","number","number"])}function Xce(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,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=As(i,r,t),m=d;if(h){let 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Mde(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 v.sizeFromShape(r.shape)===0||MF(a,s),r}var Pde={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Rde,kernelFunc:Mde},Ode=Je(Eo),Lde=Je(_o),zde=Je(Fo);function Wde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a,o=v.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g0?l+1:0;if(u<0)throw new Error(N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let p=r.shape.slice();p[0]=u;let d=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(s.dataId).id,h=n.dataIdMap.get(i.dataId).id,m=n.makeOutput(p,r.dtype),f=n.dataIdMap.get(m.dataId).id,g=n.makeOutput([4],"int32"),b=n.dataIdMap.get(g.dataId).id;CF(d,et[r.dtype],r.shape[0],c,h,f,b,t,0);let y=n.readSync(g.dataId),x;switch(y[0]){case 0:{x=N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{x=N.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:x=N.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(y[1],y[2]);break;case 3:x=N.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(y[1],y[2],y[3]);break;default:x=""}if(n.disposeData(g.dataId),x)throw n.disposeData(m.dataId),new Error(x);return m}function _de(e){return EF(e,!0)}var Ede={kernelName:Oc,backendName:"wasm",setupFunc:_F,kernelFunc:_de};function Ade(e){return EF(e,!1)}var Fde={kernelName:Lc,backendName:"wasm",setupFunc:_F,kernelFunc:Ade},AF;function $de(e){AF=e.wasm.cwrap(Vu,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Dde(e){let{backend:t,inputs:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=n,{outputShape:o}=a,l=t.makeOutput(o,i.dtype);if(v.sizeFromShape(o)===0)return 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Pe(this.left,this.top)}get topRight(){return new Pe(this.right,this.top)}get bottomLeft(){return new Pe(this.left,this.bottom)}get bottomRight(){return new Pe(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 dt({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 dt({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 an&&(o=-d+n+a,d=n),c>t&&(l=-c+t+r,c=t),u<1&&(l=2-u,u=1),p<1&&(l=2-p,p=1),{dy:i,edy:l,dx:s,edx:o,y:p,ey:c,x:u,ex:d,w:a,h:r}}calibrate(t){return new dt({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 mp=class extends dt{constructor(t,n,a,r,s=!1){super({left:t,top:n,right:a,bottom:r},s)}};var Fs=class{constructor(t,n,a,r,s){this._imageDims=new En(s.width,s.height),this._score=t,this._classScore=n,this._className=a,this._box=new dt(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 dt(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new Fs(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var wt=class extends Fs{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 wt(a,r,s)}};function BF(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 VF(e){let t=e.map(o=>o.x),n=e.map(o=>o.y),a=t.reduce((o,l)=>lloo({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 u=0;ul[p]<=n)}return s}function br(e,t){return P(()=>{let[n,a,r]=t,s=xn([...e.shape.slice(0,3),1],n,"float32"),i=xn([...e.shape.slice(0,3),1],a,"float32"),o=xn([...e.shape.slice(0,3),1],r,"float32"),l=Je([s,i,o],3);return pe(e,l)})}function GF(e,t=!1){return P(()=>{let[n,a]=e.shape.slice(1);if(n===a)return e;let r=Math.abs(n-a),s=Math.round(r*(t?.5:1)),i=n>a?2:1,o=c=>{let h=e.shape.slice();return h[i]=c,xn(h,0,"float32")},l=o(s),u=r-l.shape[i],d=[t&&u?o(u):null,e,l].filter(c=>!!c).map(c=>ie(c,"float32"));return Je(d,i)})}function qve(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let a=Math.floor(Math.random()*(n+1)),r=t[n];t[n]=t[a],t[a]=r}return t}function Uf(e){return 1/(1+Math.exp(-e))}function Kve(e){return Math.log(e/(1-e))}var fp=class extends dt{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var Uhe=.5,Ghe=.43,Hhe=.45,wa=class{constructor(t,n,a=new Pe(0,0)){let{width:r,height:s}=n;this._imgDims=new En(r,s),this._shift=a,this._positions=t.map(i=>i.mul(new Pe(r,s)).add(a))}get shift(){return new Pe(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new Pe(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new Pe(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let s=t instanceof wt?t.box.floor():new dt(t);return this.shiftBy(s.x,s.y).align(null,n)}let{useDlibAlignment:a,minBoxPadding:r}={useDlibAlignment:!1,minBoxPadding:.2,...n};return a?this.alignDlib():this.alignMinBbox(r)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,a,r]=t,s=d=>r.sub(d).magnitude(),i=(s(n)+s(a))/2,o=Math.floor(i/Hhe),l=Yo(t),u=Math.floor(Math.max(0,l.x-Uhe*o)),p=Math.floor(Math.max(0,l.y-Ghe*o));return new fp(u,p,Math.min(o,this.imageWidth+u),Math.min(o,this.imageHeight+p))}alignMinBbox(t){let n=VF(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var HF=class extends wa{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],Yo([t[3],t[4]])]}};var gp=class extends wa{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(Yo)}};var _d=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}${t?` (${Xo(this.distance)})`:""}`}};var Ed=class extends dt{constructor(n,a){super(n);this._label=a}static assertIsValidLabeledBox(n,a){if(dt.assertIsValidBox(n,a),!Za(n.label))throw new Error(`${a} - expected property label (${n.label}) to be a number`)}get label(){return this._label}};var Lr=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 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 qF=class extends Ed{constructor(n,a,r,s){super(n,a);this._score=r,this._classScore=s}static assertIsValidPredictedBox(n,a){if(Ed.assertIsValidLabeledBox(n,a),!hp(n.score)||!hp(n.classScore))throw new Error(`${a} - expected properties score (${n.score}) and (${n.classScore}) to be a number between [0, 1]`)}get score(){return this._score}get classScore(){return this._classScore}};function zr(e){return e.detection instanceof wt}function bp(e,t){return{...e,...{detection:t}}}function ek(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser 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xp({...i,...s})}},Fd=class{constructor(t,n={}){this.box=new dt(t),this.options=new Hf(n)}draw(t){let n=ra(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:u}=this.options;u&&new Wr([u],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function Khe(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof wt?a.score:zr(a)?a.detection.score:void 0,s=a instanceof wt?a.box:zr(a)?a.detection.box:new dt(a),i=r?`${Xo(r)}`:void 0;new Fd(s,{label:i}).draw(e)})}function qf(e){let{Image:t,Video:n}=at.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function jF(e){return new Promise((t,n)=>{(e instanceof at.getEnv().Canvas||qf(e))&&t(null);function a(s){s.currentTarget&&(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function r(s){s.currentTarget&&(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),t(s))}e.addEventListener("load",r),e.addEventListener("error",a)})}function KF(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let a=new FileReader;a.onload=()=>{typeof a.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let r=at.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function vp(e){let{Image:t,Video:n}=at.getEnv();return e instanceof t?new En(e.naturalWidth,e.naturalHeight):e instanceof n?new En(e.videoWidth,e.videoHeight):new En(e.width,e.height)}function wp({width:e,height:t}){let{createCanvasElement:n}=at.getEnv(),a=n();return a.width=e,a.height=t,a}function jf(e,t){let{ImageData:n}=at.getEnv();if(!(e instanceof n)&&!qf(e))throw new Error("createCanvasFromMedia - media has not finished 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Br=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Or(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(va(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof at.getEnv().Canvas?a:jf(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return gr(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),a=this.getInputHeight(t);return Q1({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,P(()=>{let a=gr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof Te){let o=va(i)?i:Qt(i);return o=GF(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Ea.resizeBilinear(o,[t,t],!1,!1)),o.as3D(t,t,3)}if(i instanceof at.getEnv().Canvas)return Wo.fromPixels(YF(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return Dt(a.map(s=>ie(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function kt(e){if(e instanceof Br)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");let n=r=>Array.isArray(e)?` at input index ${r}:`:"",a=t.map(yp);return a.forEach((r,s)=>{if(!ik(r)&&!Or(r)&&!va(r))throw typeof t[s]=="string"?new Error(`toNetInput -${n(s)} string passed, but could not resolve HTMLElement for element id ${t[s]}`):new Error(`toNetInput -${n(s)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(va(r)){let i=r.shape[0];if(i!==1)throw new Error(`toNetInput -${n(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(a.map(r=>ik(r)&&jF(r))),new Br(a,Array.isArray(e))}async function 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fn{constructor(n,a){super(n);this._faceFeatureExtractor=a}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return P(()=>{let r=n instanceof Br?this.faceFeatureExtractor.forwardInput(n):n;return Pd(r.as2D(r.shape[0],-1),a.fc)})}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:a,paramMappings:r}=this.extractClassifierParams(n);this._params=a,this._paramMappings=r}extractClassifierParams(n){return n$(n,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(n){let{featureExtractorMap:a,classifierMap:r}=eg(n);return this.faceFeatureExtractor.loadFromWeightMap(a),a$(r)}extractParams(n){let a=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),s=r*a+r,i=n.slice(0,n.length-s),o=n.slice(n.length-s);return this.faceFeatureExtractor.extractWeights(i),this.extractClassifierParams(o)}};var 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cme(e){let t=pt(De(e,[1,0])),n=[pe(t[2],t[0]),pe(t[3],t[1])],a=[X(t[0],he(n[0],2)),X(t[1],he(n[1],2))];return{sizes:n,centers:a}}function dme(e,t){let{sizes:n,centers:a}=cme(e),r=pt(De(t,[1,0])),s=he(z(yn(he(r[2],5)),n[0]),2),i=X(z(he(r[0],10),n[0]),a[0]),o=he(z(yn(he(r[3],5)),n[1]),2),l=X(z(he(r[1],10),n[1]),a[1]);return De(Dt([pe(i,s),pe(l,o),X(i,s),X(l,o)]),[1,0])}function S$(e,t,n){return P(()=>{let a=e.shape[0],r=dme(W(Ln(n.extra_dim,[a,1,1]),[-1,4]),W(e,[-1,4]));r=W(r,[a,r.shape[0]/a,4]);let s=ha(Ve(t,[0,0,1],[-1,-1,-1])),i=Ve(s,[0,0,0],[-1,-1,1]);i=W(i,[a,i.shape[1]]);let o=pt(r),l=pt(i);return{boxes:o,scores:l}})}function Jo(e,t){return P(()=>{let n=e.shape[0],a=W(Zo(e,t.box_encoding_predictor),[n,-1,1,4]),r=W(Zo(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function N$(e,t,n){return P(()=>{let 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l.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return w$(t)}extractParams(t){return v$(t)}};function hme(e){let t=new Qo;return t.extractWeights(e),t}function I2e(e){return hme(e)}var T$=class extends Qo{};var C$=.4,_$=[new Pe(.738768,.874946),new Pe(2.42204,2.65704),new Pe(4.30971,7.04493),new Pe(10.246,4.59428),new Pe(12.6868,11.8741)],E$=[new Pe(1.603231,2.094468),new Pe(6.041143,7.080126),new Pe(2.882459,3.518061),new Pe(4.266906,5.178857),new Pe(9.041765,10.66308)],A$=[117.001,114.697,97.404],F$="tiny_yolov2_model",$$="tiny_yolov2_separable_conv_model";var ug=e=>typeof e=="number";function D$(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(!ug(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: 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n=Ns(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),Fp(n)})}function mme(e,t){let n=kp(e,t);function a(i,o){let l=je(e(i)),u=je(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=Ip(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function R$(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=Fn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=mme(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,b,y,x]=a,w=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),T=u(h,m,"conv2"),C=u(m,f,"conv3"),E=u(f,g,"conv4"),F=u(g,b,"conv5"),D=y?u(b,y,"conv6"):void 0,$=x?u(y,x,"conv7"):void 0,S=o(x||y||b,5*n,1,"conv8");p={conv0:w,conv1:I,conv2:T,conv3:C,conv4:E,conv5:F,conv6:D,conv7:$,conv8:S}}else{let[d,c,h,m,f,g,b,y,x]=a,w=l(d,c,"conv0"),I=l(c,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),E=l(f,g,"conv4"),F=l(g,b,"conv5"),D=l(b,y,"conv6"),$=l(y,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:w,conv1:I,conv2:T,conv3:C,conv4:E,conv5:F,conv6:D,conv7:$,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function fme(e,t){let n=sa(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Sp(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function M$(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=fme(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 An(e,n),{params:i,paramMappings:n}}var yr=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 fk=class extends fn{constructor(n){super("TinyYolov2");D$(n),this._config=n}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(n,a){let r=Vr(n,a.conv0);return r=Mt(r,[2,2],[2,2],"same"),r=Vr(r,a.conv1),r=Mt(r,[2,2],[2,2],"same"),r=Vr(r,a.conv2),r=Mt(r,[2,2],[2,2],"same"),r=Vr(r,a.conv3),r=Mt(r,[2,2],[2,2],"same"),r=Vr(r,a.conv4),r=Mt(r,[2,2],[2,2],"same"),r=Vr(r,a.conv5),r=Mt(r,[2,2],[1,1],"same"),r=Vr(r,a.conv6),r=Vr(r,a.conv7),Zo(r,a.conv8,"valid",!1)}runMobilenet(n,a){let r=this.config.isFirstLayerConv2d?Fp(Zo(n,a.conv0,"valid",!1)):Ur(n,a.conv0);return r=Mt(r,[2,2],[2,2],"same"),r=Ur(r,a.conv1),r=Mt(r,[2,2],[2,2],"same"),r=Ur(r,a.conv2),r=Mt(r,[2,2],[2,2],"same"),r=Ur(r,a.conv3),r=Mt(r,[2,2],[2,2],"same"),r=Ur(r,a.conv4),r=Mt(r,[2,2],[2,2],"same"),r=Ur(r,a.conv5),r=Mt(r,[2,2],[1,1],"same"),r=a.conv6?Ur(r,a.conv6):r,r=a.conv7?Ur(r,a.conv7):r,Zo(r,a.conv8,"valid",!1)}forwardInput(n,a){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return P(()=>{let s=ie(n.toBatchTensor(a,!1),"float32");return s=this.config.meanRgb?br(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(n,a){return this.forwardInput(await kt(n),a)}async detect(n,a={}){let{inputSize:r,scoreThreshold:s}=new yr(a),i=await kt(n),o=await this.forwardInput(i,r),l=P(()=>pt(o)[0].expandDims()),u={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(l,i.getReshapedInputDimensions(0),s);o.dispose(),l.dispose();let d=p.map(b=>b.box),c=p.map(b=>b.score),h=p.map(b=>b.classScore),m=p.map(b=>this.config.classes[b.label]);return UF(d.map(b=>b.rescale(r)),c,this.config.iouThreshold,!0).map(b=>new Fs(c[b],h[b],m[b],d[b],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(n){return M$(n,this.config)}extractParams(n){let a=this.config.filterSizes||fk.DEFAULT_FILTER_SIZES,r=a?a.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 R$(n,this.config,this.boxEncodingSize,a)}async extractBoxes(n,a,r){let{width:s,height:i}=a,o=Math.max(s,i),l=o/s,u=o/i,p=n.shape[1],d=this.config.anchors.length,[c,h,m]=P(()=>{let y=n.reshape([p,p,d,this.boxEncodingSize]),x=y.slice([0,0,0,0],[p,p,d,4]),w=y.slice([0,0,0,4],[p,p,d,1]),I=this.withClassScores?Xa(y.slice([0,0,0,5],[p,p,d,this.config.classes.length]),3):ve(0);return[x,w,I]}),f=[],g=await h.array(),b=await c.array();for(let y=0;yr){let T=(x+Uf(b[y][x][w][0]))/p*l,C=(y+Uf(b[y][x][w][1]))/p*u,E=Math.exp(b[y][x][w][2])*this.config.anchors[w].x/p*l,F=Math.exp(b[y][x][w][3])*this.config.anchors[w].y/p*u,D=T-E/2,$=C-F/2,S={row:y,col:x,anchor:w},{classScore:M,label:B}=this.withClassScores?await this.extractPredictedClass(m,S):{classScore:1,label:0};f.push({box:new mp(D,$,D+E,$+F),score:I,classScore:I*M,label:B,...S})}}return c.dispose(),h.dispose(),m.dispose(),f}async extractPredictedClass(n,a){let{row:r,col:s,anchor:i}=a,o=await n.array();return Array(this.config.classes.length).fill(0).map((l,u)=>o[r][s][i][u]).map((l,u)=>({classScore:l,label:u})).reduce((l,u)=>l.classScore>u.classScore?l:u)}},el=fk;el.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var $p=class extends el{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:C$,classes:["face"],...t?{anchors:E$,meanRgb:A$}:{anchors:_$,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 wt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?$$:F$}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function mCe(e,t=!0){let n=new $p(t);return n.extractWeights(e),n}var pg=class extends yr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var La=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function tl(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Cp(l)?r(l):l.detection),i=a||(t instanceof Te?await Dd(t,s):await $d(t,s)),o=await n(i);return i.forEach(l=>l instanceof Te&&l.dispose()),o}async function Dp(e,t,n,a,r){return tl([e],t,async s=>n(s[0]),a,r)}var P$=.4,O$=[new Pe(1.603231,2.094468),new Pe(6.041143,7.080126),new Pe(2.882459,3.518061),new Pe(4.266906,5.178857),new Pe(9.041765,10.66308)],L$=[117.001,114.697,97.404];var Rp=class extends el{constructor(){let t={withSeparableConvs:!0,iouThreshold:P$,classes:["face"],anchors:O$,meanRgb:L$,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 wt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var rt={ssdMobilenetv1:new Qo,tinyFaceDetector:new Rp,tinyYolov2:new $p,faceLandmark68Net:new Ep,faceLandmark68TinyNet:new og,faceRecognitionNet:new Ap,faceExpressionNet:new tg,ageGenderNet:new sg},gme=(e,t)=>rt.ssdMobilenetv1.locateFaces(e,t),GCe=(e,t)=>rt.tinyFaceDetector.locateFaces(e,t),HCe=(e,t)=>rt.tinyYolov2.locateFaces(e,t),bme=e=>rt.faceLandmark68Net.detectLandmarks(e),qCe=e=>rt.faceLandmark68TinyNet.detectLandmarks(e),jCe=e=>rt.faceRecognitionNet.computeFaceDescriptor(e),KCe=e=>rt.faceExpressionNet.predictExpressions(e),XCe=e=>rt.ageGenderNet.predictAgeAndGender(e),yme=e=>rt.ssdMobilenetv1.load(e),YCe=e=>rt.tinyFaceDetector.load(e),ZCe=e=>rt.tinyYolov2.load(e),JCe=e=>rt.faceLandmark68Net.load(e),QCe=e=>rt.faceLandmark68TinyNet.load(e),e_e=e=>rt.faceRecognitionNet.load(e),t_e=e=>rt.faceExpressionNet.load(e),n_e=e=>rt.ageGenderNet.load(e),a_e=yme,r_e=gme,s_e=bme;var cg=class extends La{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},nl=class extends cg{async run(){let t=await this.parentTask,n=await tl(t,this.input,async a=>Promise.all(a.map(r=>rt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>ok(a,n[r]))}withAgeAndGender(){return new rl(this,this.input)}},al=class extends cg{async run(){let t=await this.parentTask;if(!t)return;let n=await Dp(t,this.input,a=>rt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return ok(t,n)}withAgeAndGender(){return new sl(this,this.input)}},Rs=class extends nl{withAgeAndGender(){return new Ps(this,this.input)}withFaceDescriptors(){return new Ls(this,this.input)}},Ms=class extends al{withAgeAndGender(){return new Os(this,this.input)}withFaceDescriptor(){return new zs(this,this.input)}};var dg=class extends La{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},rl=class extends dg{async run(){let t=await this.parentTask,n=await tl(t,this.input,async a=>Promise.all(a.map(r=>rt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return hk(mk(a,i,o),s)})}withFaceExpressions(){return new nl(this,this.input)}},sl=class extends dg{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Dp(t,this.input,s=>rt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return hk(mk(t,a,r),n)}withFaceExpressions(){return new al(this,this.input)}},Ps=class extends rl{withFaceExpressions(){return new Rs(this,this.input)}withFaceDescriptors(){return new Ls(this,this.input)}},Os=class extends sl{withFaceExpressions(){return new Ms(this,this.input)}withFaceDescriptor(){return new zs(this,this.input)}};var hg=class extends La{constructor(n,a){super();this.parentTask=n;this.input=a}},Ls=class extends hg{async run(){let t=await this.parentTask;return(await tl(t,this.input,a=>Promise.all(a.map(r=>rt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>dk(t[r],a))}withFaceExpressions(){return new Rs(this,this.input)}withAgeAndGender(){return new Ps(this,this.input)}},zs=class extends hg{async run(){let t=await this.parentTask;if(!t)return;let n=await Dp(t,this.input,a=>rt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return dk(t,n)}withFaceExpressions(){return new Ms(this,this.input)}withAgeAndGender(){return new Os(this,this.input)}};var mg=class extends La{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?rt.faceLandmark68TinyNet:rt.faceLandmark68Net}},fg=class extends mg{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Te?await Dd(this.input,n):await $d(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Te&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>Od(i,r[o]))}withFaceExpressions(){return new Rs(this,this.input)}withAgeAndGender(){return new Ps(this,this.input)}withFaceDescriptors(){return new Ls(this,this.input)}},gg=class extends mg{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Te?await Dd(this.input,[n]):await $d(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Te&&s.dispose()),Od(t,r)}withFaceExpressions(){return new Ms(this,this.input)}withAgeAndGender(){return new Os(this,this.input)}withFaceDescriptor(){return new zs(this,this.input)}};var bg=class extends La{constructor(n,a=new Oa){super();this.input=n;this.options=a}},zd=class extends bg{async run(){let{input:t,options:n}=this,a;if(n instanceof pg)a=rt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Oa)a=rt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof yr)a=rt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>bp({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new fg(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new nl(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new rl(this.runAndExtendWithFaceDetections(),this.input)}},yg=class extends bg{async run(){let t=await new zd(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?bp({},n):void 0)})}withFaceLandmarks(t=!1){return new gg(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new al(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new sl(this.runAndExtendWithFaceDetection(),this.input)}};function nEe(e,t=new Oa){return new yg(e,t)}function gk(e,t=new Oa){return new zd(e,t)}async function xme(e,t){return gk(e,new Oa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function lEe(e,t={}){return gk(e,new yr(t)).withFaceLandmarks().withFaceDescriptors()}var uEe=xme;function z$(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*s,0))}var xg=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 ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof Lr)return i;if(i instanceof Float32Array)return new Lr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Lr(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>z$(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new _d(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Lr.fromJSON(a));return new xg(n,t.distanceThreshold)}};function CEe(e){let t=new Rp;return t.extractWeights(e),t}function vme(e,t){let{width:n,height:a}=new En(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>vme(r,{width:n,height:a}));if(Cp(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Od(bp(e,r),s)}return zr(e)?bp(e,e.detection.forSize(n,a)):e instanceof wa||e instanceof wt?e.forSize(n,a):e}var LEe=o$;export{sg as AgeGenderNet,mp as BoundingBox,dt as Box,La as ComposableTask,Ls as ComputeAllFaceDescriptorsTask,hg as ComputeFaceDescriptorsTaskBase,zs as ComputeSingleFaceDescriptorTask,fg as DetectAllFaceLandmarksTask,zd as DetectAllFacesTask,mg as DetectFaceLandmarksTaskBase,bg as DetectFacesTaskBase,gg as DetectSingleFaceLandmarksTask,yg as DetectSingleFaceTask,En as Dimensions,r$ as FACE_EXPRESSION_LABELS,wt as FaceDetection,T$ as FaceDetectionNet,tg as FaceExpressionNet,Ds as FaceExpressions,Ep as FaceLandmark68Net,og as FaceLandmark68TinyNet,f$ as FaceLandmarkNet,wa as FaceLandmarks,HF as FaceLandmarks5,gp as FaceLandmarks68,_d as FaceMatch,xg as FaceMatcher,Ap as FaceRecognitionNet,uk as Gender,Ed as LabeledBox,Lr as LabeledFaceDescriptors,Br as NetInput,fn as NeuralNetwork,Fs as ObjectDetection,Pe as Point,qF as PredictedBox,fp as Rect,Qo as SsdMobilenetv1,Oa as SsdMobilenetv1Options,Rp as TinyFaceDetector,pg as TinyFaceDetectorOptions,$p as TinyYolov2,yr as TinyYolov2Options,uEe as allFaces,xme as allFacesSsdMobilenetv1,lEe as allFacesTinyYolov2,jF as awaitMediaLoaded,KF as bufferToImage,jCe as computeFaceDescriptor,wp as createCanvas,jf as createCanvasFromMedia,I2e as createFaceDetectionNet,ITe as createFaceRecognitionNet,hme as createSsdMobilenetv1,CEe as createTinyFaceDetector,mCe as createTinyYolov2,gk as detectAllFaces,bme as detectFaceLandmarks,qCe as detectFaceLandmarksTiny,s_e as detectLandmarks,nEe as detectSingleFace,i$ as draw,at as env,z$ as euclideanDistance,hk as extendWithAge,dk as extendWithFaceDescriptor,bp as extendWithFaceDetection,ok as extendWithFaceExpressions,Od as extendWithFaceLandmarks,mk as extendWithGender,Dd as extractFaceTensors,$d as extractFaces,D1e as fetchImage,ZF as fetchJson,L1e as fetchNetWeights,$s as fetchOrThrow,G1e as fetchVideo,ra as getContext2dOrThrow,vp as getMediaDimensions,XF as imageTensorToCanvas,YF as imageToSquare,Kve as inverseSigmoid,BF as iou,ik as isMediaElement,qf as isMediaLoaded,CTe as isWithAge,zr as isWithFaceDetection,s$ as isWithFaceExpressions,Cp as isWithFaceLandmarks,FTe as isWithGender,n_e as loadAgeGenderModel,a_e as loadFaceDetectionModel,t_e as loadFaceExpressionModel,JCe as loadFaceLandmarkModel,QCe as loadFaceLandmarkTinyModel,e_e as loadFaceRecognitionModel,yme as loadSsdMobilenetv1Model,YCe as loadTinyFaceDetectorModel,ZCe as loadTinyYolov2Model,QF as loadWeightMap,r_e as locateFaces,Z1e as matchDimensions,VF as minBbox,rt as nets,UF as nonMaxSuppression,br as normalize,GF as padToSquare,XCe as predictAgeAndGender,KCe as recognizeFaceExpressions,vme as resizeResults,yp as resolveInput,qve as shuffleArray,Uf as sigmoid,gme as ssdMobilenetv1,ze as tf,GCe as tinyFaceDetector,HCe as tinyYolov2,kt as toNetInput,WF as utils,D$ as validateConfig,LEe as version}; + ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let 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Vr=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Lr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(wa(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof at.getEnv().Canvas?a:Kf(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return 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Qo(e,t,n="same",a=!1){return P(()=>{let r=X(Rt(e,t.filters,[1,1],n),t.bias);return a?Ke(r):r})}function An(e,t){Object.keys(e).forEach(n=>{t.some(a=>a.originalPath===n)||e[n].dispose()})}function kp(e,t){return(n,a,r,s)=>{let i=Da(e(n*a*r*r),[r,r,n,a]),o=je(e(a));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}}function Zf(e,t){return(n,a,r)=>{let s=Aa(e(n*a),[n,a]),i=je(e(a));return t.push({paramPath:`${r}/weights`},{paramPath:`${r}/bias`}),{weights:s,bias:i}}}var Md=class{constructor(t,n,a){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=a}};function Ip(e,t){return(n,a,r)=>{let s=Da(e(9*n),[3,3,n,1]),i=Da(e(n*a),[1,1,n,a]),o=je(e(a));return t.push({paramPath:`${r}/depthwise_filter`},{paramPath:`${r}/pointwise_filter`},{paramPath:`${r}/bias`}),new Md(s,i,o)}}function Sp(e){return t=>{let n=e(`${t}/depthwise_filter`,4),a=e(`${t}/pointwise_filter`,4),r=e(`${t}/bias`,1);return new Md(n,a,r)}}function ia(e,t){return(n,a,r)=>{let s=e[n];if(!Yo(s,a))throw new Error(`expected weightMap[${n}] to be a Tensor${a}D, instead have ${s}`);return t.push({originalPath:n,paramPath:r||n}),s}}function Fn(e){let t=e;function n(r){let s=t.slice(0,r);return t=t.slice(r),s}function a(){return t}return{extractWeights:n,getRemainingWeights:a}}function Jf(e,t){let n=kp(e,t),a=Ip(e,t);function r(i,o,l,u=!1){let p=u?n(i,o,3,`${l}/conv0`):a(i,o,`${l}/conv0`),d=a(o,o,`${l}/conv1`),c=a(o,o,`${l}/conv2`);return{conv0:p,conv1:d,conv2:c}}function s(i,o,l,u=!1){let{conv0:p,conv1:d,conv2:c}=r(i,o,l,u),h=a(o,o,`${l}/conv3`);return{conv0:p,conv1:d,conv2:c,conv3:h}}return{extractDenseBlock3Params:r,extractDenseBlock4Params:s}}function c$(e){let t=[],{extractWeights:n,getRemainingWeights:a}=Fn(e),{extractDenseBlock4Params:r}=Jf(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2"),l=r(128,256,"dense3");if(a().length!==0)throw new Error(`weights remaing after extract: 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a=ie(t.toBatchTensor(112,!0),"float32"),s=br(a,[122.782,117.001,104.298]).div(255),i=Rd(s,n.dense0,!0);return i=Rd(i,n.dense1),i=Rd(i,n.dense2),i=Rd(i,n.dense3),i=ya(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await kt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return d$(t)}extractParams(t){return c$(t)}};function Pd(e,t){return P(()=>X($e(e,t.weights),t.bias))}function h$(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=Fn(e),o=Zf(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function m$(e){let t=[],n=ia(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return An(e,t),{params:r,paramMappings:t}}function tg(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var Tp=class extends fn{constructor(n,a){super(n);this._faceFeatureExtractor=a}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return P(()=>{let r=n instanceof Vr?this.faceFeatureExtractor.forwardInput(n):n;return Pd(r.as2D(r.shape[0],-1),a.fc)})}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:a,paramMappings:r}=this.extractClassifierParams(n);this._params=a,this._paramMappings=r}extractClassifierParams(n){return h$(n,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(n){let{featureExtractorMap:a,classifierMap:r}=tg(n);return this.faceFeatureExtractor.loadFromWeightMap(a),m$(r)}extractParams(n){let a=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),s=r*a+r,i=n.slice(0,n.length-s),o=n.slice(n.length-s);return this.faceFeatureExtractor.extractWeights(i),this.extractClassifierParams(o)}};var f$=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Rs=class{constructor(t){this.neutral=0;this.happy=0;this.sad=0;this.angry=0;this.fearful=0;this.disgusted=0;this.surprised=0;if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);f$.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return f$.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var ng=class extends Tp{constructor(t=new Np){super("FaceExpressionNet",t)}forwardInput(t){return P(()=>Xa(this.runNet(t)))}async forward(t){return this.forwardInput(await kt(t))}async predictExpressions(t){let n=await kt(t),a=await this.forwardInput(n),r=await Promise.all(pt(a).map(async i=>{let o=i.dataSync();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Rs(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 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n=sa(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof gp&&(n.strokeStyle=i,n.lineWidth=s,Or(n,this.faceLandmarks.getJawOutline()),Or(n,this.faceLandmarks.getLeftEyeBrow()),Or(n,this.faceLandmarks.getRightEyeBrow()),Or(n,this.faceLandmarks.getNose()),Or(n,this.faceLandmarks.getLeftEye(),!0),Or(n,this.faceLandmarks.getRightEye(),!0),Or(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let u=p=>{n.beginPath(),n.arc(p.x,p.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(u)}}};function bme(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof ka?a:Cp(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new rg(r).draw(e)})}var y$="1.7.11";function vme(e,t){let n=kp(e,t),a=Ip(e,t);function r(i,o,l){let 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m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:c,middle_flow:h,exit_flow:g}}}function wme(e,t){let n=ia(e,t),a=Qf(n),r=Sp(n);function s(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:u,expansion_conv:p}}function i(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function v$(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=wme(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),u=s("entry_flow/reduction_block_1"),p={conv_in:o,reduction_block_0:l,reduction_block_1:u},d={};gr(t,0,1).forEach(f=>{d[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let c=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:c,separable_conv:h};return An(e,n),{params:{entry_flow:p,middle_flow:d,exit_flow:m},paramMappings:n}}function w$(e,t,n){return X(Rt(e,t.filters,n,"same"),t.bias)}function pk(e,t,n=!0){let a=n?Ke(e):e;return a=Hn(a,t.separable_conv0,[1,1]),a=Hn(Ke(a),t.separable_conv1,[1,1]),a=Mt(a,[3,3],[2,2],"same"),a=X(a,w$(e,t.expansion_conv,[2,2])),a}function kme(e,t){let n=Hn(Ke(e),t.separable_conv0,[1,1]);return n=Hn(Ke(n),t.separable_conv1,[1,1]),n=Hn(Ke(n),t.separable_conv2,[1,1]),n=X(n,e),n}var sg=class extends fn{constructor(n){super("TinyXception");this._numMainBlocks=n}forwardInput(n){let{params:a}=this;if(!a)throw new Error("TinyXception - load model before inference");return P(()=>{let r=ie(n.toBatchTensor(112,!0),"float32"),i=br(r,[122.782,117.001,104.298]).div(255),o=Ke(w$(i,a.entry_flow.conv_in,[2,2]));return o=pk(o,a.entry_flow.reduction_block_0,!1),o=pk(o,a.entry_flow.reduction_block_1),gr(this._numMainBlocks,0,1).forEach(l=>{o=kme(o,a.middle_flow[`main_block_${l}`])}),o=pk(o,a.exit_flow.reduction_block),o=Ke(Hn(o,a.exit_flow.separable_conv,[1,1])),o})}async forward(n){return this.forwardInput(await kt(n))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(n){return v$(n,this._numMainBlocks)}extractParams(n){return x$(n,this._numMainBlocks)}};function k$(e){let t=[],{extractWeights:n,getRemainingWeights:a}=Fn(e),r=Zf(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: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function I$(e){let t=[],n=ia(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 An(e,t),{params:r,paramMappings:t}}var ck=(n=>(n.FEMALE="female",n.MALE="male",n))(ck||{});var ig=class extends fn{constructor(n=new sg(2)){super("AgeGenderNet");this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return P(()=>{let r=n instanceof Vr?this.faceFeatureExtractor.forwardInput(n):n,s=ya(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),i=Pd(s,a.fc.age).as1D(),o=Pd(s,a.fc.gender);return{age:i,gender:o}})}forwardInput(n){return P(()=>{let{age:a,gender:r}=this.runNet(n);return{age:a,gender:Xa(r)}})}async forward(n){return this.forwardInput(await kt(n))}async predictAgeAndGender(n){let a=await kt(n),r=await this.forwardInput(a),s=pt(r.age),i=pt(r.gender),o=s.map((u,p)=>({ageTensor:u,genderTensor:i[p]})),l=await Promise.all(o.map(async({ageTensor:u,genderTensor:p})=>{let d=u.dataSync()[0],c=p.dataSync()[0],h=c>.5,m=h?"male":"female",f=h?c:1-c;return u.dispose(),p.dispose(),{age:d,gender:m,genderProbability:f}}));return r.age.dispose(),r.gender.dispose(),a.isBatchInput?l:l[0]}getDefaultModelName(){return"age_gender_model"}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:a,paramMappings:r}=this.extractClassifierParams(n);this._params=a,this._paramMappings=r}extractClassifierParams(n){return k$(n)}extractParamsFromWeightMap(n){let{featureExtractorMap:a,classifierMap:r}=tg(n);return this.faceFeatureExtractor.loadFromWeightMap(a),I$(r)}extractParams(n){let r=n.slice(0,n.length-1539),s=n.slice(n.length-1539);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(s)}};var _p=class extends Tp{postProcess(t,n,a){let r=a.map(({width:i,height:o})=>{let l=n/Math.max(o,i);return{width:i*l,height:o*l}}),s=r.length;return P(()=>{let i=(d,c)=>Dt([xn([68],d,"float32"),xn([68],c,"float32")],1).as2D(1,136).as1D(),o=(d,c)=>{let{width:h,height:m}=r[d];return c(h,m)?Math.abs(h-m)/2:0},l=d=>o(d,(c,h)=>co(d,(c,h)=>hi(l(c),u(c))))).div(Dt(Array.from(Array(s),(d,c)=>i(r[c].width,r[c].height))))})}forwardInput(t){return P(()=>{let n=this.runNet(t);return this.postProcess(n,t.inputSize,t.inputDimensions.map(([a,r])=>({height:a,width:r})))})}async forward(t){return this.forwardInput(await kt(t))}async detectLandmarks(t){let n=await kt(t),a=P(()=>pt(this.forwardInput(n))),r=await Promise.all(a.map(async(s,i)=>{let o=Array.from(s.dataSync()),l=o.filter((p,d)=>Vf(d)),u=o.filter((p,d)=>!Vf(d));return new gp(Array(68).fill(0).map((p,d)=>new Pe(l[d],u[d])),{height:n.getInputHeight(i),width:n.getInputWidth(i)})}));return a.forEach(s=>s.dispose()),n.isBatchInput?r:r[0]}getClassifierChannelsOut(){return 136}};var Ep=class extends _p{constructor(t=new Np){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function S$(e){let t=[],{extractDenseBlock3Params:n}=eg(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return An(e,t),{params:a,paramMappings:t}}function N$(e){let t=[],{extractWeights:n,getRemainingWeights:a}=Fn(e),{extractDenseBlock3Params:r}=Jf(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var og=class extends fn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return P(()=>{let a=ie(t.toBatchTensor(112,!0),"float32"),s=br(a,[122.782,117.001,104.298]).div(255),i=Yf(s,n.dense0,!0);return i=Yf(i,n.dense1),i=Yf(i,n.dense2),i=ya(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await kt(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return S$(t)}extractParams(t){return N$(t)}};var lg=class extends _p{constructor(t=new og){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var T$=class extends Ep{};function C$(e,t){return X(z(e,t.weights),t.biases)}function dk(e,t,n,a,r="same"){let{filters:s,bias:i}=t.conv,o=Rt(e,s,n,r);return o=X(o,i),o=C$(o,t.scale),a?Ke(o):o}function _$(e,t){return dk(e,t,[1,1],!0)}function hk(e,t){return dk(e,t,[1,1],!1)}function ug(e,t){return dk(e,t,[2,2],!0,"valid")}function Ime(e,t){function n(o,l,u){let p=e(o),d=p.length/(l*u*u);if(ek(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, numFilters: ${l}, filterSize: ${u}`);return P(()=>De(Da(p,[l,d,u,u]),[2,3,1,0]))}function a(o,l,u,p){let d=n(o,l,u),c=je(e(l));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:d,bias:c}}function r(o,l){let u=je(e(o)),p=je(e(o));return t.push({paramPath:`${l}/weights`},{paramPath:`${l}/biases`}),{weights:u,biases:p}}function s(o,l,u,p){let d=a(o,l,u,`${p}/conv`),c=r(l,`${p}/scale`);return{conv:d,scale:c}}function i(o,l,u,p,d=!1){let c=s((d?.5:1)*o,l,u,`${p}/conv1`),h=s(o,l,u,`${p}/conv2`);return{conv1:c,conv2:h}}return{extractConvLayerParams:s,extractResidualLayerParams:i}}function 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0,S=o(x||y||b,5*n,1,"conv8");p={conv0:w,conv1:I,conv2:T,conv3:C,conv4:E,conv5:F,conv6:D,conv7:$,conv8:S}}else{let[d,c,h,m,f,g,b,y,x]=a,w=l(d,c,"conv0"),I=l(c,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),E=l(f,g,"conv4"),F=l(g,b,"conv5"),D=l(b,y,"conv6"),$=l(y,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:w,conv1:I,conv2:T,conv3:C,conv4:E,conv5:F,conv6:D,conv7:$,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function Mme(e,t){let n=ia(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Sp(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function q$(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Mme(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 An(e,n),{params:i,paramMappings:n}}var yr=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 bk=class extends fn{constructor(n){super("TinyYolov2");G$(n),this._config=n}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(n,a){let r=Ur(n,a.conv0);return r=Mt(r,[2,2],[2,2],"same"),r=Ur(r,a.conv1),r=Mt(r,[2,2],[2,2],"same"),r=Ur(r,a.conv2),r=Mt(r,[2,2],[2,2],"same"),r=Ur(r,a.conv3),r=Mt(r,[2,2],[2,2],"same"),r=Ur(r,a.conv4),r=Mt(r,[2,2],[2,2],"same"),r=Ur(r,a.conv5),r=Mt(r,[2,2],[1,1],"same"),r=Ur(r,a.conv6),r=Ur(r,a.conv7),Qo(r,a.conv8,"valid",!1)}runMobilenet(n,a){let r=this.config.isFirstLayerConv2d?Fp(Qo(n,a.conv0,"valid",!1)):Gr(n,a.conv0);return r=Mt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv1),r=Mt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv2),r=Mt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv3),r=Mt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv4),r=Mt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv5),r=Mt(r,[2,2],[1,1],"same"),r=a.conv6?Gr(r,a.conv6):r,r=a.conv7?Gr(r,a.conv7):r,Qo(r,a.conv8,"valid",!1)}forwardInput(n,a){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return P(()=>{let s=ie(n.toBatchTensor(a,!1),"float32");return s=this.config.meanRgb?br(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(n,a){return this.forwardInput(await kt(n),a)}async detect(n,a={}){let{inputSize:r,scoreThreshold:s}=new yr(a),i=await kt(n),o=await this.forwardInput(i,r),l=P(()=>pt(o)[0].expandDims()),u={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(l,i.getReshapedInputDimensions(0),s);o.dispose(),l.dispose();let d=p.map(b=>b.box),c=p.map(b=>b.score),h=p.map(b=>b.classScore),m=p.map(b=>this.config.classes[b.label]);return e$(d.map(b=>b.rescale(r)),c,this.config.iouThreshold,!0).map(b=>new $s(c[b],h[b],m[b],d[b],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(n){return q$(n,this.config)}extractParams(n){let a=this.config.filterSizes||bk.DEFAULT_FILTER_SIZES,r=a?a.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return H$(n,this.config,this.boxEncodingSize,a)}async extractBoxes(n,a,r){let{width:s,height:i}=a,o=Math.max(s,i),l=o/s,u=o/i,p=n.shape[1],d=this.config.anchors.length,[c,h,m]=P(()=>{let y=n.reshape([p,p,d,this.boxEncodingSize]),x=y.slice([0,0,0,0],[p,p,d,4]),w=y.slice([0,0,0,4],[p,p,d,1]),I=this.withClassScores?Xa(y.slice([0,0,0,5],[p,p,d,this.config.classes.length]),3):ve(0);return[x,w,I]}),f=[],g=await h.array(),b=await c.array();for(let y=0;yr){let T=(x+Gf(b[y][x][w][0]))/p*l,C=(y+Gf(b[y][x][w][1]))/p*u,E=Math.exp(b[y][x][w][2])*this.config.anchors[w].x/p*l,F=Math.exp(b[y][x][w][3])*this.config.anchors[w].y/p*u,D=T-E/2,$=C-F/2,S={row:y,col:x,anchor:w},{classScore:M,label:B}=this.withClassScores?await this.extractPredictedClass(m,S):{classScore:1,label:0};f.push({box:new mp(D,$,D+E,$+F),score:I,classScore:I*M,label:B,...S})}}return c.dispose(),h.dispose(),m.dispose(),f}async extractPredictedClass(n,a){let{row:r,col:s,anchor:i}=a,o=await n.array();return Array(this.config.classes.length).fill(0).map((l,u)=>o[r][s][i][u]).map((l,u)=>({classScore:l,label:u})).reduce((l,u)=>l.classScore>u.classScore?l:u)}},nl=bk;nl.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var $p=class extends nl{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:L$,classes:["face"],...t?{anchors:W$,meanRgb:B$}:{anchors:z$,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new wt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?U$:V$}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function LCe(e,t=!0){let n=new $p(t);return n.extractWeights(e),n}var cg=class extends yr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var La=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function al(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Cp(l)?r(l):l.detection),i=a||(t instanceof Ce?await Dd(t,s):await $d(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ce&&l.dispose()),o}async function Dp(e,t,n,a,r){return al([e],t,async s=>n(s[0]),a,r)}var j$=.4,K$=[new Pe(1.603231,2.094468),new Pe(6.041143,7.080126),new Pe(2.882459,3.518061),new Pe(4.266906,5.178857),new Pe(9.041765,10.66308)],X$=[117.001,114.697,97.404];var Rp=class extends nl{constructor(){let t={withSeparableConvs:!0,iouThreshold:j$,classes:["face"],anchors:K$,meanRgb:X$,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 wt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var rt={ssdMobilenetv1:new tl,tinyFaceDetector:new Rp,tinyYolov2:new $p,faceLandmark68Net:new Ep,faceLandmark68TinyNet:new lg,faceRecognitionNet:new Ap,faceExpressionNet:new ng,ageGenderNet:new ig},Pme=(e,t)=>rt.ssdMobilenetv1.locateFaces(e,t),m_e=(e,t)=>rt.tinyFaceDetector.locateFaces(e,t),f_e=(e,t)=>rt.tinyYolov2.locateFaces(e,t),Ome=e=>rt.faceLandmark68Net.detectLandmarks(e),g_e=e=>rt.faceLandmark68TinyNet.detectLandmarks(e),b_e=e=>rt.faceRecognitionNet.computeFaceDescriptor(e),y_e=e=>rt.faceExpressionNet.predictExpressions(e),x_e=e=>rt.ageGenderNet.predictAgeAndGender(e),Lme=e=>rt.ssdMobilenetv1.load(e),v_e=e=>rt.tinyFaceDetector.load(e),w_e=e=>rt.tinyYolov2.load(e),k_e=e=>rt.faceLandmark68Net.load(e),I_e=e=>rt.faceLandmark68TinyNet.load(e),S_e=e=>rt.faceRecognitionNet.load(e),N_e=e=>rt.faceExpressionNet.load(e),T_e=e=>rt.ageGenderNet.load(e),C_e=Lme,__e=Pme,E_e=Ome;var dg=class extends La{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},rl=class extends dg{async run(){let t=await this.parentTask,n=await al(t,this.input,async a=>Promise.all(a.map(r=>rt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>uk(a,n[r]))}withAgeAndGender(){return new il(this,this.input)}},sl=class extends dg{async run(){let t=await this.parentTask;if(!t)return;let n=await Dp(t,this.input,a=>rt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return uk(t,n)}withAgeAndGender(){return new ol(this,this.input)}},Ms=class extends rl{withAgeAndGender(){return new Os(this,this.input)}withFaceDescriptors(){return new zs(this,this.input)}},Ps=class extends sl{withAgeAndGender(){return new Ls(this,this.input)}withFaceDescriptor(){return new Ws(this,this.input)}};var hg=class extends La{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},il=class extends hg{async run(){let t=await this.parentTask,n=await al(t,this.input,async a=>Promise.all(a.map(r=>rt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return fk(gk(a,i,o),s)})}withFaceExpressions(){return new rl(this,this.input)}},ol=class extends hg{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Dp(t,this.input,s=>rt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return fk(gk(t,a,r),n)}withFaceExpressions(){return new sl(this,this.input)}},Os=class extends il{withFaceExpressions(){return new Ms(this,this.input)}withFaceDescriptors(){return new zs(this,this.input)}},Ls=class extends ol{withFaceExpressions(){return new Ps(this,this.input)}withFaceDescriptor(){return new Ws(this,this.input)}};var mg=class extends La{constructor(n,a){super();this.parentTask=n;this.input=a}},zs=class extends mg{async run(){let t=await this.parentTask;return(await al(t,this.input,a=>Promise.all(a.map(r=>rt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>mk(t[r],a))}withFaceExpressions(){return new Ms(this,this.input)}withAgeAndGender(){return new Os(this,this.input)}},Ws=class extends mg{async run(){let t=await this.parentTask;if(!t)return;let n=await Dp(t,this.input,a=>rt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return mk(t,n)}withFaceExpressions(){return new Ps(this,this.input)}withAgeAndGender(){return new Ls(this,this.input)}};var fg=class extends La{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?rt.faceLandmark68TinyNet:rt.faceLandmark68Net}},gg=class extends fg{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Ce?await Dd(this.input,n):await $d(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Ce&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>Od(i,r[o]))}withFaceExpressions(){return new Ms(this,this.input)}withAgeAndGender(){return new Os(this,this.input)}withFaceDescriptors(){return new zs(this,this.input)}},bg=class extends fg{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ce?await Dd(this.input,[n]):await $d(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ce&&s.dispose()),Od(t,r)}withFaceExpressions(){return new Ps(this,this.input)}withAgeAndGender(){return new Ls(this,this.input)}withFaceDescriptor(){return new Ws(this,this.input)}};var yg=class extends La{constructor(n,a=new Oa){super();this.input=n;this.options=a}},zd=class extends yg{async run(){let{input:t,options:n}=this,a;if(n instanceof cg)a=rt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Oa)a=rt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof yr)a=rt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>bp({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new gg(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new rl(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new il(this.runAndExtendWithFaceDetections(),this.input)}},xg=class extends yg{async run(){let t=await new zd(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?bp({},n):void 0)})}withFaceLandmarks(t=!1){return new bg(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new sl(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ol(this.runAndExtendWithFaceDetection(),this.input)}};function TEe(e,t=new Oa){return new xg(e,t)}function yk(e,t=new Oa){return new zd(e,t)}async function zme(e,t){return yk(e,new Oa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function $Ee(e,t={}){return yk(e,new yr(t)).withFaceLandmarks().withFaceDescriptors()}var DEe=zme;function Y$(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*s,0))}var vg=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 ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof zr)return i;if(i instanceof Float32Array)return new zr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new zr(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>Y$(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new _d(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>zr.fromJSON(a));return new vg(n,t.distanceThreshold)}};function ZEe(e){let t=new Rp;return t.extractWeights(e),t}function Wme(e,t){let{width:n,height:a}=new En(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>Wme(r,{width:n,height:a}));if(Cp(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Od(bp(e,r),s)}return Wr(e)?bp(e,e.detection.forSize(n,a)):e instanceof ka||e instanceof wt?e.forSize(n,a):e}var lAe=y$;export{ig as AgeGenderNet,mp as BoundingBox,dt as Box,La as ComposableTask,zs as ComputeAllFaceDescriptorsTask,mg as ComputeFaceDescriptorsTaskBase,Ws as ComputeSingleFaceDescriptorTask,gg as DetectAllFaceLandmarksTask,zd as DetectAllFacesTask,fg as DetectFaceLandmarksTaskBase,yg as DetectFacesTaskBase,bg as DetectSingleFaceLandmarksTask,xg as DetectSingleFaceTask,En as Dimensions,f$ as FACE_EXPRESSION_LABELS,wt as FaceDetection,O$ as FaceDetectionNet,ng as FaceExpressionNet,Rs as FaceExpressions,Ep as FaceLandmark68Net,lg as FaceLandmark68TinyNet,T$ as FaceLandmarkNet,ka as FaceLandmarks,n$ as FaceLandmarks5,gp as FaceLandmarks68,_d as FaceMatch,vg as FaceMatcher,Ap as FaceRecognitionNet,ck as Gender,Ed as LabeledBox,zr as LabeledFaceDescriptors,Vr as NetInput,fn as NeuralNetwork,$s as ObjectDetection,Pe as Point,a$ as PredictedBox,fp as Rect,tl as SsdMobilenetv1,Oa as SsdMobilenetv1Options,Rp as TinyFaceDetector,cg as TinyFaceDetectorOptions,$p as TinyYolov2,yr as TinyYolov2Options,DEe as allFaces,zme as allFacesSsdMobilenetv1,$Ee as allFacesTinyYolov2,r$ as awaitMediaLoaded,s$ as bufferToImage,b_e as computeFaceDescriptor,wp as createCanvas,Kf as createCanvasFromMedia,j2e as createFaceDetectionNet,jTe as createFaceRecognitionNet,Dme as createSsdMobilenetv1,ZEe as createTinyFaceDetector,LCe as createTinyYolov2,yk as detectAllFaces,Ome as detectFaceLandmarks,g_e as detectFaceLandmarksTiny,E_e as detectLandmarks,TEe as detectSingleFace,b$ as draw,at as env,Y$ as euclideanDistance,fk as extendWithAge,mk as extendWithFaceDescriptor,bp as extendWithFaceDetection,uk as extendWithFaceExpressions,Od as extendWithFaceLandmarks,gk as extendWithGender,Dd as extractFaceTensors,$d as extractFaces,ake as fetchImage,l$ as fetchJson,lke as fetchNetWeights,Ds as fetchOrThrow,mke as fetchVideo,sa as getContext2dOrThrow,vp as getMediaDimensions,i$ as imageTensorToCanvas,o$ as imageToSquare,ywe as inverseSigmoid,JF as iou,lk as isMediaElement,jf as isMediaLoaded,ZTe as isWithAge,Wr as isWithFaceDetection,g$ as isWithFaceExpressions,Cp as isWithFaceLandmarks,t2e as isWithGender,T_e as loadAgeGenderModel,C_e as loadFaceDetectionModel,N_e as loadFaceExpressionModel,k_e as loadFaceLandmarkModel,I_e as loadFaceLandmarkTinyModel,S_e as loadFaceRecognitionModel,Lme as loadSsdMobilenetv1Model,v_e as loadTinyFaceDetectorModel,w_e as loadTinyYolov2Model,p$ as loadWeightMap,__e as locateFaces,wke as matchDimensions,QF as minBbox,rt as nets,e$ as nonMaxSuppression,br as normalize,t$ as padToSquare,x_e as predictAgeAndGender,y_e as recognizeFaceExpressions,Wme as resizeResults,yp as resolveInput,gwe as shuffleArray,Gf as sigmoid,Pme as ssdMobilenetv1,ze as tf,m_e as tinyFaceDetector,f_e as tinyYolov2,kt as toNetInput,ZF as utils,G$ as validateConfig,lAe as version}; //# sourceMappingURL=face-api.esm.js.map diff --git a/dist/face-api.esm.js.map b/dist/face-api.esm.js.map index 91f5354..45f1228 100644 --- a/dist/face-api.esm.js.map +++ b/dist/face-api.esm.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": 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g=this.backend.numDataIds();u=this.tidy(()=>d(this.backend,h));let x=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,g,x),x}}let{inputs:c,attrs:p}=t,m=b0(t)?null:t.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?e=a():(f=this.profiler.profileKernel(l,c,()=>a()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(f),e=f.outputs)}),o&&this.addTapeNode(l,c,e,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:e.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?e:e[0]}saveTensorsForBackwardMode(t){return 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this.trackTensor(i,e),i}makeVariable(t,e=!0,n,o){n=n||this.nextVariableId().toString(),o!=null&&o!==t.dtype&&(t=t.cast(o));let s=new dl(t,e,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(t,e){this.state.numTensors++,t.dtype===\"string\"&&this.state.numStringTensors++;let n=0;t.dtype!==\"complex64\"&&t.dtype!==\"string\"&&(n=t.size*Dp(t.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:n})),t instanceof 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e=v(r,\"s0\",\"broadcastArgs\",\"int32\"),n=v(t,\"s1\",\"broadcastArgs\",\"int32\");if(e.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). 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s={x:v(r,\"x\",\"cumsum\")},i={axis:t,exclusive:e,reverse:n};return T.runKernel(is,s,i)}var fm=k({cumsum_:uj});function cj(r,t,e,n=!1){let o=v(r,\"x\",\"denseBincount\"),s=v(t,\"weights\",\"denseBincount\");_(o.dtype===\"int32\",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),_(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${o.rank}.`),_(e>=0,()=>`size must be non-negative, but got ${e}.`),_(s.size===o.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${o.shape}, weights shape: ${s.shape}.`);let i={x:o,weights:s},a={size:e,binaryOutput:n};return T.runKernel(Jl,i,a)}var dh=k({denseBincount_:cj});function pj(r,t,e=\"NHWC\"){let n=v(r,\"x\",\"depthToSpace\",\"float32\"),o=e===\"NHWC\"?n.shape[1]:n.shape[2],s=e===\"NHWC\"?n.shape[2]:n.shape[3],i=e===\"NHWC\"?n.shape[3]:n.shape[1];_(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: 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${a.shape[2]}`);let c={x:u,filter:a},p={strides:e,pad:n,dilations:o},m=T.runKernel(ls,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Dx=k({dilation2d_:dj});var Ur={};Kt(Ur,{assertAndGetBroadcastShape:()=>Mt,getBroadcastDims:()=>mE,getReductionAxes:()=>ye});function mE(r,t){let e=r.length,n=[];for(let o=0;o1&&i===1&&n.unshift(s)}return n}function ye(r,t){let e=[];for(let n=0;n1)&&e.unshift(s)}return e}function Mt(r,t){let e=Math.max(r.length,t.length),n=new Array(e);for(let o=0;o`Error in dot: inputs must all be rank 1 or 2, but got ranks ${e.rank} and ${n.rank}.`);let o=e.rank===1?e.size:e.shape[1],s=n.rank===1?n.size:n.shape[0];if(_(o===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${o} and ${s}.`),e.rank===1&&n.rank===1){let i=R(e,[1,-1]),a=R(n,[-1,1]),u=Bt(i,a);return R(u,[])}else if(e.rank===1&&n.rank===2){let i=R(e,[1,-1]),a=R(n,[n.shape[0],n.shape[1]]),u=Bt(i,a);return R(u,[u.size])}else if(e.rank===2&&n.rank===1){let i=R(n,[-1,1]),a=Bt(e,i);return R(a,[a.size])}else{let i=R(n,[n.shape[0],n.shape[1]]);return Bt(e,i)}}var Rx=k({dot_:bj});function wj(r,...t){let e=t.map((o,s)=>v(o,`tensors${s}`,\"einsum\")),n={equation:r};return T.runKernel(zp,e,n)}var fE=k({einsum_:wj});function Ij(r){let e={x:v(r,\"x\",\"elu\",\"float32\")};return T.runKernel(cs,e)}var sa=k({elu_:Ij});function Cj(r){let t=v(r,\"x\",\"erf\");_(t.dtype===\"int32\"||t.dtype===\"float32\",()=>\"Input dtype must be `int32` or `float32`.\"),t.dtype===\"int32\"&&(t=J(t,\"float32\"));let e={x:t};return T.runKernel(La,e)}var Fx=k({erf_:Cj});function M0(r,t){for(let e=0;er[s]);return[e,o]}function vo(r,t){let e=t.map(n=>1);return dE(r,e,t)}function vj(r,t,e){_(M0(t,e),()=>`${r} supports only inner-most axes for now. 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T.runKernel(Pp,p,m)}var Dm=k({conv2DBackpropFilter_:l5});function Cc(r,t,e){if(e==null||e===\"linear\")return r;if(e===\"relu\")return $(r,So(t));throw new Error(`Cannot compute gradient for fused activation ${e}.`)}function vc(r,t){let e=t,n=ye(r.shape,t.shape);return n.length>0&&(e=pt(e,n)),R(e,r.shape)}function Sc(r,t,e,n){if(t===\"linear\")return r;if(t===\"relu\")return Pr(r);if(t===\"elu\")return sa(r);if(t===\"relu6\")return xm(r);if(t===\"prelu\")return _u(r,e);if(t===\"leakyrelu\")return vu(r,n);if(t===\"sigmoid\")return tn(r);throw new Error(`Unknown fused activation ${t}.`)}var Nc=(r,t)=>!(r>0)||t===\"linear\";function u5({x:r,filter:t,strides:e,pad:n,dataFormat:o=\"NHWC\",dilations:s=[1,1],dimRoundingMode:i,bias:a,activation:u=\"linear\",preluActivationWeights:l,leakyreluAlpha:c}){if(u=u||\"linear\",Nc(T.state.gradientDepth,u)===!1){_(o===\"NHWC\",()=>`Error in fused conv2d: got dataFormat of ${o} but only NHWC is currently supported for the case of gradient depth is 0 and 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className(){return\"Momentum\"}constructor(t,e,n=!1){super(t),this.learningRate=t,this.momentum=e,this.useNesterov=n,this.accumulations=[],this.m=ft(this.momentum)}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n];this.accumulations[o]==null&&(this.accumulations[o]={originalName:`${n}/momentum`,variable:B(()=>vt(s).variable(!1))});let i=this.accumulations[o].variable,a=Array.isArray(t)?t[o].tensor:t[n];a!=null&&B(()=>{let u,l=Y($(this.m,i),a);this.useNesterov?u=Y($(this.c,Y(a,$(l,this.m))),s):u=Y($(this.c,l),s),i.assign(l),s.assign(u)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Tt(this.accumulations.map(t=>t.variable))}setMomentum(t){this.momentum=t}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var Dc=class extends qr{static get className(){return\"RMSProp\"}constructor(t,e=.9,n=0,o=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=T.backend.epsilon()),t==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n],i=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${n}/rms`,variable:B(()=>vt(s).variable(i))}),this.accumulatedMoments[o]==null&&(this.accumulatedMoments[o]={originalName:`${n}/momentum`,variable:B(()=>vt(s).variable(i))}),this.accumulatedMeanGrads[o]==null&&this.centered&&(this.accumulatedMeanGrads[o]={originalName:`${n}/mg`,variable:B(()=>vt(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedMeanSquares[o].variable,l=this.accumulatedMoments[o].variable;B(()=>{let c=Y($(u,this.decay),$(Gt(a),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[o].variable,m=Y($(p,this.decay),$(a,1-this.decay)),f=ut($(a,this.learningRate),Ne(lt(c,Y(Gt(m),this.epsilon)))),d=Y($(l,this.momentum),f);u.assign(c),p.assign(m),l.assign(d);let h=lt(s,d);s.assign(h)}else{let 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used.\");return this.model.fitDataset(t,e)}async trainOnBatch(t,e){return this.model.trainOnBatch(t,e)}static fromConfig(t,e,n={},o=!1){let s,i={};if(e instanceof Array){if(e[0].className==null||e[0].className===\"Merge\")throw new z(\"Legacy serialization format not supported yet.\");s=e}else y.assert(e.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=e.layers,delete e.layers,i=e;let a=new t(i);if(!(a instanceof xa))throw new St(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(let u of s){let c=bn(u,void 0,o);o&&c.setFastWeightInitDuringBuild(!0),a.add(c)}return a}set stopTraining(t){if(this.model==null)throw new z(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new z(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");return 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extends nn{apply(t){return O$(t)}};fb.className=\"hardSigmoid\";Q.registerClass(fb);var db=class extends nn{apply(t){return ii(t)}};db.className=\"softplus\";Q.registerClass(db);var hb=class extends nn{apply(t){return F$(t)}};hb.className=\"softsign\";Q.registerClass(hb);var gb=class extends nn{apply(t){return ea(t)}};gb.className=\"tanh\";Q.registerClass(gb);var rf=class extends nn{apply(t,e=-1){return Eu(t,e)}};rf.className=\"softmax\";Q.registerClass(rf);var xb=class extends nn{apply(t,e=-1){return dm(t,e)}};xb.className=\"logSoftmax\";Q.registerClass(xb);var yb=class extends nn{apply(t,e=1){return B(()=>$(tn($(t,e)),t))}};yb.className=\"swish\";Q.registerClass(yb);var bb=class extends nn{apply(t){return B(()=>$(t,ea(ii(t))))}};bb.className=\"mish\";Q.registerClass(bb);function fi(r){return r.getClassName()}function FN(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,\"activation\")}function di(r){if(r==null){let t={};return t.className=\"linear\",t.config={},FN(t)}if(typeof r==\"string\"){let t={};return t.className=r,t.config={},FN(t)}else return r instanceof nn?r:FN(r)}function ON(r){if(r!=null&&typeof r!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var wb=class extends Q.Serializable{},Lu=class extends wb{constructor(t){super(),ON(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=Te([1]);return this.hasL1&&(e=Y(e,pt($(this.l1,Ee(t))))),this.hasL2&&(e=Y(e,pt($(this.l2,Oc(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Lu.className=\"L1L2\";Q.registerClass(Lu);function gR(r){return ON(r),new Lu({l1:r!=null?r.l1:null,l2:0})}function xR(r){return ON(r),new Lu({l2:r!=null?r.l2:null,l1:0})}var dR={l1l2:\"L1L2\"};function me(r){return Rm(r)}function hR(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,\"regularizer\")}function Ce(r){if(r==null)return null;if(typeof r==\"string\"){let e={className:r in dR?dR[r]:r,config:{}};return hR(e)}else return r instanceof wb?r:hR(r)}var nf=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=Nt(t);let n=Pr(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};nf.className=\"ReLU\";Q.registerClass(nf);var of=class extends Et{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=Nt(t);return vu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};of.className=\"LeakyReLU\";Q.registerClass(of);var sf=class extends Et{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER=\"zeros\",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=he(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ce(t.alphaRegularizer),this.alphaConstraint=Ve(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes==\"number\")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Wt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight(\"alpha\",e,\"float32\",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o(Oe(t),t===\"channelsFirst\"?Vt(r,[0,2,3,1]):r))}function PN(r,t){return B(()=>(Oe(t),t===\"channelsFirst\"?Vt(r,[0,2,3,4,1]):r))}function d7(r,t,e,n=1,o=\"valid\",s,i=1){return B(()=>{if(s==null&&(s=hn()),Oe(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.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(e!=null&&e.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s===\"channelsFirst\"&&(r=Vt(r,[0,2,1])),o===\"causal\")throw new St(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");let a=um(r,t,n,o===\"same\"?\"same\":\"valid\",\"NWC\",i);return e!=null&&(a=gn(a,e)),a})}function yR(r,t,e,n=[1,1],o=\"valid\",s,i,a=null){return B(()=>{if(s==null&&(s=hn()),Oe(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(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Lh(r,s);if(o===\"causal\")throw new St(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=Ru.conv2d({x:u,filter:t,strides:n,pad:o===\"same\"?\"same\":\"valid\",dilations:i,dataFormat:\"NHWC\",bias:e,activation:a}),s===\"channelsFirst\"&&(u=Vt(u,[0,3,1,2])),u})}function h7(r,t,e,n=[1,1,1],o=\"valid\",s,i){return B(()=>{if(s==null&&(s=hn()),Oe(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(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=PN(r,s);if(o===\"causal\")throw new St(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return a=Tx(a,t,n,o===\"same\"?\"same\":\"valid\",\"NDHWC\",i),e!=null&&(a=gn(a,e)),s===\"channelsFirst\"&&(a=Vt(a,[0,4,1,2,3])),a})}var qc=class extends Et{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",qc.verifyArgs(e),this.rank=t,Qe(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new St(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zu(e.kernelSize,t,\"kernelSize\"),this.strides=zu(e.strides==null?1:e.strides,t,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,fn(this.padding),this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Oe(this.dataFormat),this.activation=di(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=he(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ve(e.biasConstraint),this.biasRegularizer=Ce(e.biasRegularizer),this.activityRegularizer=Ce(e.activityRegularizer),this.dilationRate=zu(e.dilationRate==null?1:e.dilationRate,t,\"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(t){if(uo(\"kernelSize\"in t,\"required key 'kernelSize' not in config\"),typeof t.kernelSize!=\"number\"&&!Ey(t.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(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:fi(this.activation),useBias:this.useBias,biasInitializer:_e(this.biasInitializer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},Bu=class extends qc{constructor(t,e){super(t,e),this.kernel=null,Bu.verifyArgs(e),this.filters=e.filters,Qe(this.filters,\"filters\"),this.kernelInitializer=he(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ve(e.kernelConstraint),this.kernelRegularizer=Ce(e.kernelRegularizer)}build(t){t=Wt(t);let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",o,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:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=Nt(t);let n,o=this.bias==null?null:this.bias.read(),s=Ay(this.activation.getClassName());if(s!=null&&this.rank===2)n=yR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=d7(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=yR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=h7(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new St(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Wt(t);let e=[],n=this.dataFormat===\"channelsLast\"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},El=class extends Bu{constructor(t){super(2,t),El.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Ey(t.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(t.kernelSize)}.`)}};El.className=\"Conv2D\";Q.registerClass(El);var Al=class extends Bu{constructor(t){super(3,t),Al.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Al.className=\"Conv3D\";Q.registerClass(Al);var cf=class extends El{constructor(t){if(super(t),this.inputSpec=[new Ie({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Wt(t),t.length!==4)throw new z(\"Input should have rank 4; Received input shape: \"+JSON.stringify(t));let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",o,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=Nt(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat===\"channelsFirst\"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=hi(u,m,c,this.padding),h=hi(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Vt(n,[0,2,3,1]));let x=pm(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(x=Vt(x,[0,3,1,2])),this.bias!=null&&(x=gn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=Wt(t);let e=t.slice(),n,o,s;this.dataFormat===\"channelsFirst\"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=hi(e[o],u,i,this.padding),e[s]=hi(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};cf.className=\"Conv2DTranspose\";Q.registerClass(cf);var pf=class extends Al{constructor(t){if(super(t),this.inputSpec=[new Ie({ndim:5})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Wt(t),t.length!==5)throw new z(\"Input should have rank 5; Received input shape: \"+JSON.stringify(t));let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",o,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=Nt(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat===\"channelsFirst\"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=hi(l,h,m,this.padding),w=hi(c,g,f,this.padding),I=hi(p,x,d,this.padding),N=[s,b,w,I,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Vt(n,[0,2,3,4,1]));let E=Ex(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(E=Vt(E,[0,4,1,2,3])),this.bias!==null&&(E=gn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Wt(t);let e=t.slice(),n,o,s,i;this.dataFormat===\"channelsFirst\"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=hi(e[o],c,a,this.padding),e[s]=hi(e[s],p,u,this.padding),e[i]=hi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};pf.className=\"Conv3DTranspose\";Q.registerClass(pf);var Ib=class extends Bu{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(e.padding!=null&&e.padding!==\"same\"&&e.padding!==\"valid\")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=he(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ce(e.depthwiseRegularizer),this.depthwiseConstraint=Ve(e.depthwiseConstraint),this.pointwiseInitializer=he(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ce(e.pointwiseRegularizer),this.pointwiseConstraint=Ve(e.pointwiseConstraint)}build(t){if(t=Wt(t),t.length{t=Nt(t);let n;if(this.rank===1)throw new St(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(t=Vt(t,[0,2,3,1])),n=Im(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=gn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=Vt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=_e(this.depthwiseInitializer),t.pointwiseInitializer=_e(this.pointwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.pointwiseRegularizer=me(this.pointwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseConstraint),t.pointwiseConstraint=Be(this.pointwiseConstraint),t}};Ib.className=\"SeparableConv\";var mf=class extends Ib{constructor(t){super(2,t)}};mf.className=\"SeparableConv2D\";Q.registerClass(mf);var Vu=class extends Bu{constructor(t){super(1,t),Vu.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Ey(t.kernelSize,\"number\",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};Vu.className=\"Conv1D\";Q.registerClass(Vu);var ff=class extends Et{constructor(t){super(t),typeof t.cropping==\"number\"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]==\"number\"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?\"channelsLast\":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat===\"channelsFirst\"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=Nt(t),this.dataFormat===\"channelsLast\"){let n=_h(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return _h(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=_h(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return _h(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};ff.className=\"Cropping2D\";Q.registerClass(ff);var df=class extends Et{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Oe(this.dataFormat),this.interpolation=t.interpolation==null?\"nearest\":t.interpolation,T$(this.interpolation)}computeOutputShape(t){if(this.dataFormat===\"channelsFirst\"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=Nt(t),o=n.shape;if(this.dataFormat===\"channelsFirst\"){n=Vt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation===\"nearest\"?lo.resizeNearestNeighbor(n,[s,i]):lo.resizeBilinear(n,[s,i]);return Vt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation===\"nearest\"?lo.resizeNearestNeighbor(n,[s,i]):lo.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};df.className=\"UpSampling2D\";Q.registerClass(df);function g7(r,t,e=[1,1],n=\"valid\",o,s){return B(()=>{o==null&&(o=hn()),Oe(o);let i=Lh(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=oa(i,t,e,n===\"same\"?\"same\":\"valid\",\"NHWC\",s),o===\"channelsFirst\"&&(i=Vt(i,[0,3,1,2])),i})}var hf=class extends qc{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=he(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ve(t.depthwiseConstraint),this.depthwiseRegularizer=Ce(t.depthwiseRegularizer)}build(t){if(t=Wt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat===\"channelsFirst\"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",o,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(t,e){return B(()=>{t=Nt(t);let n=g7(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=gn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Wt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=Tn(e,this.kernelSize[0],this.padding,this.strides[0]),i=Tn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=_e(this.depthwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseRegularizer),t}};hf.className=\"DepthwiseConv2D\";Q.registerClass(hf);function MN(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z(\"When inputs is an array, neither initialState or constants should be provided\");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function LN(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(dn(2,u));if(t=Vt(t,l),s!=null)throw new St(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");i&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),o!=null&&(o=J(J(o,\"bool\"),\"float32\"),o.rank===u-1&&(o=ir(o,-1)),o=Vt(o,l)),n&&(t=hr(t,0),o!=null&&(o=hr(o,0)));let c=[],p,m=e,f=t.shape[0],d=xr(t),h;o!=null&&(h=xr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=lt(Ir(N),N),A=Y($(w[0],N),$(m[0],E)),D=m.map((F,P)=>Y($(w[1][P],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=qe(c,1)),[p,g,m]})}var _n=class extends Et{constructor(t){super(t);let e;if(t.cell==null)throw new z(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(t.cell)?e=new Xc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ie({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return dn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){zy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ie({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new kn(\"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(o=>Te([n,o])):this.states_=[Te([n,this.cell.stateSize])];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Te([n,o])):this.states_[0]=Te([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let o=0;o$e(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=MN(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ie({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof rn){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=Nt(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} 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 a={training:o},l=LN((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=Te(t.shape);return e=pt(e,[1,2]),e=kl(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Py(e,[1,n]):e):this.cell.stateSize>1?[Py(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===_n.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=bn(o,n);return new t(Object.assign(e,{cell:s}))}};_n.className=\"RNN\";Q.registerClass(_n);var Dl=class extends Et{},Kc=class extends Dl{constructor(t){super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=t.units,Qe(this.units,\"units\"),this.activation=di(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Fc([1,pi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Fc([1,pi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Wt(t),this.kernel=this.addWeight(\"kernel\",[t[t.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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0Ir(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0Ir(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Do($(t,i),this.kernel.read()):s=Do(t,this.kernel.read()),this.bias!=null&&(s=gn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=Y(s,Do(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:fi(this.activation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),recurrentInitializer:_e(this.recurrentInitializer),biasInitializer:_e(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};Kc.className=\"SimpleRNNCell\";Q.registerClass(Kc);var gf=class extends _n{constructor(t){t.cell=new Kc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};gf.className=\"SimpleRNN\";Q.registerClass(gf);var jc=class extends Dl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.resetAfter)throw new z(\"GRUCell does not support reset_after parameter set to true.\");this.units=t.units,Qe(this.units,\"units\"),this.activation=di(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=di(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Fc([1,pi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Fc([1,pi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Wt(t);let e=t[t.length-1];this.kernel=this.addWeight(\"kernel\",[e,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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0Ir(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0Ir(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};xf.className=\"GRU\";Q.registerClass(xf);var $l=class extends Dl{constructor(t){super(t),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=t.units,Qe(this.units,\"units\"),this.activation=di(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=di(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Fc([1,pi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Fc([1,pi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Wt(t);let n=t[t.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 o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends xn{apply(u,l){let c=s.apply([i]),p=new Pu().apply([i]),m=s.apply([i*2]);return vN(vN(c,p),m)}},e.className=\"CustomInit\",e)}else o=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0Ir(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0Ir(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};yf.className=\"LSTM\";Q.registerClass(yf);var Xc=class extends Dl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{ci(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(bn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return Ah(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Ly(t(),e),a=()=>Ou(i,t,n);return!o||o<=1?$e(a().clone()):Array(o).fill(void 0).map(a).map(l=>$e(l.clone()))}var x7=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols==\"function\")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z(\"ConvRNN2D cell does not support constants\");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=Te(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new kn(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.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(()=>Te(s)):this.states_=[Te(s)];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Te(s)):this.states_[0]=Te(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let a=0;a$e(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e===\"channelsFirst\",l=t[u?3:2],c=t[u?4:3],p=Tn(l,o[0],s,i[0],a[0]),m=Tn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};Cb.className=\"ConvRNN2D\";var Yc=class extends $l{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,Qe(this.filters,\"filters\"),this.kernelSize=zu(n,2,\"kernelSize\"),this.kernelSize.forEach(u=>Qe(u,\"kernelSize\")),this.strides=zu(o||1,2,\"strides\"),this.strides.forEach(u=>Qe(u,\"strides\")),this.padding=s||\"valid\",fn(this.padding),this.dataFormat=i||\"channelsLast\",Oe(this.dataFormat),this.dilationRate=zu(a||1,2,\"dilationRate\"),this.dilationRate.forEach(u=>Qe(u,\"dilationRate\"))}build(t){var e;t=Wt(t);let n=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight(\"kernel\",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends xn{apply(m,f){let d=l.apply([c]),h=dr([c]),g=l.apply([c*2]);return Om([d,h,g])}},e.className=\"CustomInit\",e)}else u=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0Ir(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(nt,st,at)=>!st||!st[at]?nt:$(st[at],nt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0Ir(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[I,N,E,A]=gr(this.kernel.read(),a,w),[D,F,P,V]=this.useBias?gr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,I,D,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,P,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=gr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let K=this.recurrentActivation.apply(Y(c,h)),X=this.recurrentActivation.apply(Y(p,g)),Z=Y($(X,i),$(K,this.activation.apply(Y(m,x)))),et=$(this.recurrentActivation.apply(Y(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=x7(t,[\"units\"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=Sn(t,e,this.strides,o||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?gn(s,n,this.dataFormat):s}recurrentConv(t,e){return Sn(t,e,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Yc.className=\"ConvLSTM2DCell\";Q.registerClass(Yc);var bf=class extends Cb{constructor(t){let e=new Yc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};bf.className=\"ConvLSTM2D\";Q.registerClass(bf);var Zc=class extends Et{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=Nt(t);if(0Ly(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Zc.className=\"Dropout\";Q.registerClass(Zc);var wf=class extends Zc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};wf.className=\"SpatialDropout1D\";Q.registerClass(wf);var If=class extends Et{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,Qe(this.units,\"units\"),this.activation=di(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ve(t.kernelConstraint),this.biasConstraint=Ve(t.biasConstraint),this.kernelRegularizer=Ce(t.kernelRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Wt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[e,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]:e}}],this.built=!0}computeOutputShape(t){t=Wt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Nt(t),o=Ay(this.activation.getClassName()),s;return o!=null?s=Do(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Do(n,this.kernel.read()),this.bias!=null&&(s=gn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:fi(this.activation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),biasInitializer:_e(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};If.className=\"Dense\";Q.registerClass(If);var Cf=class extends Et{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Wt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to \"Flatten\" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[t[0],Ao(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Nt(t);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let o=[0];for(let s=2;s{this.invokeCallHook(t,e);let n=Nt(t);return this.activation.apply(n)})}getConfig(){let t={activation:fi(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};vf.className=\"Activation\";Q.registerClass(vf);var Sf=class extends Et{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=Nt(t),A$(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};Sf.className=\"RepeatVector\";Q.registerClass(Sf);var Nf=class extends Et{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e{this.invokeCallHook(t,e);let n=Nt(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};Nf.className=\"Reshape\";Q.registerClass(Nf);var kf=class extends Et{constructor(t){if(super(t),t.dims==null)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${t.dims} instead.`);let e=dn(1,t.dims.length+1);if(!y.arraysEqual(t.dims.slice().sort(),e))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(t.dims)+\" `dims` must contain consecutive integers starting from 1.\");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ie({ndim:this.dims.length+1})]}computeOutputShape(t){t=Wt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Vt(Nt(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};kf.className=\"Permute\";Q.registerClass(kf);var Tf=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=Nt(t),o=-1;return fc(ai(n,this.maskValue),o)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Nt(t),o=-1,s=!0,i=fc(ai(n,this.maskValue),o,s);return $(n,J(i,n.dtype))})}};Tf.className=\"Masking\";Q.registerClass(Tf);var _f=class extends Et{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(we(t.inputLength))}this.inputDim=t.inputDim,Qe(this.inputDim,\"inputDim\"),this.outputDim=t.outputDim,Qe(this.outputDim,\"outputDim\"),this.embeddingsInitializer=he(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ce(t.embeddingsRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.embeddingsConstraint=Ve(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight(\"embeddings\",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=Nt(t),ai(t,vt(t))):null)}computeOutputShape(t){if(t=Wt(t),this.inputLength==null)return[...t,this.outputDim];let e=we(this.inputLength);if(e.length!==t.length-1)throw new z(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o{this.invokeCallHook(t,e);let n=Nt(t);n.dtype!==\"int32\"&&(n=en(n,\"int32\"));let o=My(this.embeddings.read(),R(n,[n.size]));return R(o,Wt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:_e(this.embeddingsInitializer),embeddingsRegularizer:me(this.embeddingsRegularizer),activityRegularizer:me(this.activityRegularizer),embeddingsConstraint:Be(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};_f.className=\"Embedding\";Q.registerClass(_f);var Fl=class extends Et{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new St}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(t)}.`);let n=t[0]==null?null:t[0].slice(1);for(let s=1;ss.length);t.indexOf(null)===-1&&Eo(o).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(t,e){return B(()=>{if(t=t,this.reshapeRequired){let n=[],o=t.map(s=>s.rank);if(o.indexOf(null)===-1){let s=pi(o);for(let i of t){let a=i.rank;for(let u=0;u1){let c=dn(1,l).concat([0]);n.push(Vt(u,c)),s=!0}else n.push(u)}let i=this.mergeFunction(n),a=i.rank;if(s){if(a==null){let u=i.shape,l=u.length,c=u[l-1],p=[c].concat(u.slice(0,u.length-1));i=R(Vt(R(i,[-1,c]),[1,0]),p)}else if(a>1){let u=[a-1].concat(dn(0,a-1));i=Vt(i,u)}}return i}}else return this.mergeFunction(t)})}computeOutputShape(t){t=t;let e;t[0]==null?e=null:e=t[0].slice(1);for(let o=1;o{if(e==null)return null;if(!Array.isArray(e))throw new z(\"`mask` should be an Array\");if(!Array.isArray(t))throw new z(\"`inputs` should be an Array\");if(e.length!==t.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${t.length} vs ${e.length})`);if(e.every(o=>o==null))return null;e=e.map(o=>o==null?o:ir(o,0));let n=e[0];for(let o=1;o{let e=t[0].clone();for(let n=1;n{let e=t[0].clone();for(let n=1;n{let e=t[0].clone();for(let n=1;n{let e=t[0];for(let n=1;n{let e=t[0];for(let n=1;n1)throw new z(\"A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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Array.isArray(this.axes)?o=this.axes.map((s,i)=>zh(s,t[i].shape.length)):o=[zh(this.axes,e.shape.length),zh(this.axes,n.shape.length)],this.normalize&&(e=Dh(e,o[0]),n=Dh(n,o[1])),y7(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[zh(this.axes,t.length),zh(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new St(\"Dot layer does not support tensors of 4D or higher rank yet.\");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Of.className=\"Dot\";Q.registerClass(Of);var Pf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Nt(t);return Ou(()=>Y(Pm(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};Pf.className=\"GaussianNoise\";Q.registerClass(Pf);var Mf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Nt(t);return this.rate>0&&this.rate<1?Ou(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,Pm(n.shape,1,s))},()=>n,e.training||!1):n})}};Mf.className=\"GaussianDropout\";Q.registerClass(Mf);var Lf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return 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e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ie({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=Nt(t),s=o.shape,i=s.length,a=dn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=To(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,dn(0,i).slice(0,i-1)),m=()=>{if(p){let 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t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_e(this.betaInitializer),gammaInitializer:_e(this.gammaInitializer),movingMeanInitializer:_e(this.movingMeanInitializer),movingVarianceInitializer:_e(this.movingVarianceInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer),betaConstraint:Be(this.betaConstraint),gammaConstraint:Be(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};zf.className=\"BatchNormalization\";Q.registerClass(zf);var Bf=class extends Et{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.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 e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw 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n=Nt(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=xc(n,this.axis,!0),l=To(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.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(e==null&&(e=hn()),e!==\"channelsLast\"&&e!==\"channelsFirst\")throw new z(`Unknown data format: ${e}. 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s===\"max\"?i=ku(r,t,e,a):i=bu(r,t,e,a),o===\"channelsFirst\"&&(i=Vt(i,[0,3,1,2])),i})}function bR(r,t,e,n,o,s){return B(()=>{Oe(o),bN(s),fn(n),e==null&&(e=[1,1,1]),n==null&&(n=\"valid\"),o==null&&(o=hn()),s==null&&(s=\"max\"),r=PN(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=qx(r,t,e,a):i=xx(r,t,e,a),o===\"channelsFirst\"&&(i=Vt(i,[0,4,1,2,3])),i})}var vb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize==\"number\")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]==\"number\")this.poolSize=t.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(t.poolSize)}`);if(Qe(this.poolSize,\"poolSize\"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides==\"number\")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof 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vb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),fn(o),_b(t,e,n,o,s,\"avg\")}};Wf.className=\"AveragePooling1D\";Q.registerClass(Wf);var Sb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.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 ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];Qe(this.poolSize,\"poolSize\"),Qe(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Oe(this.dataFormat),fn(this.padding),this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){t=Wt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(Nt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Uf=class extends Sb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),fn(o),_b(t,e,n,o,s,\"max\")}};Uf.className=\"MaxPooling2D\";Q.registerClass(Uf);var Hf=class extends Sb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),fn(o),_b(t,e,n,o,s,\"avg\")}};Hf.className=\"AveragePooling2D\";Q.registerClass(Hf);var Nb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.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 ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];Qe(this.poolSize,\"poolSize\"),Qe(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Oe(this.dataFormat),fn(this.padding),this.inputSpec=[new Ie({ndim:5})]}computeOutputShape(t){t=Wt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[4]:t[3];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),o=Tn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(Nt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},qf=class extends Nb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),fn(o),bR(t,e,n,o,s,\"max\")}};qf.className=\"MaxPooling3D\";Q.registerClass(qf);var Kf=class extends Nb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),fn(o),bR(t,e,n,o,s,\"avg\")}};Kf.className=\"AveragePooling3D\";Q.registerClass(Kf);var kb=class extends Et{constructor(t){super(t),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new St}},jf=class extends kb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=Nt(t);return ke(n,1)})}};jf.className=\"GlobalAveragePooling1D\";Q.registerClass(jf);var Xf=class extends kb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=Nt(t);return Sr(n,1)})}};Xf.className=\"GlobalMaxPooling1D\";Q.registerClass(Xf);var Tb=class extends Et{constructor(t){super(t),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Oe(this.dataFormat),this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat===\"channelsLast\"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new St}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Yf=class extends Tb{call(t,e){return B(()=>{let n=Nt(t);return this.dataFormat===\"channelsLast\"?ke(n,[1,2]):ke(n,[2,3])})}};Yf.className=\"GlobalAveragePooling2D\";Q.registerClass(Yf);var Zf=class extends Tb{call(t,e){return B(()=>{let n=Nt(t);return this.dataFormat===\"channelsLast\"?Sr(n,[1,2]):Sr(n,[2,3])})}};Zf.className=\"GlobalMaxPooling2D\";Q.registerClass(Zf);var Eb=class extends Et{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}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(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=bn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},Jf=class extends Eb{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Wt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Wt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=Nt(t),LN((i,a)=>[Nt(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Jf.className=\"TimeDistributed\";Q.registerClass(Jf);function v7(r){da(N$,\"BidirectionalMergeMode\",r)}var S7=\"concat\",Qf=class extends Eb{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=bn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=bn(o),this.forwardLayer.name=\"forward_\"+this.forwardLayer.name,this.backwardLayer.name=\"backward_\"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?S7:t.mergeMode,v7(this.mergeMode),t.weights)throw new St(\"weights support is not implemented for Bidirectional layer yet.\");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode===\"concat\"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):kr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=MN(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];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.\");e.initialState=n,i.push(...n);let c=n.map(p=>new Ie({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new St(\"Support for constants in Bidirectional layers is not implemented yet.\");let u=i[0]instanceof rn;for(let l of i)if(l instanceof rn!==u)throw new z(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=hr(s,1));let a;return this.mergeMode===\"concat\"?a=Om([o,s]):this.mergeMode===\"sum\"?a=Y(o,s):this.mergeMode===\"ave\"?a=$(.5,Y(o,s)):this.mergeMode===\"mul\"?a=$(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){ci(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),ci(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=bn(e.layer);if(delete e.layer,e.numConstants!=null)throw new St(\"Deserialization of a Bidirectional layer with numConstants present is not supported yet.\");let o=e;return o.layer=n,new t(o)}};Qf.className=\"Bidirectional\";Q.registerClass(Qf);var td=class extends Et{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>(t=Nt(t),t.dtype!==\"float32\"&&(t=en(t,\"float32\")),Y($(t,this.scale),this.offset)))}};td.className=\"Rescaling\";Q.registerClass(td);var{resizeBilinear:N7,cropAndResize:k7}=lo,ed=class extends Et{constructor(t){super(t),this.height=t.height,this.width=t.width}centerCrop(t,e,n,o,s,i,a,u){return B(()=>{let l,c=!1,p=e/i,m=n/a,f=(o+e)/i,d=(s+n)/a,h=[p,m,f,d],g=[];t.rank===3?(c=!0,l=qe([t])):l=t;for(let N=0;N{let s=N7(t,[e,n]);return en(s,o)})}call(t,e){return B(()=>{let n=Nt(t),o=n.dtype,s=n.shape,i=s[s.length-3],a=s[s.length-2],u=0;i!==this.height&&(u=Math.floor((i-this.height)/2));let l=0;return a!==this.width&&(l=Math.floor((a-this.width)/2),l===0&&(l=1)),u>=0&&l>=0?this.centerCrop(n,u,l,this.height,this.width,i,a,o):this.upsize(t,this.height,this.width,o)})}getConfig(){let t={height:this.height,width:this.width},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Wt(t);let e=t.length-3,n=t.length-2;return t[e]=this.height,t[n]=this.width,t}};ed.className=\"CenterCrop\";Q.registerClass(ed);function wR(r,t,e,n){let o=Nt(r);if(o.dtype!==\"int32\"&&(o=en(o,\"int32\")),t===\"int\")return o;let s=o.shape;if(o.rank===0&&(o=ir(o,-1)),t===\"oneHot\"&&o.shape[o.shape.length-1]!==1&&(o=ir(o,-1)),o.rank>2)throw new z(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${o.rank}.`);let i=[\"multiHot\",\"oneHot\"].includes(t),a=o,u;if(typeof n!=\"undefined\"&&t===\"count\"?u=dh(a,n,e,i):u=dh(a,[],e,i),t!==\"tfIdf\")return u;if(n)return $(u,n);throw new z(\"When outputMode is 'tfIdf', weights must be provided.\")}var rd=class extends Et{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode=\"multiHot\"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return t=Wt(t),t==null?[this.numTokens]:this.outputMode===\"oneHot\"&&t[t.length-1]!==1?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)}call(t,e){return B(()=>{t=Nt(t),t.dtype!==\"int32\"&&(t=en(t,\"int32\"));let n;if(typeof e.countWeights!=\"undefined\"){if(this.outputMode!==\"count\")throw new z(`countWeights is not used when outputMode !== count.\n Received countWeights=${e.countWeights}`);n=Nt(e.countWeights)}let o=Sr(t),s=xl(t),i=Fe(this.numTokens,o).bufferSync().get(0),a=cn(s,0).bufferSync().get(0);if(!(i&&a))throw new z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return wR(t,this.outputMode,this.numTokens,n)})}};rd.className=\"CategoryEncoding\";Q.registerClass(rd);var _7=[\"bilinear\",\"nearest\"],IR=new Set(_7),nd=class extends Et{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if(IR.has(t.interpolation))this.interpolation=t.interpolation;else throw new z(`Invalid interpolation parameter: 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new nd(r)}function GJ(r){return new rd(r)}var TR={};Kt(TR,{MAPE:()=>QJ,MSE:()=>rQ,binaryAccuracy:()=>WJ,binaryCrossentropy:()=>UJ,categoricalAccuracy:()=>qJ,categoricalCrossentropy:()=>KJ,cosineProximity:()=>YJ,mape:()=>tQ,meanAbsoluteError:()=>ZJ,meanAbsolutePercentageError:()=>JJ,meanSquaredError:()=>eQ,mse:()=>nQ,precision:()=>jJ,recall:()=>XJ,sparseCategoricalAccuracy:()=>HJ});function WJ(r,t){return Fh(r,t)}function UJ(r,t){return Zy(r,t)}function HJ(r,t){return Jy(r,t)}function qJ(r,t){return Oh(r,t)}function KJ(r,t){return Ph(r,t)}function jJ(r,t){return TN(r,t)}function XJ(r,t){return Z$(r,t)}function YJ(r,t){return Rh(r,t)}function ZJ(r,t){return Zm(r,t)}function JJ(r,t){return Mu(r,t)}function QJ(r,t){return Mu(r,t)}function tQ(r,t){return Mu(r,t)}function eQ(r,t){return ga(r,t)}function rQ(r,t){return ga(r,t)}function nQ(r,t){return ga(r,t)}var _R={};Kt(_R,{modelFromJSON:()=>mR});var ER={};Kt(ER,{l1:()=>sQ,l1l2:()=>oQ,l2:()=>iQ});function oQ(r){return new Lu(r)}function 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i.setItem(o,s),[i.idTensor]}case\"TensorListGetItem\":{let n=C(\"tensorListId\",r,t,e),o=C(\"index\",r,t,e),s=C(\"elementShape\",r,t,e),i=C(\"elementDType\",r,t,e);return[e.getTensorList(n.id).getItem(o,s,i)]}case\"TensorListScatterV2\":case\"TensorListScatter\":{let n=C(\"indices\",r,t,e),o=C(\"tensor\",r,t,e),s=C(\"elementShape\",r,t,e),i=C(\"numElements\",r,t,e),a=BR(o,n,s,i);return e.addTensorList(a),[a.idTensor]}case\"TensorListReserve\":case\"EmptyTensorList\":{let n=C(\"elementShape\",r,t,e),o=C(\"elementDType\",r,t,e),s;r.op===\"TensorListReserve\"?s=\"numElements\":s=\"maxNumElements\";let i=C(s,r,t,e),a=r.op===\"TensorListReserve\"?-1:i,u=zR(n,o,i,a);return e.addTensorList(u),[u.idTensor]}case\"TensorListGather\":{let n=C(\"tensorListId\",r,t,e),o=C(\"indices\",r,t,e),s=C(\"elementShape\",r,t,e),i=C(\"elementDType\",r,t,e);return[e.getTensorList(n.id).gather(o,i,s)]}case\"TensorListStack\":{let n=C(\"tensorListId\",r,t,e),o=C(\"elementShape\",r,t,e),s=C(\"elementDType\",r,t,e),i=C(\"numElements\",r,t,e);return[e.getTensorList(n.id).stack(o,s,i)]}case\"TensorListFromTensor\":{let n=C(\"tensor\",r,t,e),o=C(\"elementShape\",r,t,e),s=C(\"elementDType\",r,t,e),i=LR(n,o,s);return e.addTensorList(i),[i.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let n=C(\"tensorListId\",r,t,e),o=e.getTensorList(n.id),s=C(\"dtype\",r,t,e),i=C(\"elementShape\",r,t,e);return[o.concat(s,i)]}case\"TensorListPushBack\":{let n=C(\"tensorListId\",r,t,e),o=C(\"tensor\",r,t,e),s=e.getTensorList(n.id);return s.pushBack(o),[s.idTensor]}case\"TensorListPopBack\":{let n=C(\"tensorListId\",r,t,e),o=C(\"elementShape\",r,t,e),s=C(\"elementDType\",r,t,e);return[e.getTensorList(n.id).popBack(o,s)]}case\"TensorListSplit\":{let n=C(\"tensor\",r,t,e),o=C(\"elementShape\",r,t,e),s=C(\"lengths\",r,t,e),i=VR(n,s,o);return e.addTensorList(i),[i.idTensor]}case\"TensorListLength\":{let n=C(\"tensorListId\",r,t,e),o=e.getTensorList(n.id);return[ft(o.size(),\"int32\")]}case\"TensorListResize\":{let n=C(\"tensorListId\",r,t,e),o=C(\"size\",r,t,e),i=e.getTensorList(n.id).resize(o);return e.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function WR(r,t,e){let[n,o]=C(\"fusedOps\",r,t,e),s=n===\"biasadd\",i=!s,a=o===\"prelu\",u=n===\"fusedbatchnorm\",l=C(\"numArgs\",r,t,e);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&&s&&l!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(u)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=C(\"strides\",r,t,e),p=Vh(r,t,e),m=C(\"dataFormat\",r,t,e).toUpperCase(),f=C(\"dilations\",r,t,e),[d,h]=C(\"args\",r,t,e);i&&(h=d,d=void 0);let g=C(\"leakyreluAlpha\",r,t,e);return{stride:c,pad:p,dataFormat:m,dilations:f,biasArg:d,preluArg:h,activationFunc:o,leakyreluAlpha:g}}var UR=(r,t,e,n=se)=>{switch(r.op){case\"Conv1D\":{let o=C(\"stride\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"dataFormat\",r,t,e).toUpperCase(),a=C(\"dilation\",r,t,e);return[n.conv1d(C(\"x\",r,t,e),C(\"filter\",r,t,e),o,s,i,a)]}case\"Conv2D\":{let o=C(\"strides\",r,t,e),s=Vh(r,t,e),i=C(\"dataFormat\",r,t,e).toUpperCase(),a=C(\"dilations\",r,t,e);return[n.conv2d(C(\"x\",r,t,e),C(\"filter\",r,t,e),[o[1],o[2]],s,i,[a[1],a[2]])]}case\"_FusedConv2D\":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=WR(r,t,e);return[n.fused.conv2d({x:C(\"x\",r,t,e),filter:C(\"filter\",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case\"FusedDepthwiseConv2dNative\":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=WR(r,t,e);return[n.fused.depthwiseConv2d({x:C(\"x\",r,t,e),filter:C(\"filter\",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let o=C(\"outputShape\",r,t,e),s=C(\"strides\",r,t,e),i=Vh(r,t,e);return[n.conv2dTranspose(C(\"x\",r,t,e),C(\"filter\",r,t,e),o,[s[1],s[2]],i)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let o=C(\"strides\",r,t,e),s=Vh(r,t,e),i=C(\"dilations\",r,t,e),a=C(\"dataFormat\",r,t,e).toUpperCase();return[n.depthwiseConv2d(C(\"input\",r,t,e),C(\"filter\",r,t,e),[o[1],o[2]],s,a,[i[1],i[2]])]}case\"Conv3D\":{let o=C(\"strides\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"dataFormat\",r,t,e).toUpperCase(),a=C(\"dilations\",r,t,e);return[n.conv3d(C(\"x\",r,t,e),C(\"filter\",r,t,e),[o[1],o[2],o[3]],s,i,[a[1],a[2],a[3]])]}case\"AvgPool\":{let o=C(\"strides\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"kernelSize\",r,t,e);return[n.avgPool(C(\"x\",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case\"MaxPool\":{let o=C(\"strides\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"kernelSize\",r,t,e);return[n.maxPool(C(\"x\",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case\"MaxPoolWithArgmax\":{let o=C(\"strides\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"kernelSize\",r,t,e),a=C(\"includeBatchInIndex\",r,t,e),{result:u,indexes:l}=n.maxPoolWithArgmax(C(\"x\",r,t,e),[i[1],i[2]],[o[1],o[2]],s,a);return[u,l]}case\"AvgPool3D\":{let o=C(\"strides\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"kernelSize\",r,t,e);return[n.avgPool3d(C(\"x\",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case\"MaxPool3D\":{let o=C(\"strides\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"kernelSize\",r,t,e);return[n.maxPool3d(C(\"x\",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case\"Dilation2D\":{let o=C(\"strides\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"dilations\",r,t,e),a=o[1],u=o[2],l=i[1],c=i[2];return[n.dilation2d(C(\"x\",r,t,e),C(\"filter\",r,t,e),[a,u],s,[l,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HR=(r,t,e,n=se)=>{switch(r.op){case\"Fill\":{let o=C(\"shape\",r,t,e),s=C(\"dtype\",r,t,e),i=C(\"value\",r,t,e);return[n.fill(o,i,s)]}case\"LinSpace\":{let o=C(\"start\",r,t,e),s=C(\"stop\",r,t,e),i=C(\"num\",r,t,e);return[n.linspace(o,s,i)]}case\"Multinomial\":{let o=C(\"logits\",r,t,e),s=C(\"numSamples\",r,t,e),i=C(\"seed\",r,t,e);return[n.multinomial(o,s,i)]}case\"OneHot\":{let o=C(\"indices\",r,t,e),s=C(\"depth\",r,t,e),i=C(\"onValue\",r,t,e),a=C(\"offValue\",r,t,e),u=C(\"dtype\",r,t,e);return[n.oneHot(o,s,i,a,u)]}case\"Ones\":return[n.ones(C(\"shape\",r,t,e),C(\"dtype\",r,t,e))];case\"OnesLike\":return[n.onesLike(C(\"x\",r,t,e))];case\"RandomStandardNormal\":return[n.randomStandardNormal(C(\"shape\",r,t,e),C(\"dtype\",r,t,e),C(\"seed\",r,t,e))];case\"RandomUniform\":return[n.randomUniform(C(\"shape\",r,t,e),C(\"minval\",r,t,e),C(\"maxval\",r,t,e),C(\"dtype\",r,t,e))];case\"Range\":{let o=C(\"start\",r,t,e),s=C(\"stop\",r,t,e),i=C(\"step\",r,t,e);return[n.range(o,s,i,C(\"dtype\",r,t,e))]}case\"TruncatedNormal\":{let o=C(\"shape\",r,t,e),s=C(\"mean\",r,t,e),i=C(\"stdDev\",r,t,e),a=C(\"seed\",r,t,e);return[n.truncatedNormal(o,s,i,C(\"dtype\",r,t,e),a)]}case\"Zeros\":return[n.zeros(C(\"shape\",r,t,e),C(\"dtype\",r,t,e))];case\"ZerosLike\":return[n.zerosLike(C(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function ck(r,t,e){let n=C(\"boxes\",r,t,e),o=C(\"scores\",r,t,e),s=C(\"maxOutputSize\",r,t,e),i=C(\"iouThreshold\",r,t,e),a=C(\"scoreThreshold\",r,t,e),u=C(\"softNmsSigma\",r,t,e);return{boxes:n,scores:o,maxOutputSize:s,iouThreshold:i,scoreThreshold:a,softNmsSigma:u}}var qR=async(r,t,e,n,o=se)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l,softNmsSigma:c}=ck(r,t,e),p=await o.image.nonMaxSuppressionWithScoreAsync(s,i,a,u,l,c);return[p.selectedIndices,p.selectedScores]}case\"NonMaxSuppressionV4\":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l}=ck(r,t,e),c=C(\"padToMaxOutputSize\",r,t,e),p=await o.image.nonMaxSuppressionPaddedAsync(s,i,a,u,l,c);return[p.selectedIndices,p.validOutputs]}case\"NonMaxSuppressionV3\":case\"NonMaxSuppressionV2\":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l}=ck(r,t,e);return[await o.image.nonMaxSuppressionAsync(s,i,a,u,l)]}case\"Where\":{let s=o.cast(C(\"condition\",r,t,e),\"bool\"),i=[await o.whereAsync(s)];return s.dispose(),i}case\"ListDiff\":return o.setdiff1dAsync(C(\"x\",r,t,e),C(\"y\",r,t,e));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KR=(r,t,e,n=se)=>{switch(r.op){case\"LowerBound\":{let o=C(\"sortedSequence\",r,t,e),s=C(\"values\",r,t,e);return[n.lowerBound(o,s)]}case\"TopKV2\":{let o=C(\"x\",r,t,e),s=C(\"k\",r,t,e),i=C(\"sorted\",r,t,e),a=n.topk(o,s,i);return[a.values,a.indices]}case\"UpperBound\":{let o=C(\"sortedSequence\",r,t,e),s=C(\"values\",r,t,e);return[n.upperBound(o,s)]}case\"Unique\":{let o=C(\"x\",r,t,e),s=n.unique(o);return[s.values,s.indices]}case\"UniqueV2\":{let o=C(\"x\",r,t,e),s=C(\"axis\",r,t,e),i=n.unique(o,s);return[i.values,i.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var jR=(r,t,e,n=se)=>{switch(r.op){case\"Const\":return t[r.name];case\"PlaceholderWithDefault\":let o=C(\"default\",r,t,e);return[cr(r.name,t,e)||o];case\"Placeholder\":return[cr(r.name,t,e)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=C(\"x\",r,t,e);return[xi(c)]}case\"IdentityN\":return C(\"x\",r,t,e).map(c=>xi(c));case\"Snapshot\":let s=C(\"x\",r,t,e);return[xi(s)];case\"Shape\":return[n.tensor1d(C(\"x\",r,t,e).shape,\"int32\")];case\"ShapeN\":return C(\"x\",r,t,e).map(c=>n.tensor1d(c.shape));case\"Size\":return[n.scalar(C(\"x\",r,t,e).size,\"int32\")];case\"Rank\":return[n.scalar(C(\"x\",r,t,e).rank,\"int32\")];case\"NoOp\":return[n.scalar(1)];case\"Print\":let i=C(\"x\",r,t,e),a=C(\"data\",r,t,e),u=C(\"message\",r,t,e),l=C(\"summarize\",r,t,e);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(u);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ft(this.size(),\"int32\")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),B(()=>{let o=xr(e),s=n.length,i=o.length;y.assert(s===i,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${i} elements.`);for(let a=0;a{let o=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let o=n.getHashTableHandleByName(r.name);if(o!=null)return[o];{let s=C(\"keyDType\",r,t,e),i=C(\"valueDType\",r,t,e),a=new jb(s,i);return n.addHashTable(r.name,a),[a.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let o=C(\"tableHandle\",r,t,e,n),s=C(\"keys\",r,t,e),i=C(\"values\",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let o=C(\"tableHandle\",r,t,e,n),s=C(\"keys\",r,t,e),i=C(\"defaultValue\",r,t,e);return[await n.getHashTableById(o.id).find(s,i)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let o=C(\"tableHandle\",r,t,e,n);return[n.getHashTableById(o.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YR=(r,t,e,n=se)=>{switch(r.op){case\"ResizeBilinear\":{let o=C(\"images\",r,t,e),s=C(\"size\",r,t,e),i=C(\"alignCorners\",r,t,e),a=C(\"halfPixelCenters\",r,t,e);return[n.image.resizeBilinear(o,[s[0],s[1]],i,a)]}case\"ResizeNearestNeighbor\":{let o=C(\"images\",r,t,e),s=C(\"size\",r,t,e),i=C(\"alignCorners\",r,t,e),a=C(\"halfPixelCenters\",r,t,e);return[n.image.resizeNearestNeighbor(o,[s[0],s[1]],i,a)]}case\"CropAndResize\":{let o=C(\"image\",r,t,e),s=C(\"boxes\",r,t,e),i=C(\"boxInd\",r,t,e),a=C(\"cropSize\",r,t,e),u=C(\"method\",r,t,e),l=C(\"extrapolationValue\",r,t,e);return[n.image.cropAndResize(o,s,i,a,u,l)]}case\"ImageProjectiveTransformV3\":{let o=C(\"images\",r,t,e),s=C(\"transforms\",r,t,e),i=C(\"outputShape\",r,t,e),a=C(\"fillValue\",r,t,e),u=C(\"interpolation\",r,t,e),l=C(\"fillMode\",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZR=(r,t,e,n=se)=>{switch(r.op){case\"Equal\":return[n.equal(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"NotEqual\":return[n.notEqual(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"Greater\":return[n.greater(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"GreaterEqual\":return[n.greaterEqual(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"Less\":return[n.less(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"LessEqual\":return[n.lessEqual(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"LogicalAnd\":return[n.logicalAnd(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"LogicalNot\":return[n.logicalNot(C(\"a\",r,t,e))];case\"LogicalOr\":return[n.logicalOr(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"Select\":case\"SelectV2\":return[n.where(C(\"condition\",r,t,e),C(\"a\",r,t,e),C(\"b\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var JR=(r,t,e,n=se)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[n.matMul(C(\"a\",r,t,e),C(\"b\",r,t,e),C(\"transposeA\",r,t,e),C(\"transposeB\",r,t,e))];case\"Einsum\":return[n.einsum(C(\"equation\",r,t,e),...C(\"tensors\",r,t,e))];case\"Transpose\":return[n.transpose(C(\"x\",r,t,e),C(\"perm\",r,t,e))];case\"_FusedMatMul\":let[o,s]=C(\"fusedOps\",r,t,e),i=o===\"biasadd\",a=s===\"prelu\",u=C(\"numArgs\",r,t,e),l=C(\"leakyreluAlpha\",r,t,e);if(i){if(a&&u!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!a&&u!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,p]=C(\"args\",r,t,e);return[n.fused.matMul({a:C(\"a\",r,t,e),b:C(\"b\",r,t,e),transposeA:C(\"transposeA\",r,t,e),transposeB:C(\"transposeB\",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case\"MatrixBandPart\":return[n.linalg.bandPart(C(\"a\",r,t,e),C(\"numLower\",r,t,e),C(\"numUpper\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QR=(r,t,e,n=se)=>{switch(r.op){case\"EuclideanNorm\":return[n.euclideanNorm(C(\"x\",r,t,e),C(\"axis\",r,t,e),C(\"keepDims\",r,t,e))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[n.batchNorm(C(\"x\",r,t,e),C(\"mean\",r,t,e),C(\"variance\",r,t,e),C(\"offset\",r,t,e),C(\"scale\",r,t,e),C(\"epsilon\",r,t,e))];case\"FusedBatchNormV3\":return[n.batchNorm(C(\"x\",r,t,e),C(\"mean\",r,t,e),C(\"variance\",r,t,e),C(\"offset\",r,t,e),C(\"scale\",r,t,e),C(\"epsilon\",r,t,e))];case\"LRN\":return[n.localResponseNormalization(C(\"x\",r,t,e),C(\"radius\",r,t,e),C(\"bias\",r,t,e),C(\"alpha\",r,t,e),C(\"beta\",r,t,e))];case\"Softmax\":return[n.softmax(C(\"x\",r,t,e))];case\"LogSoftmax\":return[n.logSoftmax(C(\"x\",r,t,e))];case\"SparseToDense\":return[n.sparseToDense(C(\"sparseIndices\",r,t,e),C(\"outputShape\",r,t,e),C(\"sparseValues\",r,t,e),C(\"defaultValue\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var tF=(r,t,e,n=se)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(C(\"paramsNestedSplits\",r,t,e),C(\"paramsDenseValues\",r,t,e),C(\"indices\",r,t,e),C(\"outputRaggedRank\",r,t,e));return o.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(C(\"starts\",r,t,e),C(\"limits\",r,t,e),C(\"splits\",r,t,e));return[o,s]}case\"RaggedTensorToTensor\":return[n.raggedTensorToTensor(C(\"shape\",r,t,e),C(\"values\",r,t,e),C(\"defaultValue\",r,t,e),C(\"rowPartitionTensors\",r,t,e),C(\"rowPartitionTypes\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var eF=(r,t,e,n=se)=>{switch(r.op){case\"Max\":{let a=C(\"axis\",r,t,e),u=C(\"keepDims\",r,t,e);return[n.max(C(\"x\",r,t,e),a,u)]}case\"Mean\":{let a=C(\"axis\",r,t,e),u=C(\"keepDims\",r,t,e);return[n.mean(C(\"x\",r,t,e),a,u)]}case\"Min\":{let a=C(\"axis\",r,t,e),u=C(\"keepDims\",r,t,e);return[n.min(C(\"x\",r,t,e),a,u)]}case\"Sum\":{let a=C(\"axis\",r,t,e),u=C(\"keepDims\",r,t,e);return[n.sum(C(\"x\",r,t,e),a,u)]}case\"All\":{let a=C(\"axis\",r,t,e),u=C(\"keepDims\",r,t,e);return[n.all(C(\"x\",r,t,e),a,u)]}case\"Any\":{let a=C(\"axis\",r,t,e),u=C(\"keepDims\",r,t,e);return[n.any(C(\"x\",r,t,e),a,u)]}case\"ArgMax\":{let a=C(\"axis\",r,t,e);return[n.argMax(C(\"x\",r,t,e),a)]}case\"ArgMin\":{let a=C(\"axis\",r,t,e);return[n.argMin(C(\"x\",r,t,e),a)]}case\"Prod\":{let a=C(\"axis\",r,t,e),u=C(\"keepDims\",r,t,e);return[n.prod(C(\"x\",r,t,e),a,u)]}case\"Cumprod\":{let a=C(\"axis\",r,t,e),u=C(\"exclusive\",r,t,e),l=C(\"reverse\",r,t,e);return[n.cumprod(C(\"x\",r,t,e),a,u,l)]}case\"Cumsum\":{let a=C(\"axis\",r,t,e),u=C(\"exclusive\",r,t,e),l=C(\"reverse\",r,t,e);return[n.cumsum(C(\"x\",r,t,e),a,u,l)]}case\"Bincount\":let o=C(\"x\",r,t,e),s=C(\"weights\",r,t,e),i=C(\"size\",r,t,e);return[n.bincount(o,s,i)];case\"DenseBincount\":{let a=C(\"x\",r,t,e),u=C(\"weights\",r,t,e),l=C(\"size\",r,t,e),c=C(\"binaryOutput\",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var rF=(r,t,e,n=se)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let o=C(\"n\",r,t,e),s=C(\"axis\",r,t,e),i=C(\"tensors\",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case\"Gather\":{let o=C(\"x\",r,t,e),s=C(\"indices\",r,t,e);return[n.gather(o,n.cast(s,\"int32\"),0)]}case\"GatherV2\":{let o=C(\"axis\",r,t,e),s=C(\"batchDims\",r,t,e),i=C(\"x\",r,t,e),a=C(\"indices\",r,t,e);return[n.gather(i,n.cast(a,\"int32\"),o,s)]}case\"Reverse\":{let o=C(\"dims\",r,t,e),s=[];for(let a=0;a{let o=C(\"axis\",r,t,e),s=C(\"tensors\",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error(\"the input tensors shape does not match\");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case\"Unpack\":{let o=C(\"axis\",r,t,e),s=C(\"tensor\",r,t,e);return n.unstack(s,o)}case\"Tile\":{let o=C(\"reps\",r,t,e);return[n.tile(C(\"x\",r,t,e),o)]}case\"Split\":case\"SplitV\":{let o=C(\"axis\",r,t,e),s=C(\"numOrSizeSplits\",r,t,e),i=C(\"x\",r,t,e);return n.split(i,s,o)}case\"ScatterNd\":{let o=C(\"indices\",r,t,e),s=C(\"values\",r,t,e),i=C(\"shape\",r,t,e);return[n.scatterND(o,s,i)]}case\"GatherNd\":{let o=C(\"x\",r,t,e),s=C(\"indices\",r,t,e);return[n.gatherND(o,s)]}case\"SparseToDense\":{let o=C(\"sparseIndices\",r,t,e),s=C(\"outputShape\",r,t,e),i=C(\"sparseValues\",r,t,e),a=C(\"defaultValue\",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case\"TensorScatterUpdate\":{let o=C(\"indices\",r,t,e),s=C(\"values\",r,t,e),i=C(\"tensor\",r,t,e);return[n.tensorScatterUpdate(i,o,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var nF=(r,t,e,n=se)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(C(\"indices\",r,t,e),C(\"values\",r,t,e),C(\"denseShape\",r,t,e),C(\"defaultValue\",r,t,e));return[o,s,i,a]}case\"SparseReshape\":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(C(\"inputIndices\",r,t,e),C(\"inputShape\",r,t,e),C(\"newShape\",r,t,e));return[o,s]}case\"SparseSegmentMean\":return[n.sparse.sparseSegmentMean(C(\"data\",r,t,e),C(\"indices\",r,t,e),C(\"segmentIds\",r,t,e))];case\"SparseSegmentSum\":return[n.sparse.sparseSegmentSum(C(\"data\",r,t,e),C(\"indices\",r,t,e),C(\"segmentIds\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var oF=(r,t,e,n=se)=>{switch(r.op){case\"FFT\":return[n.fft(C(\"x\",r,t,e))];case\"IFFT\":return[n.ifft(C(\"x\",r,t,e))];case\"RFFT\":return[n.rfft(C(\"x\",r,t,e))];case\"IRFFT\":return[n.irfft(C(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var sF=(r,t,e,n=se)=>{switch(r.op){case\"StaticRegexReplace\":return[n.string.staticRegexReplace(C(\"input\",r,t,e),C(\"pattern\",r,t,e),C(\"rewrite\",r,t,e),C(\"replaceGlobal\",r,t,e))];case\"StringNGrams\":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(C(\"data\",r,t,e),C(\"dataSplits\",r,t,e),C(\"separator\",r,t,e),C(\"nGramWidths\",r,t,e),C(\"leftPad\",r,t,e),C(\"rightPad\",r,t,e),C(\"padWidth\",r,t,e),C(\"preserveShortSequences\",r,t,e));return[o,s]}case\"StringSplit\":{let{indices:o,values:s,shape:i}=n.string.stringSplit(C(\"input\",r,t,e),C(\"delimiter\",r,t,e),C(\"skipEmpty\",r,t,e));return[o,s,i]}case\"StringToHashBucketFast\":return[n.string.stringToHashBucketFast(C(\"input\",r,t,e),C(\"numBuckets\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var iF=(r,t,e,n=se)=>{switch(r.op){case\"Cast\":return[n.cast(C(\"x\",r,t,e),C(\"dtype\",r,t,e))];case\"ExpandDims\":{let o=C(\"axis\",r,t,e);return[n.expandDims(C(\"x\",r,t,e),o)]}case\"Squeeze\":{let o=C(\"axis\",r,t,e);return[n.squeeze(C(\"x\",r,t,e),o)]}case\"Reshape\":return[n.reshape(C(\"x\",r,t,e),C(\"shape\",r,t,e))];case\"MirrorPad\":return[n.mirrorPad(C(\"x\",r,t,e),C(\"padding\",r,t,e),C(\"mode\",r,t,e))];case\"PadV2\":case\"Pad\":return[n.pad(C(\"x\",r,t,e),C(\"padding\",r,t,e),C(\"constantValue\",r,t,e))];case\"SpaceToBatchND\":{let o=C(\"blockShape\",r,t,e),s=C(\"paddings\",r,t,e);return[n.spaceToBatchND(C(\"x\",r,t,e),o,s)]}case\"BatchToSpaceND\":{let o=C(\"blockShape\",r,t,e),s=C(\"crops\",r,t,e);return[n.batchToSpaceND(C(\"x\",r,t,e),o,s)]}case\"DepthToSpace\":{let o=C(\"blockSize\",r,t,e),s=C(\"dataFormat\",r,t,e).toUpperCase();return[n.depthToSpace(C(\"x\",r,t,e),o,s)]}case\"BroadcastTo\":return[n.broadcastTo(C(\"x\",r,t,e),C(\"shape\",r,t,e))];case\"BroadcastArgs\":return[n.broadcastArgs(C(\"s0\",r,t,e),C(\"s1\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function pk(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case\"arithmetic\":return o(()=>OR(i,a,u));case\"basic_math\":return o(()=>PR(i,a,u));case\"control\":return GR(i,a,u);case\"convolution\":return o(()=>UR(i,a,u));case\"creation\":return o(()=>HR(i,a,u));case\"dynamic\":return qR(i,a,u);case\"evaluation\":return o(()=>KR(i,a,u));case\"image\":return o(()=>YR(i,a,u));case\"graph\":return o(()=>jR(i,a,u));case\"logical\":return o(()=>ZR(i,a,u));case\"matrices\":return o(()=>JR(i,a,u));case\"normalization\":return o(()=>QR(i,a,u));case\"ragged\":return o(()=>tF(i,a,u));case\"reduction\":return o(()=>eF(i,a,u));case\"slice_join\":return o(()=>rF(i,a,u));case\"sparse\":return o(()=>nF(i,a,u));case\"spectral\":return o(()=>oF(i,a,u));case\"string\":return o(()=>sF(i,a,u));case\"transformation\":return o(()=>iF(i,a,u));case\"hash_table\":return XR(i,a,u,n);case\"custom\":let l=Rb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Hb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Wh=class{constructor(t={},e={},n={},o={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?\"\":`${e.frameName}-${e.iterationId}`).join(\"/\"):\"\"}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),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 t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function mk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>wn(m)[0]));n=n||[];let c=new Set(n.map(m=>wn(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Jc(m)||o9(m)||s9(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&!l.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function aF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>wn(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],i=g=>e.has(typeof g==\"string\"?g:g.name);function a(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let u=a([...o,...r.weights,...s]).filter(i),l=a([...u,...Object.values(r.nodes)]).filter(i),c=new Map(l.map(g=>[g.name,g])),p={};for(let g of l){p[g.name]=p[g.name]||0;for(let x of g.children)i(x)||(p[x.name]=Number.POSITIVE_INFINITY),p[x.name]=(p[x.name]||0)+1}let m=Object.entries(p).filter(([,g])=>g===0).map(([g])=>g),f=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(i))--p[b.name]===0&&(f.push(b.name),m.push(b.name))}let d=f.map(g=>c.get(g)),h=QQ(d,u);return t9(h,u),h}function QQ(r,t){let e=new Map(r.map(i=>[i.name,i])),n=t.map(i=>i.name),o=new Set(n);for(;n.length>0;){let i=n.pop(),a=e.get(i);for(let u of a.children)!e.has(u.name)||o.has(u.name)||(o.add(u.name),n.push(u.name))}return r.filter(i=>o.has(i.name))}var sd=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function t9(r,t){let e=new Map(r.map((a,u)=>[a.name,u])),n=new Set(t.map(a=>a.name)),o=a=>n.has(typeof a==\"string\"?a:a.name),s=new Set(r.map(a=>a.name)),i=a=>s.has(typeof a==\"string\"?a:a.name);for(let a of r){for(let u of a.children.filter(i)){if(!e.has(u.name))throw new sd(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new sd(`Node ${a.name} is scheduled to run after its child ${u.name}.`)}if(!o(a))for(let u of a.inputs){if(!e.has(u.name))throw new sd(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new sd(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function lF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Jc(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;at[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new Qc(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+\"--\"+o.join(this.SEPARATOR)}compile(t,e){let n=mk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let l=e.map(p=>p.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${c}]. Missing the following inputs: [${o}]`)}let a=aF(this.graph,n),u=lF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return $e(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[wn(m)[0]]),s=e.map(m=>wn(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Wh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=wn(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=pk(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. 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c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=gi(a.name,n);s[u]||!i.has(a.name)||(a.op===\"Merge\"?a.inputNames.some(l=>!!cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=wn(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let 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s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=n,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let o=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Qc(Gh.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Gh.Instance.transformGraph(t.modelInitializer);this.initializer=new Qc(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t==\"string\"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than 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next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}},xk=class extends tr{constructor(t){super(),this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},yk=class extends tr{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},wk=class extends tr{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},Ik=class extends tr{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},Ck=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},vk=class extends tr{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},Jb=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=await this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},ep=class extends tr{constructor(){super(),this.outputQueue=new tp,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}}},Sk=class extends ep{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return!0}},Qb=class extends tr{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return\"TODO: fill in upstream of chained summaries -> Chained\"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(t){if(await t,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 e=await this.iterator.next();return e.done?(this.iterator=null,this.readFromChain(t)):e}},Pl;(function(r){r[r.FAIL=0]=\"FAIL\",r[r.SHORTEST=1]=\"SHORTEST\",r[r.LONGEST=2]=\"LONGEST\"})(Pl||(Pl={}));var Nk=class extends tr{constructor(t,e=Pl.FAIL){super(),this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){return\"{TODO: fill in upstream of zip summaries} -> Zip\"}async nextState(t){await t;let e=0,n=0;function o(i){return i instanceof tr?{value:i.next().then(u=>(e++,u.done&&n++,u.value)),recurse:!1}:{value:null,recurse:!0}}let s=await Zb(this.iterators,o);if(e===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Pl.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Pl.SHORTEST:return{value:null,done:!0};case Pl.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},tw=class extends tr{constructor(t,e){super(),this.upstream=t,this.bufferSize=e,this.buffer=new id(e)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let t=this.upstream.next();this.buffer.push(t)}}next(){return this.refill(),this.buffer.shift()}},kk=class extends tw{constructor(t,e,n){super(t,e),this.upstream=t,this.windowSize=e,this.upstreamExhausted=!1,this.random=hF.alea(n||y.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(t){return Math.floor(this.random()*t)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let t=this.chooseIndex(),e=await this.buffer.shuffleExcise(t);if(e.done)this.upstreamExhausted=!0;else return this.refill(),e}return{value:null,done:!0}}};var yi=class{constructor(){this.size=null}batch(t,e=!0){let n=this;y.assert(t>0,()=>`batchSize needs to be positive, but it is\n ${t}`);let o;return this.size===1/0||this.size==null?o=this.size:e?o=Math.ceil(this.size/t):o=Math.floor(this.size/t),En(async()=>(await n.iterator()).columnMajorBatch(t,e,f9),o)}concatenate(t){let e=this,n;return this.size===1/0||t.size===1/0?n=1/0:this.size!=null&&t.size!=null?n=this.size+t.size:n=null,En(async()=>(await e.iterator()).concatenate(await t.iterator()),n)}filter(t){let e=this,n;return this.size===1/0?n=1/0:n=null,En(async()=>(await e.iterator()).filter(o=>B(()=>t(o))),n)}async forEachAsync(t){return(await this.iterator()).forEachAsync(t)}map(t){let e=this;return En(async()=>(await e.iterator()).map(n=>B(()=>t(n))),this.size)}mapAsync(t){let e=this;return En(async()=>(await e.iterator()).mapAsync(t),this.size)}prefetch(t){if(t==null)throw new RangeError(\"`Dataset.prefetch()` requires bufferSize to be specified.\");let e=this;return En(async()=>(await e.iterator()).prefetch(t),this.size)}repeat(t){let e=this,n;return this.size!=null&&t>0?n=this.size*t:t===0?n=0:this.size!=null&&(t===void 0||t<0)?n=1/0:n=null,En(async()=>{let o=Hh(async()=>({value:await e.iterator(),done:!1}));return gF(o.take(t))},n)}skip(t){let e=this,n;return this.size!=null&&t>=0&&this.size>=t?n=this.size-t:this.size!=null&&(this.size(await e.iterator()).skip(t),n)}shuffle(t,e,n=!0){if(t==null||t<0)throw this.size==null?new RangeError(\"`Dataset.shuffle()` requires bufferSize to be specified.\"):new RangeError(`\\`Dataset.shuffle()\\` requires bufferSize to be specified. 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At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(t={}){if(!L().get(\"IS_BROWSER\"))throw new Error(\"microphone API is only supported in browser environment.\");let e=new ud(t);return await e.start(),e}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 t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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 e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.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 t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},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(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),or(n,e)}};var cd=class extends tr{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ke([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=li([i,s,u,a],[1,4])}else this.cropBox=li([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(t,e={}){if(!L().get(\"IS_BROWSER\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!t){if(t=document.createElement(\"video\"),!e.resizeWidth||!e.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new cd(t,e);return await n.start(),n}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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error(\"Could not obtain video from webcam.\");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=Sy.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=ir(J(t,\"float32\"),0),n;n=lo.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,\"bilinear\");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error(\"Can not convert infinite video stream to array.\")}};var pd=class{};var Kh=class extends tr{split(t){return new Ek(this,t)}},Ek=class extends Kh{constructor(t,e){super(),this.upstream=t,this.impl=new Ak(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Ak=class extends ep{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=\"\"}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===\"\"?!1:(this.outputQueue.push(this.carryover),this.carryover=\"\",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var nw=class extends tr{decodeUTF8(){return new Dk(this)}},Dk=class extends Kh{constructor(t){super(),this.upstream=t,this.impl=new $k(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},$k=class extends ep{constructor(t){if(super(),this.upstream=t,L().get(\"IS_BROWSER\"))this.decoder=new TextDecoder(\"utf-8\");else{let{StringDecoder:e}=fk();this.decoder=new e(\"utf8\")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let n;return L().get(\"IS_BROWSER\")?n=this.decoder.decode(e,{stream:!0}):n=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(n),!0}};var md=class extends nw{constructor(t,e={}){super(),this.file=t,this.options=e,y.assert(t instanceof Uint8Array||(L().get(\"IS_BROWSER\")?t instanceof File||t instanceof Blob:!1),()=>\"FileChunkIterator only supports File, Blob and Uint8Array right now.\"),this.offset=e.offset||0,this.chunkSize=e.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,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=a=>{let u=s.result;if(u instanceof ArrayBuffer&&(u=new Uint8Array(u)),!(u instanceof Uint8Array))return n(new TypeError(\"FileReader returned unknown type.\"));e(u)},s.onabort=a=>n(new Error(\"Aborted\")),s.onerror=a=>n(new Error(a.type));let i=this.file.slice(this.offset,o);s.readAsArrayBuffer(i)}this.offset=o}),done:!1}}};async function vF(r,t={},e){let n,o;typeof r==\"string\"?n=r:(n=r.url,o=h9(r));let s=await(e||y.fetch)(n,o);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new md(i,t)}else throw new Error(s.statusText)}var h9=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function ow(r){return typeof r==\"string\"&&r.slice(0,7)===\"file://\"}var fd=class extends pd{constructor(t,e={}){super(),this.input=t,this.options=e}async 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c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,I=c.effectiveFilterDepth,N=c.effectiveFilterHeight,E=c.effectiveFilterWidth,A=I-1-c.padInfo.front,D=E-1-c.padInfo.left,F=N-1-c.padInfo.top,P=bt(s.shape,\"float32\"),V=1/(d*h*g),G=e.bufferSync(o);for(let W=0;W=c.outDepth||Math.floor(ot)!==ot))for(let it=0;it=c.outHeight||Math.floor(mt)!==mt))for(let gt=0;gt=c.outWidth||Math.floor(It)!==It)continue;let Rt=G.get(W,ot,mt,It,q);st+=Rt}}}P.set(st*V,W,H,K,X,q)}return e.makeTensorInfo(P.shape,P.dtype,P.values)}var LO={kernelName:Xl,backendName:\"cpu\",kernelFunc:utt};function ctt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;tt([o,s],\"avgPoolGrad\");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,I=x-1-c.padInfo.top,N=bt(i.shape,\"float32\"),E=1/(f*d),A=e.data.get(o.dataId).values,D=bt(o.shape,\"float32\",A);for(let F=0;F=c.outHeight||Math.floor(X)!==X))for(let Z=0;Z=c.outWidth||Math.floor(et)!==et)continue;let nt=D.get(F,X,et,P);H+=nt}}N.set(H*E,F,V,G,P)}return e.makeTensorInfo(N.shape,N.dtype,N.values)}var zO={kernelName:Fp,backendName:\"cpu\",kernelFunc:ctt};function ptt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;y.assert(a.shape.length===u.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(i==null||a.shape.length===i.shape.length,()=>\"Batch 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a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=Jt({inputs:{x:o},backend:e,attrs:{shape:u}}),d=Ge({inputs:{x:f},backend:e,attrs:{perm:l}}),h=Jt({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Mo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var VO={kernelName:Di,backendName:\"cpu\",kernelFunc:mtt};function ftt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=xd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var GO={kernelName:Da,backendName:\"cpu\",kernelFunc:ftt};function dtt(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],\"int32\",Int32Array.from(a))}var WO={kernelName:Yl,backendName:\"cpu\",kernelFunc:dtt};var htt=At(ho,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Yr({inputs:{x:u[0]},backend:e});if(u[0].dtype===\"complex64\"){let h=u.map(I=>Fo({inputs:{input:I},backend:e})),g=u.map(I=>ba({inputs:{input:I},backend:e})),x=Uu({inputs:h,backend:e,attrs:{axis:s}}),b=Uu({inputs:g,backend:e,attrs:{axis:s}}),w=Cr({inputs:{real:x,imag:b},backend:e});return h.forEach(I=>e.disposeIntermediateTensorInfo(I)),g.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Jt({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=rp(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var KO={kernelName:$i,backendName:\"cpu\",kernelFunc:Uu};function fT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],\"conv2d\");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat===\"channelsLast\",I=new le(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],D=w?N[1]:N[2],F=w?N[2]:1,P=w?1:N[1],V=I.strides[0],G=w?I.strides[1]:I.strides[2],W=w?I.strides[2]:1,q=w?1:I.strides[1],H=e.data.get(o.dataId).values,K=e.data.get(s.dataId).values,X=I.values;for(let Z=0;Z=m.inHeight)continue;let gt=it*E[0],It=et+mt*D;for(let Rt=0;Rt=m.inWidth)continue;let ge=gt+qt*E[1],ee=It+ce*F,xe=ge;for(let fe=0;fe=l.inDepth)continue;let Z=K*F[0],et=V+X*D[1];for(let nt=0;nt=l.inHeight)continue;let mt=Z+ot*F[1],gt=et+it*D[2];for(let It=0;It=l.inWidth)continue;let ce=mt+Ht*F[2],ge=gt+qt*l.inChannels,ee=ce;for(let xe=0;xeMath.cos(r)),tP={kernelName:os,backendName:\"cpu\",kernelFunc:Ctt};var vtt=At(ss,r=>Math.cosh(r)),eP={kernelName:ss,backendName:\"cpu\",kernelFunc:vtt};function Stt(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=bt([d,h,g,f],\"float32\"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,I=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-P)*(m-1)/(g-1):0;for(let K=0;K1?F*(p-1)+K*q:.5*(F+V)*(p-1);if(X<0||X>p-1){for(let Z=0;Z1?P*(m-1)+st*H:.5*(P+G)*(m-1);if(at<0||at>m-1){for(let gt=0;gt1?P*(m-1)+Z*H:.5*(P+G)*(m-1);if(et<0||et>m-1){for(let at=0;atx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. 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at=y.locToIndex([H,K,Z,nt],G,y.computeStrides(P));W[at]=st}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),P,n.dtype),shape:P,dtype:n.dtype}}};var mP={kernelName:eu,backendName:\"cpu\",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,\"NHWC\",u);y.assert(s.rank===F.length,()=>`Error in ${eu}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W=0&&ot=0&&mtet&&(et=gt,nt=at,st=it)}}}V[nt][st][Z]+=P[W][q][K][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var fP={kernelName:tu,backendName:\"cpu\",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,\"NHWC\",u);y.assert(s.rank===F.length,()=>`Error in ${tu}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W=0&&ot=0&&mtet&&(et=gt,nt=ot,st=mt)}}}V[W][nt][st][Z]+=P[W][q][K][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function Ml(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,\"sum\");let a;o.dtype===\"bool\"?a=Oo({inputs:{x:o},backend:e,attrs:{dtype:\"int32\"}}):a=Yr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=S.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=Ge({inputs:{x:a},backend:e,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,u)),S.assertAxesAreInnerMostDims(\"sum\",p,m.shape.length);let[f,d]=S.computeOutAndReduceShapes(m.shape,p),h=S.upcastType(m.dtype,\"int32\"),g=hd(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let I=0;I=0&&(m=Ml({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var hP={kernelName:zp,backendName:\"cpu\",kernelFunc:$tt};function Rtt(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],\"eluGrad\");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=0?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,\"float32\",s)}var gP={kernelName:Ma,backendName:\"cpu\",kernelFunc:Rtt};var Ftt=S.ERF_P,Ott=S.ERF_A1,Ptt=S.ERF_A2,Mtt=S.ERF_A3,Ltt=S.ERF_A4,ztt=S.ERF_A5,Btt=At(La,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+Ftt*e);return t*(1-((((ztt*n+Ltt)*n+Mtt)*n+Ptt)*n+Ott)*n*Math.exp(-e*e))}),xP={kernelName:La,backendName:\"cpu\",kernelFunc:Btt};function Cd(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),Jt({inputs:{x:o},backend:e,attrs:{shape:a}})}var yP={kernelName:Ri,backendName:\"cpu\",kernelFunc:Cd};var Vtt=te((r,t)=>r/t),Zh=ie(us,Vtt),Jh={kernelName:us,backendName:\"cpu\",kernelFunc:Zh};function Cw(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType(\"float32\",c),m=y.getTypedArrayFromDType(\"float32\",c);for(let g=0;g{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m=0&&w=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=S.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=Jt({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=Jt({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=lw(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var NP={kernelName:Fi,backendName:\"cpu\",kernelFunc:Ytt};function Ztt(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=Jt({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=Cw(a,!0,e),l=Jt({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var kP={kernelName:Vp,backendName:\"cpu\",kernelFunc:Ztt};var Jtt=At(xs,r=>Number.isFinite(r)?1:0,\"bool\"),TP={kernelName:xs,backendName:\"cpu\",kernelFunc:Jtt};var Qtt=At(ys,r=>Math.abs(r)===1/0?1:0,\"bool\"),_P={kernelName:ys,backendName:\"cpu\",kernelFunc:Qtt};var tet=At(bs,r=>Number.isNaN(r)?1:0,\"bool\"),EP={kernelName:bs,backendName:\"cpu\",kernelFunc:tet};function eet(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=uw(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var AP={kernelName:Ha,backendName:\"cpu\",kernelFunc:eet};var ret=At(Cs,r=>Math.log1p(r)),DP={kernelName:Cs,backendName:\"cpu\",kernelFunc:ret};var net=te((r,t)=>r&&t),oet=ie(qa,net,null,\"bool\"),$P={kernelName:qa,backendName:\"cpu\",kernelFunc:oet};var set=At(Ka,r=>r?0:1,\"bool\"),RP={kernelName:Ka,backendName:\"cpu\",kernelFunc:set};var iet=te((r,t)=>r||t),aet=ie(ja,iet,null,\"bool\"),FP={kernelName:ja,backendName:\"cpu\",kernelFunc:aet};function uet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;tt(o,\"LRN\");let l=o.shape[3],c=l-1,p=e.data.get(o.dataId).values,m=y.sizeFromShape(o.shape),f=new Float32Array(m);function d(h){let g=h%l,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),w=0;for(;x<=b;x++){let I=p[x];w+=I*I}return w}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. 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_t=L();_t.registerFlag(\"HAS_WEBGL\",()=>_t.getNumber(\"WEBGL_VERSION\")>0);_t.registerFlag(\"WEBGL_VERSION\",()=>Dw(2)?2:Dw(1)?1:0);_t.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\",()=>!1);_t.registerFlag(\"WEBGL_BUFFER_SUPPORTED\",()=>_t.get(\"WEBGL_VERSION\")===2);_t.registerFlag(\"WEBGL_CPU_FORWARD\",()=>!0);_t.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\",()=>!1);_t.registerFlag(\"WEBGL_PACK\",()=>_t.getBool(\"HAS_WEBGL\"));_t.registerFlag(\"WEBGL_PACK_NORMALIZATION\",()=>_t.getBool(\"WEBGL_PACK\"));_t.registerFlag(\"WEBGL_PACK_CLIP\",()=>_t.getBool(\"WEBGL_PACK\"));_t.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\",()=>_t.getBool(\"WEBGL_PACK\"));_t.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\",()=>_t.getBool(\"WEBGL_PACK\"));_t.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\",()=>_t.getBool(\"WEBGL_PACK\"));_t.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\",()=>_t.getBool(\"WEBGL_PACK\"));_t.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\",()=>_t.getBool(\"WEBGL_PACK\"));_t.registerFlag(\"WEBGL_PACK_REDUCE\",()=>_t.getBool(\"WEBGL_PACK\"));_t.registerFlag(\"WEBGL_LAZILY_UNPACK\",()=>_t.getBool(\"WEBGL_PACK\"));_t.registerFlag(\"WEBGL_CONV_IM2COL\",()=>_t.getBool(\"WEBGL_PACK\"));_t.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\",()=>PT(_t.getNumber(\"WEBGL_VERSION\")));_t.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\",()=>MT(_t.getNumber(\"WEBGL_VERSION\")));_t.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",()=>{let r=_t.getNumber(\"WEBGL_VERSION\");return r===0?0:LT(r)});_t.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",()=>_t.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!xu.isMobile());_t.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\",()=>zT(_t.getNumber(\"WEBGL_VERSION\")));_t.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\",()=>_t.getBool(\"WEBGL_FORCE_F16_TEXTURES\")?!1:_t.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\"));_t.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",()=>BT(_t.getNumber(\"WEBGL_VERSION\")));_t.registerFlag(\"WEBGL_FENCE_API_ENABLED\",()=>VT(_t.getNumber(\"WEBGL_VERSION\")));_t.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\",()=>_t.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0);_t.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});_t.registerFlag(\"WEBGL_FLUSH_THRESHOLD\",()=>xu.isMobile()?1:-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});_t.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\",()=>128);_t.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\",()=>!1);_t.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e5);_t.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\",()=>128);_t.registerFlag(\"WEBGL_EXP_CONV\",()=>!1);_t.registerFlag(\"SOFTWARE_WEBGL_ENABLED\",()=>_t.getBool(\"IS_TEST\"));_t.registerFlag(\"WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE\",()=>1/0);_t.registerFlag(\"WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE\",()=>!1);_t.registerFlag(\"WEBGL2_ISNAN_CUSTOM\",()=>!1);_t.registerFlag(\"ENGINE_COMPILE_ONLY\",()=>!1);function We(){let r,t,e,n,o,s,i,a,u,l;return L().getNumber(\"WEBGL_VERSION\")===2?(r=\"#version 300 es\",t=\"in\",e=\"out\",n=\"in\",o=\"texture\",s=\"outputColor\",i=\"out vec4 outputColor;\",a=L().getBool(\"WEBGL2_ISNAN_CUSTOM\")?`\n bool isnan_custom(float val) {\n uint floatToUint = floatBitsToUint(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan_custom(val.x),\n isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n }\n\n #define isnan(value) isnan_custom(value)\n `:\"\",u=\"\",l=`\n #define round(value) newRound(value)\n int newRound(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 newRound(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `):(r=\"\",t=\"attribute\",e=\"varying\",n=\"varying\",o=\"texture2D\",s=\"gl_FragColor\",i=\"\",a=`\n #define isnan(value) isnan_custom(value)\n bool isnan_custom(float val) {\n return (val > 0. || val < 1. || val == 0.) ? false : true;\n }\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n }\n `,u=`\n uniform float INFINITY;\n\n bool isinf(float val) {\n return abs(val) == INFINITY;\n }\n bvec4 isinf(vec4 val) {\n return equal(abs(val), vec4(INFINITY));\n }\n `,l=`\n int round(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 round(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function Ii(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join(\"\")}function fp(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join(\"\")}function Grt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function nL(r,t,e=\"index\"){let n=r.map((s,i)=>i),o=Grt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join(\"\")}function Td(r){let t=y.computeStrides(r).map(e=>e.toString());return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;\n }\n`}function _d(){return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n`}var $w=`\n const float FLOAT_MAX = 1.70141184e38;\n const float FLOAT_MIN = 1.17549435e-38;\n\n lowp vec4 encode_float(highp float v) {\n if (isnan(v)) {\n return vec4(255, 255, 255, 255);\n }\n\n highp float av = abs(v);\n\n if(av < FLOAT_MIN) {\n return vec4(0.0, 0.0, 0.0, 0.0);\n } else if(v > FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n } else if(v < -FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\n }\n\n highp vec4 c = vec4(0,0,0,0);\n\n highp float e = floor(log2(av));\n highp float m = exp2(fract(log2(av))) - 1.0;\n\n c[2] = floor(128.0 * m);\n m -= c[2] / 128.0;\n c[1] = floor(32768.0 * m);\n m -= c[1] / 32768.0;\n c[0] = floor(8388608.0 * m);\n\n highp float ebias = e + 127.0;\n c[3] = floor(ebias / 2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n`;var{getBroadcastDims:oL}=S;function sL(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:\"\"};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=Rw(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push(\"uniform int outShape;\");break;case 2:n.push(\"uniform ivec2 outShape;\"),n.push(\"uniform int outShapeStrides;\");break;case 3:n.push(\"uniform ivec3 outShape;\"),n.push(\"uniform ivec2 outShapeStrides;\");break;case 4:n.push(\"uniform ivec4 outShape;\"),n.push(\"uniform ivec3 outShapeStrides;\");break;default:break}n.push(\"uniform ivec2 outTexShape;\")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:\"\"};`)});let o=n.join(`\n`),s=r.map(f=>Wrt(f,t,e.packedInputs,e.enableShapeUniforms)).join(`\n`),i=t.texShape,a=We(),u=qrt(a),l,c,p=Xrt(a);return t.isPacked?(l=Urt(t.logicalShape,i,e.enableShapeUniforms),c=jrt(a)):(l=Hrt(t.logicalShape,i,e.enableShapeUniforms),c=Krt(a)),e.packedInputs&&(p+=Qrt),[p,u,c,o,l,s,e.userCode].join(`\n`)}function Ad(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return pnt(r,t);case 1:return fnt(r,t);case 2:return hnt(r,t);case 3:return xnt(r,t);case 4:return bnt(r,t);case 5:return wnt(r);case 6:return Int(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function iL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return cnt(r);case 1:return mnt(r,t);case 2:return dnt(r,t);case 3:return gnt(r,t);default:return ynt(r,t)}}function Wrt(r,t,e=!1,n){let o=\"\";e?o+=iL(r,n):o+=Ad(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=Cnt(r,t):o+=vnt(r,t)),o}function Urt(r,t,e){switch(r.length){case 0:return aL();case 1:return tnt(r,t,e);case 2:return lnt(r,t,e);case 3:return rnt(r,t,e);default:return ont(r,t,e)}}function Hrt(r,t,e){switch(r.length){case 0:return aL();case 1:return ent(r,t,e);case 2:return unt(r,t,e);case 3:return nnt(r,t,e);case 4:return snt(r,t,e);case 5:return int(r,t);case 6:return ant(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function qrt(r){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${r.texture2D}(textureSampler, uv).r;\n }\n `}function Krt(r){return`\n void setOutput(float val) {\n ${r.output} = vec4(val, 0, 0, 0);\n }\n `}function jrt(r){return`\n void setOutput(vec4 val) {\n ${r.output} = val;\n }\n `}function Xrt(r){return`${r.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${r.varyingFs} vec2 resultUV;\n ${r.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${r.defineSpecialNaN}\n ${r.defineSpecialInf}\n ${r.defineRound}\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n int idiv(int a, int b, float sign) {\n int res = a / b;\n int mod = imod(a, b);\n if (sign < 0. && mod != 0) {\n res -= 1;\n }\n return res;\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${Yrt}\n ${Zrt}\n ${Jrt}\n `}var Yrt=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n int texelIndex = index / 2;\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,Zrt=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n int texNumC, int row, int col) {\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,Jrt=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n int texelsInBatch, int texelsInLogicalRow, int b,\n int row, int col) {\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,Qrt=`\n float getChannel(vec4 frag, vec2 innerDims) {\n vec2 modCoord = mod(innerDims, 2.);\n return modCoord.x == 0. ?\n (modCoord.y == 0. ? frag.r : frag.g) :\n (modCoord.y == 0. ? frag.b : frag.a);\n }\n float getChannel(vec4 frag, int dim) {\n float modCoord = mod(float(dim), 2.);\n return modCoord == 0. ? frag.r : frag.g;\n }\n`;function aL(){return`\n int getOutputCoords() {\n return 0;\n }\n `}function tnt(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${n[1]}.0);\n }\n `:n[1]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${n[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);\n }\n `}function ent(r,t,e){return t[0]===1?e?`\n int getOutputCoords() {\n return int(resultUV.x * float(outTexShape[1]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.x * ${t[1]}.0);\n }\n `:t[1]===1?e?`\n int getOutputCoords() {\n return int(resultUV.y * float(outTexShape[0]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.y * ${t[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n return resTexRC.x * outTexShape[1] + resTexRC.y;\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n return resTexRC.x * ${t[1]} + resTexRC.y;\n }\n `}function rnt(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec3(b, r, c);\n }\n `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n int index = resTexRC.x * ${n[1]} + resTexRC.y;\n\n int b = index / ${s};\n index -= b * ${s};\n\n int r = 2 * (index / ${o});\n int c = imod(index, ${o}) * 2;\n\n return ivec3(b, r, c);\n }\n `}function nnt(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n ${fp([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n`;let n=Ii([\"r\",\"c\",\"d\"],r);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n int index = resTexRC.x * ${t[1]} + resTexRC.y;\n ${n}\n return ivec3(r, c, d);\n }\n `}function ont(r,t,e){if(e)return`\n ivec4 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatchN = texelsInBatch * outShape[1];\n\n int b2 = index / texelsInBatchN;\n index -= b2 * texelsInBatchN;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec4(b2, b, r, c);\n }\n `;let 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m={name:p,uniform:r.getUniformLocation(e,p,c),offset:r.getUniformLocation(e,`offset${p}`,c)};t.enableShapeUniforms&&(m.shape=r.getUniformLocation(e,`${p}Shape`,c),m.texShape=r.getUniformLocation(e,`${p}TexShape`,c)),n.push(m)}if(t.enableShapeUniforms&&(s=r.getUniformLocation(e,\"outShape\",c),a=r.getUniformLocation(e,\"outShapeStrides\",c),i=r.getUniformLocation(e,\"outTexShape\",c)),t.customUniforms)for(let p of t.customUniforms)o.push(r.getUniformLocation(e,p.name,c));return{variablesLocations:n,customUniformLocations:o,infLoc:u,nanLoc:l,outShapeLocation:s,outShapeStridesLocation:a,outTexShapeLocation:i}}function lL(r,t){if(r.length!==t.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t.length} inputs`);r.forEach((e,n)=>{let o=e.logicalShape,s=t[n],i=s.shape;if(!y.arraysEqual(o,i))throw Error(`Binary was compiled with different shapes than the current args. 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Shape ${a} and ${u} must match`)})}function cL(r,t,e,n,o){t.program.enableShapeUniforms||(lL(t.inShapeInfos,e),lL([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):r.setOutputMatrixTexture(s.texture,i[0],i[1]),r.setProgram(t.webGLProgram),L().getNumber(\"WEBGL_VERSION\")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN);for(let u=0;u{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=Rw(r.packedInputs,i.shape,u),m=\"\",f=\"\",d=\"\";if(c.length===1&&r.packedInputs){let N=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${N[0]>1}_${N[1]>1}`}else if(c.length===2&&!r.packedInputs)f=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let N=y.computeStrides(c);d=`${N[0]===u[1]}_${N[N.length-1]===u[1]}`}let h=i.shape.length,g=c.length===2&&y.arraysEqual(i.shape,u),x=y.sizeFromShape(i.shape)===1,b=S.getBroadcastDims(i.shape,e.shape),w=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(u,e.texData.texShape),I=r.packedInputs||c.length>2?\"\":`${u[0]>1}_${u[1]>1}`;n+=`${h}_${w}_${l?p:\"\"}_${c.length}_${x}_${b}_${g}_${m}_${f}_${d}_${I}_${a}`}else{let u=i.isUniform?\"uniform\":i.texData.texShape;n+=`${i.shape}_${u}_${a}`}});let o=r.userCode,s=r.constructor.name;return s+=\"_\"+n+\"_\"+o+`${L().getNumber(\"WEBGL_VERSION\")}`,s}function de(r){return L().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&r<=4}var Fw=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Hu.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let e=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n 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texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${e.output} = result;\n }\n `}};var Pw=class{constructor(t){this.variableNames=[\"A\"],this.outTexUsage=Zr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=`\n ${$w}\n\n void main() {\n float x = getAAtOutCoords();\n ${e.output} = encode_float(x);\n }\n `}};var Mw=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Zr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=`\n ${$w}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${e.output} = encode_float(x);\n }\n `}};var 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n=\"WEBGL_color_buffer_float\",o=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),L().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",i=\"OES_texture_half_float\";if(this.textureFloatExtension=vd(this.gl,s),Kn(this.gl,i))this.textureHalfFloatExtension=vd(this.gl,i);else if(L().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),Kn(this.gl,o))this.colorBufferHalfFloatExtension=vd(this.gl,o);else if(L().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\",Kn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Kn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=UT(this.gl),this.indexBuffer=HT(this.gl),this.framebuffer=DT(this.gl),this.textureConfig=ng(this.gl,this.textureHalfFloatExtension)}get debug(){return L().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 t=this.gl;ht(t,()=>t.finish()),ht(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ht(t,()=>t.deleteFramebuffer(this.framebuffer)),ht(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ht(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ht(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),qT(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),KT(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),jT(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),QT(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),JT(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),YT(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),XT(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(Aw(this.gl,this.framebuffer),this.outputTexture=null),ht(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,n){return this.downloadMatrixDriver(t,()=>r1(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return n1(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return e1(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=t1(this.gl,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,n;if(L().getBool(\"WEBGL_FENCE_API_ENABLED\")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=()=>{let i=o.clientWaitSync(s,0,0);return i===o.ALREADY_SIGNALED||i===o.CONDITION_SATISFIED},e=s}else L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(e=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(e,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):n=()=>!0;return{query:e,isFencePassed:n}}downloadMatrixFromPackedTexture(t,e,n){return this.downloadMatrixDriver(t,()=>o1(this.gl,e,n))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=WT(e));let n=NT(e);ht(e,()=>e.attachShader(n,this.vertexShader)),ht(e,()=>e.attachShader(n,t)),kT(e,n);let o;return o=Object.assign(n,{vao:this.createVertexArray()}),this.bindVertexArray(o.vao),ht(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),console.assert(ZT(e,o,this.vertexBuffer),\"gpgpu_util.bindVertexProgramAttributeStreams not fully successful.\"),this.debug&&og(e,o),this.setProgram(o),o}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ht(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&og(this.gl,this.program)),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?$T(this.gl,t,e):RT(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ht(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),FT(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=wa(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&og(this.gl,this.program),Sd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}ht(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ht(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=vd(this.gl,L().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(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(t,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(t,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(t,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=Tnt(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let n;\"setTimeoutCustom\"in L().platform&&(n=L().platform.setTimeoutCustom.bind(L().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),sg(this.gl,t,this.framebuffer),this.debug&&Sd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(sg(this.gl,this.outputTexture,this.framebuffer),this.debug&&Sd(this.gl)):Aw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;sg(o,t,this.framebuffer),this.debug&&Sd(o),this.outputTexture=t,ht(o,()=>o.viewport(0,0,e,n)),ht(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),ht(this.gl,()=>this.gl.scissor(t,e,n,o))}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 Tnt(r){let t=0;for(;t`${r}.${e}`)}function er(r,t){return t===1?[r]:i1(r,t)}function rz(r,t){if(r===1)return\"rc\";let e=\"\";for(let n=0;n ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let e=\"\";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n= ${n};\n bool rEdge = rp1 >= ${o};\n `}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}),\n cEdge ? 0. : getA(${e[1]}),\n rEdge ? 0. : getA(${e[2]}),\n rEdge || cEdge ? 0. : getA(${e[3]})`}};var Fd=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let n=\"\";for(let o=0;o<4;o++){let s=\"thisRC = rc;\";o%2===1&&(s+=\"thisRC.z += 1;\"),o>1&&(s+=\"thisRC.y += 1;\"),n+=`\n ${s}\n ${o>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${o}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${o>0?\"}\":\"\"}\n `}this.userCode=`\n ${_nt(e,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?_d():Td(t)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":t[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":t[2]};\n\n ${n}\n\n setOutput(result);\n }\n `}};function _nt(r,t){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${t?nL([\"r\",\"c\",\"d\"],\"inputShape\"):Ii([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var jw=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=oz(e,n),s=sz(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=nz(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===Lr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Lr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Lr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=oz(n,o),i=sz(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=nz(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}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 t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Ent(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function nz(r,t,e,n,o){let s=Ant(t,n),i;if(o){let[u,l]=wa(r[0],r[1]);i=u*l}else{let[u,l]=mp(r[0],r[1]);i=u*l}let a=Ent(e,s);return i*a}function Ant(r,t){switch(r){case Lr.PACKED_2X2_FLOAT32:return Gw(t);case Lr.PACKED_2X2_FLOAT16:return Ww(t);case Lr.UNPACKED_FLOAT32:return zw(t);case Lr.UNPACKED_FLOAT16:return Bw(t);case Lr.PACKED_4X1_UNSIGNED_BYTE:return Vw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function Dnt(r){return L().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?Lr.PACKED_2X2_FLOAT32:Lr.UNPACKED_FLOAT32:r?Lr.PACKED_2X2_FLOAT16:Lr.UNPACKED_FLOAT16}function oz(r,t){if(r===Zr.UPLOAD)return Lr.PACKED_2X2_FLOAT32;if(r===Zr.RENDER||r==null)return Dnt(t);if(r===Zr.DOWNLOAD||r===Zr.PIXELS)return Lr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function sz(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var zr=class{constructor(t,e){this.variableNames=[\"A\"],this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${e}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},yr=\"if (isnan(x)) return x;\",iz=\"return x;\",a1=\"return abs(x);\";var az=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",lz=yr+`\n return (x < 0.0) ? 0.0 : x;\n`,uz=yr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Ia=\"return x;\",cz=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var mz=\"return x;\",fz=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,dz=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,hz=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,gz=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Dn=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${e}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var Xw=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let e=t.length,n=er(\"rc\",e),o=zt(e),s=rz(e,n),i=n.slice(-2),a=e<=1?\"rc\":`vec2(${i.join(\",\")})`;this.userCode=`\n void main() {\n ${o} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${a}));\n }\n `}};var Rnt=Kr.whereImpl,Fnt=1e-7,Ont=1e-4,Yw={};function Pnt(r){return r in Yw||(Yw[r]={}),Yw[r]}var Mnt=L().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),Lnt=600;function znt(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*Lnt/1024/1024}var Ku=class extends Go{nextDataId(){return Ku.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!L().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let e;if(t!=null){if(t instanceof hp)e=t;else{let n=qn(L().getNumber(\"WEBGL_VERSION\"),t);e=new hp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=qn(L().getNumber(\"WEBGL_VERSION\"));e=new hp(n),this.binaryCache=Pnt(L().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new jw(this.gpgpu),this.numMBBeforeWarning=znt(),this.texData=new Ta(this,Bn())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=Nd(e),c=new ig(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((L().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||L().getBool(\"DEBUG\"))&&this.checkNumericalProblems(t),n===\"complex64\"&&t!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Zr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(L().getBool(\"DEBUG\")&&this.checkNumericalProblems(e),o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Zr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new Dn(a,Ia):m=new zr(a,Ia);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o===\"string\")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o===\"complex64\"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new Dn(o,Ia):d=new zr(o,Ia);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(L().getBool(\"DEBUG\")&&!L().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&L().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(i!==\"complex64\"&&L().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...rg(o))}this.pendingRead.set(t,[]),i!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let p;if(i===\"complex64\"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Bn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(i!=null){let f;u?f=new Dn(s,Ia):f=new zr(s,Ia);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(t,e.customTexShape),p=Bn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype===\"string\")try{let n=e.map(o=>y.decodeString(o));return bt(t.shape,t.dtype,n)}catch(n){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return bt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(\", \")}else a.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=Mnt){return L().getBool(\"WEBGL_CPU_FORWARD\")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Bn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new Xw(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new Kw(t.shape),n=!0;return this.runWebGLProgram(e,[t],t.dtype,null,n)}packedReshape(t,e){let n=[zl(t.shape),...Bl(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[zl(e),...Bl(e)],i=new Fd(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let a=Nd(s),u;o?u=new Ow(a):u=new Fw(a);let l=!0,c=[e!=null?e:rg(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Hu.DENSE){let x=i!=null?i:rg(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=L().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!qu(b.shape,x.shape)){let w=x,I=x.shape;x.shape=b.shape,x=this.packedReshape(x,I),l.push(x),b=this.texData.get(x.dataId),w.shape=I}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=pL(t,c,p),f=this.getAndSaveBinary(m,()=>uL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get(\"ENGINE_COMPILE_ONLY\")||cL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=L().get(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!L().getBool(\"WEBGL_LAZILY_UNPACK\")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(L().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=B(()=>{if(!L().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let t=L().getBool(\"DEBUG\");L().set(\"DEBUG\",!1);let e=this.abs(ft(1e-8)).dataSync()[0];if(L().set(\"DEBUG\",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Fnt:Ont}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=OT(n,u),e.texShape=p),s!=null){let m=Nd(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=wa(p[0],p[1])),u?f=new Lw(m,g):f=new ig(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Zr.PIXELS:w.usage=Zr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let I=[[h,d]],N=!0,E=this.runWebGLProgram(f,[b],o,I,N),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,L().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=Bnt(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!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(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Sh(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(_w(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=GT(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=a,t.outTexShapeLocation=u}}createTensorFromGPUData(t,e,n){t.channels=t.channels||\"RGBA\";let{texture:o,height:s,width:i,channels:a}=t,u=Bn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let l=u.writeTexture(o,e,n,s,i,a);return Bn().makeTensorFromDataId(l,e,n,u)}};Ku.nextDataId=0;function Bnt(r,t){if(t===\"float32\"||t===\"complex64\")return r;if(t===\"int32\"||t===\"bool\"){let e=t===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew Ku,2);var n2e={forceHalfFloat:yz};var Od=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var po=class{constructor(t,e,n){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${t}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var jn=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var Lo=class{constructor(t,e,n,o=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=de(s);let i=\"\";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(i=`\n ${zt(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?i+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:i+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let u=er(\"coords\",s);this.enableShapeUniforms?i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${t}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${i}\n\n setOutput(result);\n }\n `}};function rr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var bz={kernelName:go,backendName:\"webgl\",kernelFunc:rr};function $n(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,\"complex64\"),i=e.texData.get(s.dataId),a=rr({inputs:{x:n},backend:e}),u=rr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var wz={kernelName:Op,backendName:\"webgl\",kernelFunc:$n};var l1=\"return (a < 0.) ? b * a : a;\",u1=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function Vnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),a=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Lo(u1,o.shape,i.shape):new po(l1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],\"float32\");return e.disposeIntermediateTensorInfo(i),u}var Iz={kernelName:ws,backendName:\"webgl\",kernelFunc:Vnt};var c1=\"return (a < 0.) ? b * a : a;\",p1=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function Gnt(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Lo(p1,n.shape,o.shape):new po(c1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],\"float32\")}var Cz={kernelName:Os,backendName:\"webgl\",kernelFunc:Gnt};var zo=\"if (isnan(x)) return x;\";function wt({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=L().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new Dn(i.shape,t):c=new zr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function ue({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype===\"complex64\"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[I,N]=w,E={dataId:I.dataId,dtype:I.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},D=new po(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],lr(I.dtype,N.dtype))}),b=$n({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||lr(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?S.fromUint8ToStringArray(d):d,x=u.dtype===\"string\"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),I=c.makeTensorInfo(w,p),N=c.texData.get(I.dataId);return N.values=b,I}let m=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,f;return m?f=new Lo(t,u.shape,l.shape,e):f=new po(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Vl(r,t=!1){if(r===\"linear\")return t?mz:iz;if(r===\"relu\")return t?dz:lz;if(r===\"elu\")return t?fz:az;if(r===\"relu6\")return t?hz:uz;if(r===\"prelu\")return t?p1:c1;if(r===\"leakyrelu\")return t?u1:l1;if(r===\"sigmoid\")return t?gz:cz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Pd=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=de(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?\"i * 2, rc.y\":\"rc.y, i * 2\",f=s?\"rc.z, i * 2\":\"i * 2, rc.z\",d=o?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";a&&(u?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${a}\n }`:l?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${a}\n }`:g=`vec4 activation(vec4 x) {\n ${a}\n }`,x=\"result = activation(result);\");let b=i?\"result += getBiasAtOutCoords();\":\"\";i&&this.variableNames.push(\"bias\"),u&&this.variableNames.push(\"preluActivationWeights\"),l&&this.variableNames.push(\"leakyreluAlpha\");let w=\"rc.x\",I=\"rc.x\";t[0]`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!qu(o.shape,u)&&!(c.texture!==null&&qu(c.shape,u))?Nz(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var kz={kernelName:zi,backendName:\"webgl\",kernelFunc:rt};var cg=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l=\"sumValue += dot(values, ones);\";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c=\"\";s%n>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${a}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${l}\n }\n\n int inIdx = inOffset + ${a};\n if (${u===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${l}\n } else if (${u===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${l}\n } else if (${u===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${l}\n }\n setOutput(sumValue);\n }\n `}};var Zw=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=\"0.0\",u=\"\";e===\"prod\"?a=\"1.0\":e===\"min\"?(a=\"1.0 / 1e-20\",u=\"min\"):e===\"max\"&&(a=\"-1.0 / 1e-20\",u=\"max\");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"sum\"?l=\"sumValue\":e===\"prod\"?l=\"prodValue\":e===\"all\"?l=\"allValue\":e===\"any\"&&(l=\"anyValue\");let c=Math.floor(n/4)*4,p=n%4,m=`\n if (${e===\"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${e===\"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${u}(values, minMaxValue);\n if (${e===\"min\"} || ${e===\"max\"}) {\n minMaxValue = ${u}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,f=\"vec4\";e===\"all\"?(a=\"1.0\",m=`\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `,f=\"bvec4\"):e===\"any\"&&(a=\"0.0\",m=`\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n `,f=\"bvec4\");let d=\"\";s%n>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${a};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n vec4 minMaxValue = vec4(${a});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${p===1}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${p===2}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${p===3}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function Unt(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function Xn(r,t,e,n){let o=Unt(r.shape),s=r;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],n=new Array(t);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=zt(this.rank),s=i1(\"rc\",this.rank),i=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes 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Pd(N,E,[P,f,d],e,n,G,H,W,q),st=[A,D];if(s!=null&&st.push(s),W&&st.push(i),q){let at=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));st.push(at),F.push(at)}X=o.runWebGLProgram(nt,st,et)}let Z=rt({inputs:{x:X},backend:o,attrs:{shape:I}});F.push(X);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function qnt(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return yp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var Az={kernelName:qi,backendName:\"webgl\",kernelFunc:qnt};var Dz=\"return abs(x);\";function Knt(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!==\"complex64\"){let s=e.texData.get(n.dataId),i=Hw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?o=new Dn(n.shape,Dz):o=new zr(n.shape,Dz),e.runWebGLProgram(o,[n],n.dtype)}var $z={kernelName:Ti,backendName:\"webgl\",kernelFunc:Knt};var jnt=yr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,Xnt=wt({opSnippet:jnt}),Rz={kernelName:Uo,backendName:\"webgl\",kernelFunc:Xnt};var Ynt=yr+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,Znt=wt({opSnippet:Ynt}),Fz={kernelName:Ho,backendName:\"webgl\",kernelFunc:Znt};var Oz=\"return a + b;\",Jnt=ue({opSnippet:Oz,packedOpSnippet:Oz,supportsComplex:!0,cpuKernelImpl:mL}),Pz={kernelName:ro,backendName:\"webgl\",kernelFunc:Jnt};var tI=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${n.join(`\n `)}\n\n float result = ${o};\n setOutput(result);\n }\n `}};var eI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${n.join(`\n `)}\n\n vec4 result = ${o};\n setOutput(result);\n }\n `}};function rI(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return rr({inputs:{x:n[0]},backend:e});if(n.length>L().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let u=Math.floor(n.length/2),l=rI({inputs:n.slice(0,u),backend:e}),c=rI({inputs:n.slice(u),backend:e});return rI({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>lr(u,l)),s=n.map(u=>u.shape),a=L().getBool(\"WEBGL_PACK\")?new eI(n[0].shape,s):new tI(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var Mz={kernelName:qo,backendName:\"webgl\",kernelFunc:rI};function Qnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"all\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Xn(h,h.dtype,\"all\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var Lz={kernelName:Ea,backendName:\"webgl\",kernelFunc:Qnt};function tot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"any\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Xn(h,h.dtype,\"any\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var zz={kernelName:Aa,backendName:\"webgl\",kernelFunc:tot};var nI=class{constructor(t,e,n){this.variableNames=[\"A\"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,i];let a=e===\"max\"?\">\":\"<\",u=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${o}; i++) {\n int inIdx = ${u};\n float candidate = getA(batch, inIdx);\n if (candidate ${a} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var oI=class{constructor(t,e,n,o){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push(\"bestIndicesA\");let a=this.outputShape,u=a.length,l=zt(u),c=er(\"coords\",u),p,m;if(i===1){m=u+1;let D=zt(m);p=`\n ${D} sourceLocR = ${D}(${c.join()}, 0);\n ++${c[u-1]};\n ${D} sourceLocG = ${D}(${c.join()}, 0);\n ++${c[u-2]};\n ${D} sourceLocA = ${D}(${c.join()}, 0);\n --${c[u-1]};\n ${D} sourceLocB = ${D}(${c.join()}, 0);\n --${c[u-2]};`}else m=u,p=`\n ${l} sourceLocR = coords;\n ++${c[u-1]};\n ${l} sourceLocG = coords;\n ++${c[u-2]};\n ${l} sourceLocA = coords;\n --${c[u-1]};\n ${l} sourceLocB = coords;\n --${c[u-2]};`;let f=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),d=\".\"+f[m-1],h=f.map(D=>\"int \"+D),g=er(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=er(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=er(\"sourceLocB\",m-1).concat(\"inIdx.b\"),w=er(\"sourceLocA\",m-1).concat(\"inIdx.a\"),I=n===\"max\"?\"greaterThan\":\"lessThan\",N=o?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${w.join()})));`,E=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? getAChannel(${b.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,A=o?\"\":`\n float getBestIndicesAChannel(${h.join()}) {\n return getChannel(getBestIndicesA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${h.join()}) {\n return getChannel(getA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }\n ${A}\n void main() {\n ${l} coords = getOutputCoords();\n bool hasNextCol = ${c[u-1]} < ${a[u-1]-1};\n bool hasNextRow = ${c[u-2]} < ${a[u-2]-1};\n ${p}\n ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},\n sourceLocB${d}, sourceLocA${d}) * ${e};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${E};\n\n for (int i = 0; i < ${e}; i++) {\n inIdx = srcIdx;\n ${N}\n vec4 candidate = ${E};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${I}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}};function Bz(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new nI(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=Bz(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function Vz(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new oI(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,\"int32\");if(l.shape.length===t.shape.length){let c=Vz(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function sI(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!L().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=rt({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=Bz(r,m,n);s.push(f);let d=rt({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return Vz(r,t,n)}function eot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=sI(e,u,i[0],\"max\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var Gz={kernelName:_i,backendName:\"webgl\",kernelFunc:eot};function rot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=sI(e,u,i[0],\"min\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var Wz={kernelName:Ei,backendName:\"webgl\",kernelFunc:rot};var not=yr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,oot=wt({opSnippet:not}),Uz={kernelName:Ko,backendName:\"webgl\",kernelFunc:oot};var sot=yr+\"return log(x + sqrt(x * x + 1.0));\",iot=wt({opSnippet:sot}),Hz={kernelName:jo,backendName:\"webgl\",kernelFunc:iot};var aot=yr+`\n return atan(x);\n`,lot=wt({opSnippet:aot}),qz={kernelName:Xo,backendName:\"webgl\",kernelFunc:lot};var uot=Od+`\n return atan(a, b);\n`,cot=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+jn+`\n return result;\n`,pot=ue({opSnippet:uot,packedOpSnippet:cot}),Kz={kernelName:Zo,backendName:\"webgl\",kernelFunc:pot};var mot=yr+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelName:Yo,backendName:\"webgl\",kernelFunc:fot};var Ci=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e===\"avg\",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),n){let D=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${D} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let w=\"max\",I=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(I=\"avgValue / max(count, 1.0)\");let N=Math.floor(i/4)*4,E=i%4,A=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${w}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${N}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${A}\n }\n\n int xC = xCCorner + ${N};\n if (${E===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${E===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${E===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${A}\n }\n }\n setOutput(${I});\n }\n `}},Xu=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e===\"avg\",I=\"0.0\";if(w||(I=\"-1.0 / 1e-20\"),n){let P=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${P} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${d} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let N=\"max\",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(E=\"avgValue / max(count, 1.0)\");let A=Math.floor(i/4)*4,D=i%4,F=`\n if (${w}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${N}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${I};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${I});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${A}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${F}\n }\n\n int xC = xCCorner + ${A};\n if (${D===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${D===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${D===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${F}\n }\n }\n }\n setOutput(${E});\n }\n `}};function dot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;wi(o,\"avgPool\");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new Ci(c,\"avg\",!1);return e.runWebGLProgram(p,[o],\"float32\")}var Xz={kernelName:Jo,backendName:\"webgl\",kernelFunc:dot};function hot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new Xu(p,\"avg\",!1);return e.runWebGLProgram(m,[o],\"float32\")}var Yz={kernelName:Ai,backendName:\"webgl\",kernelFunc:hot};var iI=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${p});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${u};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${l};\n wC+= ${a}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},aI=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=`\n const ivec3 pads = ivec3(${d}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${u}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${l}) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${f};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function got(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new aI(m);return e.runWebGLProgram(f,[o],i.dtype)}var Zz={kernelName:Xl,backendName:\"webgl\",kernelFunc:got};function xot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;wi([o,s],\"avgPoolGrad\");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new iI(c);return e.runWebGLProgram(p,[o],i.dtype)}var Jz={kernelName:Fp,backendName:\"webgl\",kernelFunc:xot};function yot(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return yp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var Qz={kernelName:Qo,backendName:\"webgl\",kernelFunc:yot};var lI=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"0.0\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"1.0\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${a};\n float scale = ${u};\n float inv = scale * inversesqrt(variance + float(${i}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var uI=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"vec4(0.0)\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"vec4(1.0)\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n vec4 offset = ${a};\n vec4 scale = ${u};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${i}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var bot=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(i==null||o.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(a==null||o.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=L().getBool(\"WEBGL_PACK_NORMALIZATION\")?new uI(n.shape,o.shape,s.shape,c,p,u):new lI(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},t3={kernelName:hs,backendName:\"webgl\",kernelFunc:bot};var cI=class{constructor(t){this.variableNames=[\"source\"],this.outputShape=t,this.rank=t.length;let e=zt(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let n=wot(this.rank),o,s=t.map((i,a)=>`sourceLoc.${d1[a]} = start[${a}] + coords.${d1[a]};`);o=`\n ${e} sourceLoc;\n ${e} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${o}\n setOutput(getSource(${n}));\n }\n `}},d1=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function wot(r){if(r===1)return\"sourceLoc\";if(r<=6)return d1.slice(0,r).map(t=>\"sourceLoc.\"+t).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var pI=class{constructor(t){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let e=zt(this.rank),n=er(\"coords\",this.rank),o=er(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=`\n result.x = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.y = ${i};\n --${o[this.rank-1]};\n }\n `,u=this.rank===1?\"\":`\n --${n[this.rank-1]};\n if (++${n[this.rank-2]} < ${t[this.rank-2]}) {\n ++${o[this.rank-2]};\n result.z = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.w = ${i};\n }\n }\n `,l=this.rank<=4?`sourceLoc = coords +\n ${e}(${t.map((c,p)=>`start[${p}]`).join()});`:t.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`\n`);this.userCode=`\n void main() {\n ${e} coords = getOutputCoords();\n ${e} sourceLoc;\n ${l}\n vec4 result = vec4(0.);\n ${a}\n ${u}\n setOutput(result);\n }\n `}};function Iot(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=ze.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function vi(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=ze.parseSliceParams(o,s,i);if(ze.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype===\"string\"){let p=e.texData.get(o.dataId),m=WL(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=ze.isSliceContinous(o.shape,a,u);if(l||!c){let p=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new pI(u):new cI(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),Iot(o,a,u,e)}var e3={kernelName:Vi,backendName:\"webgl\",kernelFunc:vi};var Cot=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=rt({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:c}}),x=vi({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},r3={kernelName:Di,backendName:\"webgl\",kernelFunc:Cot};function vot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=Uw(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var n3={kernelName:Da,backendName:\"webgl\",kernelFunc:vot};function Sot(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],\"int32\",Int32Array.from(a))}var o3={kernelName:Yl,backendName:\"webgl\",kernelFunc:Sot};var Not=\"return float(a != b);\",h1=ue({opSnippet:Not,cpuKernelImpl:FL,dtype:\"bool\"}),s3={kernelName:Ja,backendName:\"webgl\",kernelFunc:h1};function Gl(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.real},backend:e})}var i3={kernelName:jp,backendName:\"webgl\",kernelFunc:Gl};var kot=\"return float(int(x));\";function a3(r,t){let e=new zr(r.shape,kot),n=t.runWebGLProgram(e,[r],\"int32\");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function g1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s===\"complex64\"){if(o.dtype===\"complex64\")return rr({inputs:{x:o},backend:e});let i=Te(o.shape),a=g1({inputs:{x:o},backend:e,attrs:{dtype:\"float32\"}}),u=$n({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype===\"complex64\"){let i=Gl({inputs:{input:o},backend:e}),a=g1({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!y.hasEncodingLoss(o.dtype,s)){let i=rr({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(e.shouldExecuteOnCPU([o])){let i=e.texData.get(o.dataId).values,[a,u,l]=dL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s===\"int32\")return a3(o,e);if(s===\"bool\"){let i=e.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),u=h1({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var l3={kernelName:fo,backendName:\"webgl\",kernelFunc:g1};var u3=\"return ceil(x);\",Tot=wt({opSnippet:u3,packedOpSnippet:u3,cpuKernelImpl:hL}),c3={kernelName:ts,backendName:\"webgl\",kernelFunc:Tot};var mI=class{constructor(t){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var fI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function _ot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;L().getBool(\"WEBGL_PACK_CLIP\")?a=new fI(o.shape):a=new mI(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var p3={kernelName:ho,backendName:\"webgl\",kernelFunc:_ot};var dI=class{constructor(t){this.variableNames=[\"real\",\"imag\"],this.outputShape=t,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `}};function m3(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function Eot(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new dI(n.shape),i=[m3(n,o.complexTensorInfos.real),m3(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var f3={kernelName:Zl,backendName:\"webgl\",kernelFunc:Eot};var hI=class{constructor(t){this.outputShape=[],this.outputShape=S.computeOutShape(t,1),this.variableNames=t.map((i,a)=>`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h= ${u[h-1]}) {\n return getChannel(\n getT${h}(${gI(a,l,g)}),\n vec2(${gI(c,l,g)}));\n }`}let f=u.length,d=u[u.length-1];m+=`\n return getChannel(\n getT${f}(${gI(a,l,d)}),\n vec2(${gI(c,l,d)}));`,this.userCode=`\n float getValue(${a.map(h=>\"int \"+h)}) {\n ${m}\n }\n\n void main() {\n ${s} coords = getOutputCoords();\n vec4 result = vec4(getValue(${i}), 0., 0., 0.);\n\n ${i[o-1]} = ${i[o-1]} + 1;\n if (${i[o-1]} < ${n[o-1]}) {\n result.g = getValue(${i});\n }\n\n ${i[o-2]} = ${i[o-2]} + 1;\n if (${i[o-2]} < ${n[o-2]}) {\n result.a = getValue(${i});\n }\n\n ${i[o-1]} = ${i[o-1]} - 1;\n if (${i[o-2]} < ${n[o-2]} &&\n ${i[o-1]} < ${n[o-1]}) {\n result.b = getValue(${i});\n }\n setOutput(result);\n }\n `}};function gI(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function bp(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var d3={kernelName:Gp,backendName:\"webgl\",kernelFunc:bp};function Md(r,t,e){let n=r[0].dtype;if(n===\"complex64\"){let f=r.map(b=>Gl({inputs:{input:b},backend:e})),d=r.map(b=>bp({inputs:{input:b},backend:e})),h=Md(f,t,e),g=Md(d,t,e),x=$n({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n===\"string\"&&(o=!0),o){let f=r.map(I=>{let E=[-1,y.sizeFromShape(I.shape.slice(t))];return rt({inputs:{x:I},backend:e,attrs:{shape:E}})}),d=f.map(I=>({vals:e.readSync(I.dataId),shape:I.shape})),h=S.computeOutShape(f.map(I=>I.shape),1),g=f[0].shape[0]===1,x=gL(d,h,n,g),b=S.computeOutShape(r.map(I=>I.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let f=i?new zr(r[0].shape,Ia):new Dn(r[0].shape,Ia);return e.runWebGLProgram(f,r,n)}let a=L().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>a){let f=[];for(let h=0;hd.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=Aot(s,t,e),c=new hI(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=rt({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function Aot(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>rt({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function x1(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?rr({inputs:{x:u[0]},backend:e}):Md(u,s,e)}var h3={kernelName:$i,backendName:\"webgl\",kernelFunc:x1};var Ld=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,w=g?3:1,I=\"\",N=\"\";n&&(o?I=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?I=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:I=`\n float activation(float x) {\n ${n}\n }\n `,N=\"result = activation(result);\");let E=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${I}\n\n const ivec2 strides = ivec2(${u}, ${l});\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${w}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${p};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${d}) *\n getW(wR, wC, ${d}, d2);\n } else {\n dotProd +=\n getX(batch, ${d}, xR, xC) *\n getW(wR, wC, ${d}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2),\n getW(wR, wC, ${d} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1),\n getX(batch, xR, xC, ${d} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC),\n getX(batch, ${d} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${E}\n ${N}\n setOutput(result);\n }\n `}},yI=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${i}, ${a});\n const ivec3 pads = ivec3(${e}, ${n}, ${o});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${p}; wF++) {\n int xF = xFCorner + wF * ${u};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${l};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${d}) *\n getW(wF, wR, wC, ${d}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1),\n getX(batch, xF, xR, xC, ${d} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2),\n getW(wF, wR, wC, ${d} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var zd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(p+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*u};\n `,a===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,u===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,u>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${a}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+p}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+p}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${l}\n\n ${o.output} = result;\n }\n `}};function wI(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function II({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat===\"channelsLast\",d=!1,h=!1,g,x=[];if(s!=null){let I=wI(s.shape,f);I!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:I}}),x.push(s))}if(o!=null){let I=wI(o.shape,f);I!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:I}}),x.push(o))}if(!((p===1||m===1)&&c>f1)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let I=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,I,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(qu(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let D=yp({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get(D.dataId);y.assert(F.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=E,F.shape=e.outShape,g=rr({inputs:{x:D},backend:n}),g.shape=e.outShape,x.push(D)}else{let I=e.outHeight*e.outWidth,N=rt({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,I,e.inChannels]:[e.batchSize,e.inChannels,I]}}),E=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=yp({a:f?N:E,b:f?E:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=rt({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(E),x.push(A)}for(let I of x)n.disposeIntermediateTensorInfo(I);return g}function CI({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f===\"channelsLast\",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,I=[];if(s!=null){let Z=wI(s.shape,d);Z!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:Z}}),I.push(s))}if(o!=null){let Z=wI(o.shape,d);Z!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:Z}}),I.push(o))}let N=rt({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});I.push(N);let E=new bI(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],D=n.runWebGLProgram(E,[r],\"float32\",A),F=rt({inputs:{x:D},backend:n,attrs:{shape:x}});I.push(D),I.push(F);let P=o!=null,V=s!=null,G=a===\"leakyrelu\",W=a?Vl(a,!0):null,q=new Pd(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,P,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));H.push(Z),I.push(Z)}let K=n.runWebGLProgram(q,H,\"float32\"),X=rt({inputs:{x:K},backend:n,attrs:{shape:e.outShape}});I.push(K);for(let Z of I)n.disposeIntermediateTensorInfo(Z);return X}function Dot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,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=II({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p===\"channelsLast\"&&L().getBool(\"WEBGL_EXP_CONV\")){let h=new zd(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],\"float32\",g)}else if(L().getBool(\"WEBGL_CONV_IM2COL\"))f=CI({x:o,filter:s,convInfo:m,backend:e});else{let h=new Ld(m);f=e.runWebGLProgram(h,[o,s],\"float32\")}let d=rt({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var g3={kernelName:es,backendName:\"webgl\",kernelFunc:Dot};var vI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n ${i?`float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);`}\n }\n }\n }\n setOutput(dotProd);\n }\n `}},SI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat===\"channelsLast\",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${p}];\n\n ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n\n if (${i}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},NI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yF = 0; yF < ${t.outDepth}; yF++) {\n int xF = wF + yF * ${e} - ${s};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${n} - ${i};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${a};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},kI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${u}, ${l}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${e}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${e} - 1 - wF;\n\n for (int wR = 0; wR < ${n}; wR++) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${n} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function $ot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new vI(m);return e.runWebGLProgram(f,[o,s],\"float32\")}var x3={kernelName:Pp,backendName:\"webgl\",kernelFunc:$ot};var TI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"strides\",type:\"vec2\"}],this.outputShape=t.inShape,this.enableShapeUniforms=de(this.outputShape.length);let e=t.filterHeight,n=t.filterWidth,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left;this.userCode=`\n const ivec2 pads = ivec2(${o}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n vec4 result = vec4(0.);\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / strides[0];\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n int wCPerm = ${n} - 1 - wC;\n\n float dyC = float(dyCCorner + wC) / strides[1];\n bool idyCVal = (dyC >= 0.0) && (dyC < ${t.outWidth}.0)\n && (fract(dyC) == 0.0);\n int idyC = int(dyC);\n\n float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${t.outWidth}.0)\n && (fract(dyC2) == 0.0);\n int idyC2 = int(dyC2);\n\n if (idyCVal && idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n dySample : getDy(batch, idyR, idyC2, d2);\n\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n\n dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample2.xy : dySample2.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC2, d2);\n vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n }\n }\n }\n setOutput(result);\n }\n `}};function Rot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p);if(L().getBool(\"WEBGL_PACK\")&&p===\"channelsLast\"){let f=[[m.strideHeight,m.strideWidth]],d=new TI(m);return e.runWebGLProgram(d,[o,s],\"float32\",f)}else{let f=new SI(m);return e.runWebGLProgram(f,[o,s],\"float32\")}}var y3={kernelName:rs,backendName:\"webgl\",kernelFunc:Rot};function Fot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new yI(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var b3={kernelName:ns,backendName:\"webgl\",kernelFunc:Fot};function Oot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new NI(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var w3={kernelName:$a,backendName:\"webgl\",kernelFunc:Oot};function Pot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new kI(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var I3={kernelName:Ra,backendName:\"webgl\",kernelFunc:Pot};var Mot=zo+`\n return cos(x);\n`,Lot=`\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${jn}\n return result;\n`,zot=wt({opSnippet:Mot,packedOpSnippet:Lot}),C3={kernelName:os,backendName:\"webgl\",kernelFunc:zot};var Bot=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,Vot=wt({opSnippet:Bot}),v3={kernelName:ss,backendName:\"webgl\",kernelFunc:Vot};var _I=class{constructor(t,e,n,o,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o===\"bilinear\"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,\"(y2-y1) * height_ratio\",`y1*${d} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${d}`],[w,I,N]=m>1?[`${(u-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${w});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${i}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${I};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${d} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${N};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${f} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var Got=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new _I(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],\"float32\")},S3={kernelName:Oa,backendName:\"webgl\",kernelFunc:Got};var wp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(wp||(wp={}));var pg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,i=this.op===wp.Prod?\"1.0\":\"0.0\",a=n?i:`getX(${N3(s,\"coords\",this.op)})`,u=this.outputShape[this.outputShape.length-1],l=\"\",c=\"\";n?(l=o?`end != ${u-1}`:\"end != 0\",c=o?\"end + 1\":\"end - 1\"):(l=o?`end + pow2 < ${u}`:\"end >= pow2\",c=o?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${zt(s)} coords = getOutputCoords();\n int end = ${k3(s,\"coords\",this.op)};\n float val = ${a};\n int pow2 = int(pow(2.0, index));\n if (${l}) {\n int idx = ${c};\n ${k3(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${N3(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function N3(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function k3(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function EI(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=rr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new pg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new pg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function Wot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return EI(wp.Prod,o,e,s,i,a)}var T3={kernelName:Fa,backendName:\"webgl\",kernelFunc:Wot};function Uot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return EI(wp.Sum,o,e,s,i,a)}var _3={kernelName:is,backendName:\"webgl\",kernelFunc:Uot};function Hot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Uw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=fL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var E3={kernelName:Jl,backendName:\"webgl\",kernelFunc:Hot};var AI=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${e};\n int offset_h = imod(h, ${e});\n int in_w = w / ${e};\n int offset_w = imod(w, ${e});\n int offset_d = (offset_h * ${e} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}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 qot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i===\"NHWC\"?o.shape[1]:o.shape[2],l=i===\"NHWC\"?o.shape[2]:o.shape[3],c=i===\"NHWC\"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i===\"NHWC\"?[a,p,m,f]:[a,f,p,m],h=new AI(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var A3={kernelName:Pa,backendName:\"webgl\",kernelFunc:qot};var Bd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l=\"\",c=\"\";n&&(o?l=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?l=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:l=`\n float activation(float x) {\n ${n}\n }\n `,c=\"result = activation(result);\");let p=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${l}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${u};\n int q = d2 - d1 * ${u};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${i}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${a}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${p}\n ${c}\n setOutput(result);\n }\n `}};var Vd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(f+=`\n xC = xCCorner + ${b*l};\n `,u===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,l===1&&b>0?f+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:f+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):f+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,l>1?f+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:f+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):w===1?f+=`\n xC${b+1} = xTexelC${b};\n `:f+=`\n xCOffset = xC + ${w};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(f+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;L().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new Vd(p):m=new Bd(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],\"float32\",f)}var D3={kernelName:as,backendName:\"webgl\",kernelFunc:Kot};var DI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${i} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},$I=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${u}; dm++) {\n int d2 = d1 * ${u} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new DI(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var $3={kernelName:Mp,backendName:\"webgl\",kernelFunc:jot};function Xot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new $I(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var R3={kernelName:Lp,backendName:\"webgl\",kernelFunc:Xot};var RI=class{constructor(t){this.variableNames=[\"X\"],this.outputShape=[t,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function Yot(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=rt({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new RI(s),u=e.runWebGLProgram(a,[i],i.dtype),l=rt({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var F3={kernelName:Ql,backendName:\"webgl\",kernelFunc:Yot};var FI=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${i});\n const ivec2 pads = ivec2(${p}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${a}; h++) {\n int hIn = hBeg + h * ${l};\n\n if (hIn >= 0 && hIn < ${e}) {\n for (int w = 0; w < ${u}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${n}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function Zot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,\"NHWC\",u),c,p=new FI(l);c=e.runWebGLProgram(p,[o,s],\"float32\");let m=rt({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var O3={kernelName:ls,backendName:\"webgl\",kernelFunc:Zot};function Jot(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h=0&&(m=xp({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var P3={kernelName:zp,backendName:\"webgl\",kernelFunc:Jot};var Qot=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",tst=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,est=wt({opSnippet:Qot,packedOpSnippet:tst}),M3={kernelName:cs,backendName:\"webgl\",kernelFunc:est};var rst=\"return (b >= 0.0) ? a : a * (b + 1.0);\",nst=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,ost=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Lo(nst,n.shape,o.shape):new po(rst,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},L3={kernelName:Ma,backendName:\"webgl\",kernelFunc:ost};var sst=`\n return vec4(equal(a, b));\n`,ist=\"return float(a == b);\",ast=ue({opSnippet:ist,packedOpSnippet:sst,dtype:\"bool\",cpuKernelImpl:xL}),z3={kernelName:za,backendName:\"webgl\",kernelFunc:ast};var lst=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${S.ERF_P};\n float a1 = ${S.ERF_A1};\n float a2 = ${S.ERF_A2};\n float a3 = ${S.ERF_A3};\n float a4 = ${S.ERF_A4};\n float a5 = ${S.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,ust=wt({opSnippet:lst}),B3={kernelName:La,backendName:\"webgl\",kernelFunc:ust};var cst=zo+`\n return exp(x);\n`,pst=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,y1=wt({opSnippet:cst,packedOpSnippet:pst,cpuKernelImpl:yL,dtype:\"float32\"}),V3={kernelName:ps,backendName:\"webgl\",kernelFunc:y1};function OI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var G3={kernelName:Ri,backendName:\"webgl\",kernelFunc:OI};var W3=\"return exp(x) - 1.0;\",mst=wt({opSnippet:W3,packedOpSnippet:W3,cpuKernelImpl:bL}),U3={kernelName:ms,backendName:\"webgl\",kernelFunc:mst};var mg=class{constructor(t,e,n){this.variableNames=[\"real\",\"imag\"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:\"1.0\",a;if(t===\"real\")a=\"return real * expR - imag * expI;\";else if(t===\"imag\")a=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${t}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${a}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${o});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${o}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${i};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function PI(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=rt({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new mg(\"real\",u,t),c=new mg(\"imag\",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,\"float32\"),f=e.runWebGLProgram(c,p,\"float32\"),d=$n({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=rt({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function fst(r){let{inputs:t,backend:e}=r,{input:n}=t;return PI(n,!1,e)}var H3={kernelName:Bp,backendName:\"webgl\",kernelFunc:fst};var MI=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=t,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Wl(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s===\"string\"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new MI(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var q3={kernelName:ru,backendName:\"webgl\",kernelFunc:Wl};var LI=class{constructor(t){this.variableNames=[\"Image\"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${e} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${e}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var K3={kernelName:Ba,backendName:\"webgl\",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new LI(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var j3=\"return floor(x);\",dst=wt({opSnippet:j3,packedOpSnippet:j3,cpuKernelImpl:wL}),X3={kernelName:fs,backendName:\"webgl\",kernelFunc:dst};var hst=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,gst=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,xst=ue({opSnippet:hst,packedOpSnippet:gst,dtype:\"int32\"}),Y3={kernelName:ds,backendName:\"webgl\",kernelFunc:xst};var zI=class{constructor(t){this.variableNames=[\"A\"];let e=We(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);\n\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var BI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let e=We(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${o}.0, ${n}.0);\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${e.output} = result;\n }\n `}};var Z3={kernelName:rh,backendName:\"webgl\",kernelFunc:yst},Gd,b1=L().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function yst(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!=\"undefined\"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!=\"undefined\"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];if(a||i){let h=L().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Gd==null||h!==b1)&&(b1=h,Gd=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:b1})),Gd.canvas.width=u,Gd.canvas.height=l,Gd.drawImage(o,0,0,u,l),o=Gd.canvas}let m=e.makeTensorInfo(c,\"int32\");e.texData.get(m.dataId).usage=Zr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=L().getBool(\"WEBGL_PACK\")?new BI(p):new zI(p),d=e.runWebGLProgram(f,[m],\"int32\");return e.disposeData(m.dataId),d}function bst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),x,b=[],w=i!=null,I=a!=null,N=f===\"leakyrelu\",E=()=>{let D=[o,s],F=(P,V)=>{if(V===\"NCHW\"&&P.shape.length===1&&P.shape[0]!==1){let G=rt({inputs:{x:P},backend:e,attrs:{shape:[P.shape[0],1,1]}});return b.push(G),G}return P};if(w&&D.push(F(i,c)),I&&D.push(F(a,c)),N){let P=e.makeTensorInfo([],\"float32\",y.createScalarValue(d,\"float32\"));D.push(P),b.push(P)}return D};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=II({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(g.strideWidth<=2&&h===\"channelsLast\"&&L().getBool(\"WEBGL_EXP_CONV\")){let D=f?Vl(f,!0):null,F=new zd(g,w,D,I,N),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],V=E();x=e.runWebGLProgram(F,V,\"float32\",P)}else if(L().getBool(\"WEBGL_CONV_IM2COL\"))x=CI({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let D=f?Vl(f,!1):null,F=new Ld(g,w,D,I,N),P=E();x=e.runWebGLProgram(F,P,\"float32\")}let A=rt({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach(D=>e.disposeIntermediateTensorInfo(D)),A}var J3={kernelName:Ki,backendName:\"webgl\",kernelFunc:bst};function wst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),x=L().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Vl(m,x):null,w=[o,s],I=i!=null,N=a!=null,E=m===\"leakyrelu\";if(I&&w.push(i),N&&w.push(a),E){let P=e.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));w.push(P),d.push(P)}let A;x?A=new Vd(g,I,b,N,E):A=new Bd(g,I,b,N,E);let D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=e.runWebGLProgram(A,w,\"float32\",D);return d.forEach(P=>e.disposeIntermediateTensorInfo(P)),F}var Q3={kernelName:ji,backendName:\"webgl\",kernelFunc:wst};var VI=class{constructor(t,e,n,o){this.sliceDim=t,this.strides=e,this.paramsShape=o,this.variableNames=[\"x\",\"indices\"],this.outputShape=n;let s=zt(n.length),i=`\n int index;`;for(let a=0;a= ${this.paramsShape[a]};\n flattenIndex += index * ${this.strides[a]};`;this.userCode=`\n void main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${i}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function Ist(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=o.shape,i=s[s.length-1],a=y.sizeFromShape(n.shape),[u,l,c,p]=S.prepareAndValidate(n,o),m=rt({inputs:{x:o},backend:e,attrs:{shape:[l,i]}}),f=rt({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/c,c]}});if(e.shouldExecuteOnCPU([n,o])||n.dtype===\"string\"){let x=e.readSync(o.dataId),b=e.bufferSync(n),w=IL(x,b,n.dtype,l,i,c,p,n.shape,a);return e.makeTensorInfo(u,n.dtype,w.values)}let d=new VI(i,p,[l,c],n.shape),h=e.runWebGLProgram(d,[f,m],f.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:u}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var tB={kernelName:Va,backendName:\"webgl\",kernelFunc:Ist};var GI=class{constructor(t,e){this.variableNames=[\"A\",\"indices\"],this.outputShape=e,this.rank=e.length;let n=zt(this.rank),o=Cst(t,2);this.userCode=`\n void main() {\n ${n} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${o}));\n }\n `}};function Cst(r,t){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],n=[];for(let o=0;o=0,()=>`GatherV2: the index value ${N} is not in [0, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=rt({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=rt({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype===\"string\"){let b=e.bufferSync(f),w=e.bufferSync(m),I=CL(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,I.dtype,I.values)}let h=new GI(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=rt({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var eB={kernelName:Fi,backendName:\"webgl\",kernelFunc:w1};var vst=\"return float(a > b);\",Sst=`\n return vec4(greaterThan(a, b));\n`,Nst=ue({opSnippet:vst,packedOpSnippet:Sst,cpuKernelImpl:vL,dtype:\"bool\"}),rB={kernelName:Ga,backendName:\"webgl\",kernelFunc:Nst};var kst=\"return float(a >= b);\",Tst=`\n return vec4(greaterThanEqual(a, b));\n`,_st=ue({opSnippet:kst,packedOpSnippet:Tst,dtype:\"bool\",cpuKernelImpl:SL}),nB={kernelName:gs,backendName:\"webgl\",kernelFunc:_st};function Est(r){let{inputs:t,backend:e}=r,{input:n}=t;return PI(n,!0,e)}var oB={kernelName:Vp,backendName:\"webgl\",kernelFunc:Est};var Ast=\"return float(!isnan(x) && !isinf(x));\",Dst=wt({opSnippet:Ast,dtype:\"bool\"}),sB={kernelName:xs,backendName:\"webgl\",kernelFunc:Dst};var $st=\"return float(isinf(x));\",Rst=wt({opSnippet:$st,dtype:\"bool\"}),iB={kernelName:ys,backendName:\"webgl\",kernelFunc:Rst};var Fst=\"return float(isnan(x));\",Ost=wt({opSnippet:Fst,dtype:\"bool\"}),aB={kernelName:bs,backendName:\"webgl\",kernelFunc:Ost};var Pst=\"return float(a < b);\",Mst=`\n return vec4(lessThan(a, b));\n`,Lst=ue({opSnippet:Pst,packedOpSnippet:Mst,cpuKernelImpl:NL,dtype:\"bool\"}),lB={kernelName:Wa,backendName:\"webgl\",kernelFunc:Lst};var zst=\"return float(a <= b);\",Bst=`\n return vec4(lessThanEqual(a, b));\n`,Vst=ue({opSnippet:zst,packedOpSnippet:Bst,cpuKernelImpl:kL,dtype:\"bool\"}),uB={kernelName:Ua,backendName:\"webgl\",kernelFunc:Vst};function Gst(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=TL(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var cB={kernelName:Ha,backendName:\"webgl\",kernelFunc:Gst};var Wst=zo+`\n return x < 0.0 ? 0./0. : log(x);\n`,Ust=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,Hst=wt({opSnippet:Wst,packedOpSnippet:Ust,cpuKernelImpl:_L}),pB={kernelName:Is,backendName:\"webgl\",kernelFunc:Hst};var qst=zo+`\n return log(1.0 + x);\n`,Kst=wt({opSnippet:qst}),mB={kernelName:Cs,backendName:\"webgl\",kernelFunc:Kst};var jst=\"return float(a >= 1.0 && b >= 1.0);\",Xst=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,Yst=ue({opSnippet:jst,packedOpSnippet:Xst,dtype:\"bool\"}),fB={kernelName:qa,backendName:\"webgl\",kernelFunc:Yst};var Zst=\"return float(!(x >= 1.0));\",Jst=wt({opSnippet:Zst}),dB={kernelName:Ka,backendName:\"webgl\",kernelFunc:Jst};var Qst=\"return float(a >= 1.0 || b >= 1.0);\",tit=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,eit=ue({opSnippet:Qst,packedOpSnippet:tit,dtype:\"bool\"}),hB={kernelName:ja,backendName:\"webgl\",kernelFunc:eit};var WI=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${i}; j <= ${i}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${a}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${u};\n setOutput(val);\n }\n `}};var UI=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${i};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${i}; j <= ${i}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${u};\n setOutput(result);\n }\n `}};var rit=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=L().getBool(\"WEBGL_PACK_NORMALIZATION\")?new UI(o.shape,s,i,a,u):new WI(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},gB={kernelName:vs,backendName:\"webgl\",kernelFunc:rit};var HI=class{constructor(t,e,n,o,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${e})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${e} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${o}) * norm + float(${n});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${o})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var nit=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new HI(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},xB={kernelName:Xa,backendName:\"webgl\",kernelFunc:nit};function yB(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Xn(a,r.dtype,\"max\",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function I1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,I=new Array(a);for(let A=0;A`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new Ci(c,\"max\",!1);return e.runWebGLProgram(p,[o],o.dtype)}var IB={kernelName:ks,backendName:\"webgl\",kernelFunc:ait};function lit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new Xu(p,\"max\",!1);return e.runWebGLProgram(m,[o],o.dtype)}var CB={kernelName:Oi,backendName:\"webgl\",kernelFunc:lit};var qI=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${o}) {\n float dyR = float(dyRCorner + wR) / ${e}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${i}; wC++) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${i} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},KI=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${m}, ${f});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${u};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${e}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${l};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${a}) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${d} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${l} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function uit(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new Xu(m,\"max\",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new KI(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var vB={kernelName:nu,backendName:\"webgl\",kernelFunc:uit};function cit(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;wi([s,i],\"maxPoolGrad\");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new Ci(m,\"max\",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new qI(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var SB={kernelName:Wp,backendName:\"webgl\",kernelFunc:cit};function NB(r,t,e,n){let o=new Ci(e,\"max\",!1),s=n.runWebGLProgram(o,[r],\"float32\");o=new Ci(e,\"max\",!0,!0,t);let i=n.runWebGLProgram(o,[r],\"float32\");return[s,i]}var kB={kernelName:Up,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${l}'`);let c=S.computePool2DInfo(n.shape,o,s,l,i),[p,m]=NB(n,a,c,u);return[p,m]}};function TB(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Xn(a,\"float32\",\"mean\",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}var _B={kernelName:Ts,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{keepDims:o,axis:s}=t,i=e,a=n.shape.length,u=y.parseAxisParam(s,n.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=i.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let I=i.texData.get(d.dataId).values,N=new Array(a);for(let D=0;Dc[0]+t[p]+c[1]);let o=t.length,s=zt(o),i=e.map(c=>c[0]).join(\",\"),a=e.map((c,p)=>c[0]+t[p]).join(\",\"),u=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,o),l=n===\"reflect\"?0:1;if(o===1){this.userCode=`\n int start = ${i};\n int end = ${a};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start) {\n outC = start * 2 - outC - ${l};\n } else if(outC >= end) {\n outC = (end - 1) * 2 - outC + ${l};\n }\n setOutput(getX(outC - start));\n }\n `;return}this.userCode=`\n ${s} start = ${s}(${i});\n ${s} end = ${s}(${a});\n\n void main() {\n ${s} outC = getOutputCoords();\n for (int i = 0; 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ZI(l,i,a,u),p=rt({inputs:{x:o},backend:e,attrs:{shape:[l]}}),m=e.runWebGLProgram(c,[p],s);e.disposeIntermediateTensorInfo(p);let f=[...o.shape,i],d=rt({inputs:{x:m},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(m),d},GB={kernelName:$s,backendName:\"webgl\",kernelFunc:Dit};function fg(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype===\"complex64\"){let o=Gl({inputs:{input:n},backend:e}),s=fg({inputs:{x:o},backend:e}),i=bp({inputs:{input:n},backend:e}),a=fg({inputs:{x:i},backend:e}),u=$n({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Wl({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype===\"string\"?\"\":0},backend:e})}var WB={kernelName:Hi,backendName:\"webgl\",kernelFunc:fg};function UB(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype===\"string\")throw new Error(\"onesLike is not supported under string 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QI=class{constructor(t,e,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=zt(o),i=e.map(h=>h[0]).join(\",\"),a=e.map((h,g)=>h[0]+t[g]).join(\",\"),u=er(\"rc\",o),l=er(\"source\",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?\"source\":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1;\n if(${c}) {\n `,o===1?\"\":`}\n rc = outputLoc;\n ${u[o-2]} += 1;\n if(${u[o-2]} < ${this.outputShape[o-2]}) {`,o===1?\"\":` ${u[o-1]} += 1;\n if(${c}) {`],f=o===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",d=\"\";for(let h=0,g=o===1?2:4;h{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Wl({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new QI(o.shape,s,i):new JI(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},KB={kernelName:Rs,backendName:\"webgl\",kernelFunc:N1};var Rit=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,Fit=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = lessThan(floor(b), b);\n bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);\n `+jn+`\n return result;\n`,Oit=ue({opSnippet:Rit,packedOpSnippet:Fit}),jB={kernelName:Fs,backendName:\"webgl\",kernelFunc:Oit};function Pit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=[],l=y.parseAxisParam(s,o.shape),c=l,p=S.getAxesPermutation(c,a),m=o;p!=null&&(m=Pe({inputs:{x:o},backend:e,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,a),u.push(m)),S.assertAxesAreInnerMostDims(\"prod\",c,a);let f;if(e.shouldExecuteOnCPU([m])){let d=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=OL(m.shape,m.dtype,d,c);f=e.makeTensorInfo(g,x,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=rt({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=pc(o.dtype),w=Xn(x,b,\"prod\",e);f=rt({inputs:{x:w},backend:e,attrs:{shape:d}}),u.push(x),u.push(w)}if(i){u.push(f);let d=S.expandShapeToKeepDim(f.shape,l);f=rt({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var XB={kernelName:Ps,backendName:\"webgl\",kernelFunc:Pit};function Mit(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=PL(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],\"int32\",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var YB={kernelName:Hp,backendName:\"webgl\",kernelFunc:Mit};function Lit(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=ML(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],\"int32\",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var ZB={kernelName:qp,backendName:\"webgl\",kernelFunc:Lit};function zit(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=LL(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var JB={kernelName:Kp,backendName:\"webgl\",kernelFunc:zit};var k1=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=zL(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},QB={kernelName:ou,backendName:\"webgl\",kernelFunc:k1};var Bit=\"return 1.0 / x;\",Vit=wt({opSnippet:Bit}),tV={kernelName:Ms,backendName:\"webgl\",kernelFunc:Vit};var Git=yr+`\n return (x < 0.0) ? 0.0 : x;\n`,Wit=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Uit=wt({opSnippet:Git,packedOpSnippet:Wit}),eV={kernelName:Ls,backendName:\"webgl\",kernelFunc:Uit};var Hit=yr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,qit=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Kit=wt({opSnippet:Hit,packedOpSnippet:qit}),rV={kernelName:Vs,backendName:\"webgl\",kernelFunc:Kit};var tC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var eC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function jit(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new eC(o.shape,u,l,s,i):new tC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],\"float32\")}var nV={kernelName:Bs,backendName:\"webgl\",kernelFunc:jit};var rC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function Xit(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new rC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var oV={kernelName:nl,backendName:\"webgl\",kernelFunc:Xit};var nC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":f=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var oC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":f=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function Yit(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new oC(o.shape,u,l,s,i):new nC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var sV={kernelName:zs,backendName:\"webgl\",kernelFunc:Yit};var sC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float sourceFracRow =\n float(${u[0]}) *\n (float(dyR) / float(${l[0]}));\n\n float sourceFracCol =\n float(${u[1]}) *\n (float(dyC) / float(${l[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${o}) - 1),\n ${n} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${n} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function Zit(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new sC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var iV={kernelName:rl,backendName:\"webgl\",kernelFunc:Zit};var iC=class{constructor(t,e){this.variableNames=[\"x\"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${t[0]} - coord - 1));\n }\n `;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(\",\"),i=zt(n);this.userCode=`\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var aC=class{constructor(t,e){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=er(\"rc\",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=zt(n);n===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${t[0]} - rc - 1),\n ${t[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${t[0]} - (rc + 1) - 1),\n ${t[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${a} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${u(o.slice())};\n if(${s}){\n result.g = ${l(o.slice())};\n }\n if(${i}) {\n result.b = ${c(o.slice())};\n if(${s}) {\n result.a = ${p(o.slice())};\n }\n }\n setOutput(result);\n }\n `;function u(d){return m(d)}function l(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",m(d)}function c(d){return d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function p(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function Jit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return rr({inputs:{x:o},backend:e});let u=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new aC(o.shape,a):new iC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var aV={kernelName:Gs,backendName:\"webgl\",kernelFunc:Jit};var lC=class{constructor(t,e){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let n=t[1],o=t[2];this.outputShape=t;let s=\"\";typeof e==\"number\"?s=`float outputValue = ${e.toFixed(2)};`:s=`\n vec3 fill = vec3(${e.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var lV={kernelName:fl,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new lC(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var Qit=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n`,tat=wt({opSnippet:Qit}),uV={kernelName:Ws,backendName:\"webgl\",kernelFunc:tat};var eat=\"return inversesqrt(x);\",rat=wt({opSnippet:eat,cpuKernelImpl:BL}),cV={kernelName:Us,backendName:\"webgl\",kernelFunc:rat};var Yu=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=i;let l=zt(s.length),c=zt(i.length),p=\"\";n===1?p=\"i\":n===2&&(p=\"i, j\");let m=`getIndices(${p})`,f=\"\";o===1?f=\"i\":o===2&&(f=\"i, coords[1]\");let d=`getUpdates(${f})`,h=\"\";u&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=e>1?\"strides[j]\":\"strides\";this.userCode=`\n ${l} strides = ${l}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${t}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${e}; j++) {\n int index = round(${m});\n flattenedIndex += index * ${x};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${d};\n found = true;\n }\n }\n setOutput(mix(${g}, sum, float(found)));\n }\n `}};var uC=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=i;let l=zt(s.length),c=zt(i.length),p=\"\";n===1?p=\"i\":n===2&&(p=\"i, j\");let m=`getIndices(${p})`,f=\"\";o===1?f=\"i\":o===2&&(f=\"i, coords[1]\");let d=`getUpdates(${f})`,h=\"\";u&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=e>1?\"strides[j]\":\"strides\",b=e>1?\"strides[j + 1]\":\"strides\";this.userCode=`\n ${l} strides = ${l}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n vec4 sum = vec4(0.);\n vec4 found = vec4(0.);\n for (int i = 0; i < ${t}; i+=2) {\n ivec2 flattenedIndex = ivec2(0);\n for (int j = 0; j < ${e}; j+=2) {\n ivec4 index = round(${m});\n flattenedIndex += index.xz * ${x};\n if (j + 1 < ${e}) {\n flattenedIndex += index.yw * ${b};\n }\n }\n if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n vec4 updVals = ${d};\n if (flattenedIndex[0] == coords[0]) {\n sum.xy += updVals.xy;\n found.xy = vec2(1.);\n } else if (flattenedIndex[0] == coords[0] + 1) {\n sum.zw += updVals.xy;\n found.zw = vec2(1.);\n }\n if (flattenedIndex[1] == coords[0]) {\n sum.xy += updVals.zw;\n found.xy = vec2(1.);\n } else if (flattenedIndex[1] == coords[0] + 1) {\n sum.zw += updVals.zw;\n found.zw = vec2(1.);\n }\n }\n }\n setOutput(mix(${g}, sum, found));\n }\n `}};function nat(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=rt({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],\"float32\",new Float32Array([0])),g;L().getBool(\"WEBGL_PACK\")?g=new uC(u,a,f.shape.length,d.shape.length,c,m):g=new Yu(u,a,f.shape.length,d.shape.length,c,m);let x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var pV={kernelName:ol,backendName:\"webgl\",kernelFunc:nat};var cC=class{constructor(t,e,n,o){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[t,n];let s=\"while (left < right) {\",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,a=L().getNumber(\"WEBGL_VERSION\")===2?s:i,u=o===\"left\"?\"<\":\"<=\";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${a}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${u} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}};function oat(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=new cC(o.shape[0],o.shape[1],s.shape[1],i),u=[[o.shape[1]]];return e.runWebGLProgram(a,[o,s],\"int32\",u)}var mV={kernelName:il,backendName:\"webgl\",kernelFunc:oat};var pC=class{constructor(t,e,n){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=e;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s=\"resRC\",o=\"resRC\";else{let a=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],u=[],l=[];for(let c=0;c= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}};function sat(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=new pC(n.shape.length,o.shape,o.shape.length);return e.runWebGLProgram(i,[n,o,s],lr(o.dtype,s.dtype))}var fV={kernelName:Bi,backendName:\"webgl\",kernelFunc:sat};var iat=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${S.SELU_SCALEALPHA};\n float scale = ${S.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,aat=wt({opSnippet:iat}),dV={kernelName:Hs,backendName:\"webgl\",kernelFunc:aat};var lat=zo+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,uat=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,cat=wt({opSnippet:lat,packedOpSnippet:uat,cpuKernelImpl:GL}),hV={kernelName:js,backendName:\"webgl\",kernelFunc:cat};var pat=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,mat=wt({opSnippet:pat}),gV={kernelName:Ks,backendName:\"webgl\",kernelFunc:mat};var fat=zo+`\n return sin(x);\n`,dat=`\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${jn}\n return result;\n`,hat=wt({opSnippet:fat,packedOpSnippet:dat}),xV={kernelName:qs,backendName:\"webgl\",kernelFunc:hat};var gat=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,xat=wt({opSnippet:gat}),yV={kernelName:al,backendName:\"webgl\",kernelFunc:xat};var yat=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`,bat=wt({opSnippet:yat}),bV={kernelName:Xs,backendName:\"webgl\",kernelFunc:bat};var wat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;y.assert(o.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let a=s.reduce((x,b)=>x*b),u=[[0,0]];u.push(...i);for(let x=1+s.length;xe.disposeIntermediateTensorInfo(x)),g},wV={kernelName:Gi,backendName:\"webgl\",kernelFunc:wat};function Iat(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n ${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:\n ${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n ${i.shape}`);let a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=UL(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],\"bool\",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var IV={kernelName:su,backendName:\"webgl\",kernelFunc:Iat};function Cat(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=HL(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var CV={kernelName:ll,backendName:\"webgl\",kernelFunc:Cat};function vat(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=qw(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var vV={kernelName:iu,backendName:\"webgl\",kernelFunc:vat};function Sat(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at 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e.disposeIntermediateTensorInfo(h),g}var NV={kernelName:ul,backendName:\"webgl\",kernelFunc:Nat};function kat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=vi({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var kV={kernelName:Wi,backendName:\"webgl\",kernelFunc:kat};var TV=\"return sqrt(x);\",Tat=wt({opSnippet:TV,packedOpSnippet:TV,cpuKernelImpl:qL}),_V={kernelName:Ys,backendName:\"webgl\",kernelFunc:Tat};var _at=\"return x * x;\",Eat=wt({opSnippet:_at}),EV={kernelName:lu,backendName:\"webgl\",kernelFunc:Eat};var AV=\"return (a - b) * (a - b);\",Aat=ue({opSnippet:AV,packedOpSnippet:AV}),DV={kernelName:Qs,backendName:\"webgl\",kernelFunc:Aat};function Dat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;if(o.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");let s=e.readSync(o.dataId),i=S.fromUint8ToStringArray(s),a=KL(i,\"string\",n);return e.makeTensorInfo(o.shape,\"string\",a)}var $V={kernelName:oc,backendName:\"webgl\",kernelFunc:Dat};function $at({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=yr+`\n return x > 0.0 ? 1.0 : float(${t.alpha});\n `,s=new zr(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var RV={kernelName:xo,backendName:\"webgl\",kernelFunc:$at};var mC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=n;let o=n.length,s=zt(n.length),i=zt(n.length),a=\"\";if(o===1)a=\"coords * strides + begin\";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${t});\n ${s} strides = ${s}(${e});\n\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${a}));\n }\n `}};function Rat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:I}=ze.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=rt({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let A=ze.computeOutShape(b,w,I),D=vi({inputs:{x:o},backend:e,attrs:{begin:b,size:A}});N=rt({inputs:{x:D},backend:e,attrs:{shape:d}}),e.disposeIntermediateTensorInfo(D)}else if(e.shouldExecuteOnCPU([o])){let D=e.readSync(o.dataId),F=bt(o.shape,o.dtype,D),P=jL(f,F,I,b);N=e.makeTensorInfo(d,o.dtype,P.values)}else{let D=new mC(b,I,f);N=e.runWebGLProgram(D,[o],o.dtype)}let E=rt({inputs:{x:N},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(N),E}var FV={kernelName:cl,backendName:\"webgl\",kernelFunc:Rat};function Fat(r){let{inputs:t,backend:e,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:i,rightPad:a,padWidth:u,preserveShortSequences:l}=n,{data:c,dataSplits:p}=t,m=e.readSync(c.dataId),f=e.readSync(p.dataId),[d,h]=XL(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],\"string\",d),e.makeTensorInfo(p.shape,\"int32\",h)]}var OV={kernelName:uu,backendName:\"webgl\",kernelFunc:Fat};function Oat(r){let{inputs:t,backend:e,attrs:n}=r,{skipEmpty:o}=n,{input:s,delimiter:i}=t;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let a=e.readSync(s.dataId),u=e.readSync(i.dataId)[0],[l,c,p]=YL(a,u,o),m=c.length;return[e.makeTensorInfo([m,2],\"int32\",l),e.makeTensorInfo([m],\"string\",c),e.makeTensorInfo([2],\"int32\",new Int32Array(p))]}var PV={kernelName:cu,backendName:\"webgl\",kernelFunc:Oat};function Pat(r){let{inputs:t,backend:e,attrs:n}=r,{numBuckets:o}=n,{input:s}=t;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(o<=0)throw new Error(\"Number of buckets must be at least 1\");let i=e.readSync(s.dataId),a=ZL(i,o);return e.makeTensorInfo(s.shape,\"int32\",a)}var MV={kernelName:pu,backendName:\"webgl\",kernelFunc:Pat};var Mat=\"return tan(x);\",Lat=wt({opSnippet:Mat}),LV={kernelName:ei,backendName:\"webgl\",kernelFunc:Lat};var zat=`\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,Bat=wt({opSnippet:zat}),zV={kernelName:ri,backendName:\"webgl\",kernelFunc:Bat};function Vat(r){let{inputs:t,backend:e,attrs:n}=r,{tensor:o,indices:s,updates:i}=t,{}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(i,s,o.shape),m=[p/l,l];if(p===0)return e.makeTensorInfo(o.shape,s.dtype);let f=rt({inputs:{x:s},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:i},backend:e,attrs:{shape:[u,l]}}),h=rt({inputs:{x:o},backend:e,attrs:{shape:m}}),g=new Yu(u,a,f.shape.length,d.shape.length,c,m,!1,!0),x=e.runWebGLProgram(g,[d,f,h],h.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:o.shape}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(x),b}var BV={kernelName:sl,backendName:\"webgl\",kernelFunc:Vat};var fC=class{constructor(t,e){this.variableNames=[\"A\"];let n=new Array(t.length);for(let i=0;i5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],n=[];for(let o=0;o5){let u=e.readSync(o.dataId),l=o.dtype===\"string\"?u.map(m=>y.decodeString(m)):u,c=bt(o.shape,o.dtype,l),p=QL(c,s);return e.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new fC(o.shape,s);return e.runWebGLProgram(i,[o],o.dtype)}var VV={kernelName:no,backendName:\"webgl\",kernelFunc:T1};var dC=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n `}},hC=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function Ip(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function GV(r){let t=1;for(;tu){let P=e.readSync(o.dataId),[V,G]=tz(P,l,o.dtype,s,i);return[e.makeTensorInfo(V.shape,V.dtype,V.values),e.makeTensorInfo(G.shape,G.dtype,G.values)]}if(s===0)return l[l.length-1]=0,[e.makeTensorInfo(l,o.dtype,[]),e.makeTensorInfo(l,\"int32\",[])];if(c===1)return[o,Wl({attrs:{shape:l,dtype:\"int32\",value:0},backend:e})];let p=e.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?e.unpackTensor(o):o,h=y.sizeFromShape(l)/c,g=rt({inputs:{x:f},attrs:{shape:[h,c]},backend:e});m&&Ip(e,f);let x=GV(s),b=GV(c),w=null,I=()=>w===null?[g,g]:[g,w],N=(P,V,G)=>{let W=I(),q=new dC(G),K=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[P],[V]],X=w;w=e.runWebGLProgram(q,W,\"int32\",K),Ip(e,X)};for(let P=1;P=1;G/=2)N(V,G,[h,b])}for(let P=b;P>x;P/=2){let V=I(),G=new hC([h,P/2]),q=[[c],[w===null?1:0],[x]],H=w;w=e.runWebGLProgram(G,V,\"int32\",q),Ip(e,H);let K=x/2,X=K*2;for(let Z=K;Z>=1;Z/=2)N(X,Z,w.shape)}let E=w;w=vi({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),Ip(e,E);let A=w1({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});Ip(e,g);let D=l.slice(0,-1);D.push(s),E=w,w=rt({inputs:{x:w},attrs:{shape:D},backend:e}),Ip(e,E);let F=A;return A=rt({inputs:{x:A},attrs:{shape:D},backend:e}),Ip(e,F),[A,w]}var WV={kernelName:pl,backendName:\"webgl\",kernelFunc:Wat};var gC=class{constructor(t,e,n,o,s,i){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=i;let a=n===\"nearest\"?1:2,u;switch(o){case\"constant\":u=1;break;case\"reflect\":u=2;break;case\"wrap\":u=3;break;case\"nearest\":u=4;break;default:u=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${u} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${t} && 0 <= coordX && coordX < ${e}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = getTransforms(batch, 2);\n float b1 = getTransforms(batch, 3);\n float b2 = getTransforms(batch, 4);\n float b3 = getTransforms(batch, 5);\n float c1 = getTransforms(batch, 6);\n float c2 = getTransforms(batch, 7);\n float projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = float(${s});\n } else {\n float inX = (a1 * xf + a2 * yf + a3) / projection;\n float inY = (b1 * xf + b2 * yf + b3) / projection;\n float mapX = mapCoord(inX, float(${e}));\n float mapY = mapCoord(inY, float(${t}));\n\n if (${a} == 1) {\n int coordY = int(round(mapY));\n int coordX = int(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n float yFloor = floor(mapY);\n float xFloor = floor(mapX);\n float yCeil = yFloor + 1.0;\n float xCeil = xFloor + 1.0;\n float valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n float valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function Uat(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new gC(p,m,i,a,u,g);return e.runWebGLProgram(x,[o,s],\"float32\")}var UV={kernelName:ml,backendName:\"webgl\",kernelFunc:Uat};function Hat(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;wi(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let i=n.readSync(s.dataId),{outputValues:a,outputShape:u,indices:l}=ez(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],\"int32\",l)]}var HV={kernelName:mu,backendName:\"webgl\",kernelFunc:Hat};function qat(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o,a=i.shape.length,u=o.shape[s],l=new Array(a-1),c=0;for(let h=0;he.disposeIntermediateTensorInfo(h)),d}var qV={kernelName:Ui,backendName:\"webgl\",kernelFunc:qat};var xC=class{constructor(t,e){this.variableNames=[\"x\",\"segmentIds\"];let n=t.windowSize,o=t.batchSize,s=t.inSize,i=t.numSegments,a=i*Math.ceil(s/n);this.outputShape=[o,a];let u=\"0.0\",l=\"sumValue\",c=Math.floor(n/4)*4,p=n%4,m=`\n sumValue += dot(values, segFilter);\n `,f=\"\";s%n>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let d=\"\";s%n>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${u};\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${d}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${i})) * float(${n}));\n int currentSeg = int(mod(float(outIdx), float(${i})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${p===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function Kat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n,a=o.shape.length,u=[],l=0,c=S.getAxesPermutation([l],a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,a)[0]);let m=S.segment_util.computeOutShape(p.shape,l,i),f=y.sizeFromShape([p.shape[l]]),d=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,f]}});u.push(d);let h=pc(o.dtype),g=(I,N,E,A,D)=>{let F=I.shape[0],P=I.shape[1],V=S.segment_util.segOpComputeOptimalWindowSize(P,D),G={windowSize:V,inSize:P,batchSize:F,numSegments:D},W=new xC(G,N),q=e.compileAndRun(W,[I,E],A);if(u.push(q),q.shape[1]===D)return q;let H=k1({backend:e,attrs:{start:0,stop:D,step:1,dtype:\"float32\"}}),K=T1({inputs:{x:H},backend:e,attrs:{reps:[P/V]}});return u.push(H),u.push(K),g(q,N,K,A,D)},x=g(d,\"unsortedSegmentSum\",s,h,i),b=rt({inputs:{x},backend:e,attrs:{shape:m}}),w=b;if(c!=null){u.push(b);let I=S.getUndoAxesPermutation(c);w=Pe({inputs:{x:w},backend:e,attrs:{perm:I}})}return u.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}var KV={kernelName:fu,backendName:\"webgl\",kernelFunc:Kat};var jat=[Az,$z,Rz,Fz,Pz,Mz,Lz,zz,Gz,Wz,Uz,Hz,qz,Kz,jz,Xz,Yz,Zz,Jz,Qz,t3,r3,n3,o3,l3,c3,p3,wz,f3,h3,g3,x3,y3,b3,w3,I3,C3,v3,S3,T3,_3,E3,A3,D3,$3,R3,F3,O3,P3,M3,L3,z3,B3,V3,G3,U3,H3,q3,K3,X3,Y3,Z3,J3,Q3,tB,eB,rB,nB,bz,oB,d3,sB,iB,aB,Iz,lB,uB,cB,pB,mB,fB,dB,hB,gB,xB,bB,wB,IB,CB,vB,SB,kB,_B,EB,AB,DB,$B,MB,Sz,LB,zB,BB,VB,s3,GB,HB,qB,KB,jB,Cz,XB,YB,ZB,JB,QB,i3,RB,tV,eV,rV,kz,nV,oV,sV,iV,aV,lV,uV,cV,pV,mV,fV,dV,hV,gV,xV,yV,e3,PB,bV,wV,IV,CV,vV,SV,NV,kV,_V,EV,DV,$V,RV,FV,OV,PV,MV,OB,_z,LV,zV,BV,VV,WV,UV,Ez,HV,qV,KV,WB];for(let r of jat)sc(r);var kt;(function(r){r[r.float32=0]=\"float32\",r[r.int32=1]=\"int32\",r[r.bool=2]=\"bool\",r[r.string=3]=\"string\",r[r.complex64=4]=\"complex64\"})(kt||(kt={}));var Zu;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(Zu||(Zu={}));var jV;function Xat(r){jV=r.wasm.cwrap(qi,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Yat(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t;if(o.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=0;if(i!=null){let D=e.dataIdMap.get(i.dataId);if(D.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${D.shape.length}.`);d=D.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=Zu[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=u?o.shape[2]:o.shape[1],b=l?s.shape[1]:s.shape[2],w=Ur.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)),I=e.makeOutput([...w,x,b],o.dtype),N=e.dataIdMap.get(I.dataId).id,E=new Uint8Array(new Int32Array(o.shape).buffer),A=new Uint8Array(new Int32Array(s.shape).buffer);return jV(m,E,o.shape.length,f,A,s.shape.length,u,l,g,d,h,p||0,N),I}var XV={kernelName:qi,backendName:\"wasm\",setupFunc:Xat,kernelFunc:Yat};function Ct(r,t){let e;function n(s){e=s.wasm.cwrap(r,null,[\"number\",\"number\",\"number\"])}function o(s){let{backend:i,inputs:{x:a}}=s,u=i.dataIdMap.get(a.dataId).id,l=i.makeOutput(a.shape,t||a.dtype),c=i.dataIdMap.get(l.dataId).id;return y.sizeFromShape(l.shape)===0||e(u,kt[a.dtype],c),l}return{kernelName:r,backendName:\"wasm\",setupFunc:n,kernelFunc:o}}var YV=Ct(Ti);var ZV=Ct(Uo);var JV=Ct(Ho);function ae(r,t,e){let n;function o(i){n=i.wasm.cwrap(r,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function s(i){let{backend:a,inputs:u}=i,{a:l,b:c}=u,p=a.dataIdMap.get(l.dataId).id,m=a.dataIdMap.get(c.dataId).id,f=e!=null?e:l.dtype,d=S.assertAndGetBroadcastShape(l.shape,c.shape),h=a.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(l.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=a.dataIdMap.get(h.dataId).id;return(()=>n(p,g,l.shape.length,m,x,c.shape.length,kt[l.dtype],b))(),h}return{kernelName:r,backendName:\"wasm\",setupFunc:o,kernelFunc:s}}var Zat=!0,QV=ae(ro,Zat);var tG;function Jat(r){tG=r.wasm.cwrap(qo,null,[\"array\",\"number\",\"number\",\"number\"])}function Qat(r){let{inputs:t,backend:e}=r,n=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(n.shape)===0)return n;let o=t.map(a=>e.dataIdMap.get(a.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),i=e.dataIdMap.get(n.dataId).id;return tG(s,o.length,kt[n.dtype],i),n}var eG={kernelName:qo,backendName:\"wasm\",setupFunc:Jat,kernelFunc:Qat};function Cp(r){let{inputs:{x:t},backend:e}=r;if(t.dtype===\"string\")return or(e.readSync(t.dataId),t.shape,t.dtype);let n=e.makeOutput(t.shape,t.dtype),o=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(n).set(o),n}var rG={kernelName:go,backendName:\"wasm\",kernelFunc:Cp};var nG;function tlt(r){nG=r.wasm.cwrap(oo,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function mo(r){let{inputs:t,backend:e,attrs:n}=r,[o,s]=rlt(t.x.shape,n.perm),i=!0;for(let d=0;d=o&&(s===-1||n[s]>n[i])&&(s=i);n[s]=o}return[e,n]}var oG={kernelName:oo,backendName:\"wasm\",kernelFunc:mo,setupFunc:tlt};function In(r,t,e){let n=r.shape,o=r.shape.length,s=y.parseAxisParam(t,n),i=s,a=S.getAxesPermutation(i,o),u=null,l=!1;if(a!=null){let c=new Array(o);for(let f=0;f`new shape: ${i}, old shape: ${n.shape}. 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t.dtype===\"string\"?p.stringBytes=u.slice(d,d+y.sizeFromShape(i)):o.typedArrayFromHeap(l).set(u.subarray(d,d+y.sizeFromShape(i))),l}if(t.dtype===\"string\"){let d=sp(u,s,i,t.shape,t.dtype);return p.stringBytes=d,l}let m=o.typedArrayFromHeap(l),f=t.shape.length;if(f===2)hlt(u,c[0],m,s,i);else if(f===3)glt(u,c[0],c[1],m,s,i);else if(f===4)xlt(u,c[0],c[1],c[2],m,s,i);else{let d=sp(u,s,i,t.shape,t.dtype);m.set(d)}return l}function hlt(r,t,e,n,o){let s=0,i=n[0],a=n[1],u=i+o[0];for(let l=i;lx*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=pr({inputs:{x:o},backend:e,attrs:{shape:u}}),d=mo({inputs:{x:f},backend:e,attrs:{perm:l}}),h=pr({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Bo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeData(f.dataId),e.disposeData(d.dataId),e.disposeData(f.dataId),g}var kG={kernelName:Di,backendName:\"wasm\",kernelFunc:ylt};var TG;function blt(r){TG=r.wasm.cwrap(Da,null,[\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\"])}function wlt(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i}=n,a=s.shape.reduce((p,m)=>p*m,1)!==0,u=o.shape.length===1?[i]:[o.shape[0],i],l=t.makeOutput(u,s.dtype);function c(p){return t.dataIdMap.get(p.dataId).id}return TG(c(o),i,a,c(s),kt[s.dtype],c(l)),l}var _G={kernelName:Da,backendName:\"wasm\",setupFunc:blt,kernelFunc:wlt};function Ilt(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.typedArrayFromHeap(n),i=e.typedArrayFromHeap(o),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeOutput([a.length],\"int32\",void 0,new Int32Array(a))}var EG={kernelName:Yl,backendName:\"wasm\",kernelFunc:Ilt};function Rn(r){let{inputs:{x:t},attrs:{dtype:e},backend:n}=r,o=n.makeOutput(t.shape,e),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(o).set(s),o}var AG={kernelName:fo,backendName:\"wasm\",kernelFunc:Rn};var DG=Ct(ts);var $G;function Clt(r){$G=r.wasm.cwrap(ho,null,[\"number\",\"number\",\"number\",\"number\"])}function vlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a=e.dataIdMap.get(o.dataId).id,u=e.makeOutput(o.shape,o.dtype),l=e.dataIdMap.get(u.dataId).id;return $G(a,s,i,l),u}var RG={kernelName:ho,backendName:\"wasm\",setupFunc:Clt,kernelFunc:vlt};function _1(r){let{inputs:t,backend:e}=r,n=y.parseAxisParam(r.attrs.axis,t[0].shape)[0],o=t.map(f=>f.shape);S.assertParamsConsistent(o,n);let s=S.computeOutShape(t.map(f=>f.shape),n),i=t.filter(f=>y.sizeFromShape(f.shape)>0);if(i.length===1)return Cp({inputs:{x:i[0]},backend:e});let a=e.makeOutput(s,t[0].dtype);if(y.sizeFromShape(s)===0)return a;if(i[0].dtype===\"string\"){let f=i.map(w=>{let N=[-1,y.sizeFromShape(w.shape.slice(n))];return pr({inputs:{x:w},backend:e,attrs:{shape:N}})}),d=f.map(w=>({vals:e.readSync(w.dataId),shape:w.shape}));s=S.computeOutShape(f.map(w=>w.shape),1);let h=f[0].shape[0]===1,g=rp(d,s,t[0].dtype,h),x=S.computeOutShape(i.map(w=>w.shape),n);a.shape=x;let b=e.dataIdMap.get(a.dataId);return b.stringBytes=S.fromStringArrayToUint8(g),f.forEach(w=>e.disposeData(w.dataId)),a}let u=y.sizeFromShape(i[0].shape.slice(0,n)),l=0,c=i.map(f=>{let d=y.sizeFromShape(f.shape.slice(n));return l+=d,d}),p=i.map(f=>e.typedArrayFromHeap(f)),m=e.typedArrayFromHeap(a);for(let f=0;f`cumprod does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=mo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims(\"cumprod\",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;XG(d,i?1:0,a?1:0,f,h,kt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var YG={kernelName:Fa,backendName:\"wasm\",setupFunc:Plt,kernelFunc:Mlt};var ZG;function Llt(r){ZG=r.wasm.cwrap(is,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function zlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype===\"float32\"||o.dtype===\"int32\",()=>`cumsum does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=mo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims(\"cumsum\",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;ZG(d,i?1:0,a?1:0,f,h,kt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var JG={kernelName:is,backendName:\"wasm\",setupFunc:Llt,kernelFunc:zlt};var QG;function Blt(r){QG=r.wasm.cwrap(\"DenseBincount\",null,[\"number\",\"array\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"boolean\",\"number\"])}function Vlt(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i,binaryOutput:a}=n,u=s.shape.reduce((m,f)=>m*f,1)!==0,l=o.shape.length===1?[i]:[o.shape[0],i],c=t.makeOutput(l,s.dtype);function p(m){return t.dataIdMap.get(m.dataId).id}return QG(p(o),new Uint8Array(new Int32Array(o.shape).buffer),o.shape.length,i,u,p(s),kt[s.dtype],a,p(c)),c}var tW={kernelName:Jl,backendName:\"wasm\",setupFunc:Blt,kernelFunc:Vlt};var eW;function Glt(r){eW=r.wasm.cwrap(Pa,null,[\"number\",\"number\",\"number\",\"array\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function Wlt(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i===\"NHWC\"?o.shape[1]:o.shape[2],l=i===\"NHWC\"?o.shape[2]:o.shape[3],c=i===\"NHWC\"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i===\"NHWC\"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,\"float32\"),x=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),I=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),N=t.dataIdMap.get(h.dataId).id;return eW(x,s,i===\"NHWC\"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var rW={kernelName:Pa,backendName:\"wasm\",setupFunc:Glt,kernelFunc:Wlt};var nW;function Ult(r){nW=r.wasm.cwrap(as,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Hlt(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,I=f.dilationHeight,N=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,D=f.inChannels,F=f.outChannels,P=f.padInfo.type===\"SAME\"?1:0;if(f.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let V=n.makeOutput(f.outShape,\"float32\"),G=n.dataIdMap.get(V.dataId).id;return nW(i,o.shape[0],o.shape[1],o.shape[2],a,d,h,g,x,b,w,P,I,N,E,A,D,F,G),V}var oW={kernelName:as,backendName:\"wasm\",setupFunc:Ult,kernelFunc:Hlt};var sW;function qlt(r){sW=r.wasm.cwrap(\"Diag\",null,[\"number\",\"number\",\"number\",\"number\"])}function Klt(r){let{inputs:t,backend:e}=r,{x:n}=t,o=y.sizeFromShape(n.shape),s=e.makeOutput([...n.shape,...n.shape],n.dtype);return sW(e.dataIdMap.get(n.dataId).id,kt[n.dtype],o,e.dataIdMap.get(s.dataId).id),s}var iW={kernelName:Ql,backendName:\"wasm\",setupFunc:qlt,kernelFunc:Klt};var aW;function jlt(r){aW=r.wasm.cwrap(ls,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Xlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;if(o.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${o.dtype} and ${s.dtype}`);let l=S.computeDilation2DInfo(o.shape,s.shape,i,a,\"NHWC\",u),c=e.makeOutput(l.outShape,o.dtype);return aW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,kt[o.dtype],l.batchSize,l.inChannels,l.inHeight,l.inWidth,l.outHeight,l.outWidth,l.strideHeight,l.strideWidth,l.dilationHeight,l.dilationWidth,l.filterHeight,l.filterWidth,l.padInfo.top,l.padInfo.left),c}var lW={kernelName:ls,backendName:\"wasm\",setupFunc:jlt,kernelFunc:Xlt};var uW;function Ylt(r){uW=r.wasm.cwrap(eu,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Zlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,\"NHWC\",l),p=e.makeOutput(s.shape,s.dtype);return uW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,kt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var cW={kernelName:eu,backendName:\"wasm\",setupFunc:Ylt,kernelFunc:Zlt};var pW;function Jlt(r){pW=r.wasm.cwrap(tu,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Qlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,\"NHWC\",l),p=e.makeOutput(o.shape,o.dtype);return pW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,kt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var mW={kernelName:tu,backendName:\"wasm\",setupFunc:Jlt,kernelFunc:Qlt};var fW=Ct(cs);var dW;function tut(r){dW=r.wasm.cwrap(Ma,null,[\"number\",\"number\",\"number\"])}function eut(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=e.makeOutput(o.shape,\"float32\"),i=a=>e.dataIdMap.get(a.dataId).id;return dW(i(o),i(n),i(s)),s}var hW={kernelName:Ma,backendName:\"wasm\",setupFunc:tut,kernelFunc:eut};var rut=!1,gW=ae(za,rut,\"bool\");var xW=Ct(ps,\"float32\");function bC(r){let{inputs:t,attrs:e,backend:n}=r,{input:o}=t,{dim:s}=e,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),pr({inputs:{x:o},backend:n,attrs:{shape:a}})}var yW={kernelName:Ri,backendName:\"wasm\",kernelFunc:bC};var bW=Ct(ms,\"float32\");function A1(r){let{attrs:{shape:t,value:e,dtype:n},backend:o}=r,s=o.makeOutput(t,n);return o.typedArrayFromHeap(s).fill(e),s}var wW={kernelName:ru,backendName:\"wasm\",kernelFunc:A1};var IW;function nut(r){IW=r.wasm.cwrap(Ba,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function out(r){let{inputs:t,backend:e}=r,{image:n}=t,o=e.makeOutput(n.shape,n.dtype),s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,[a,u,l,c]=n.shape;return IW(s,a,u,l,c,i),o}var CW={kernelName:Ba,backendName:\"wasm\",kernelFunc:out,setupFunc:nut};var vW=Ct(fs);var sut=!1,SW=ae(ds,sut);var NW;function iut(r){NW=r.wasm.cwrap(hs,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function aut(r){let{backend:t,inputs:e,attrs:n}=r,{varianceEpsilon:o}=n,{x:s,mean:i,variance:a,offset:u,scale:l}=e,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,m=t.dataIdMap.get(a.dataId).id,f=u!=null?t.dataIdMap.get(u.dataId).id:0,d=l!=null?t.dataIdMap.get(l.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return NW(c,p,m,f,d,o,g),h}var kW={kernelName:hs,backendName:\"wasm\",setupFunc:iut,kernelFunc:aut};var TW;function lut(r){TW=r.wasm.cwrap(Ki,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function uut(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m),g=Zu[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,P=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,K=h.padInfo.type===\"SAME\"?1:0,X=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,\"float32\"),st=n.dataIdMap.get(nt.dataId).id,at=a==null?0:n.dataIdMap.get(a.dataId).id;return TW(x,X,Z,et,b,N,E,I,A,D,F,P,K,V,G,W,q,H,w,g,at,d||0,st),nt}var _W={kernelName:Ki,backendName:\"wasm\",setupFunc:lut,kernelFunc:uut};var EW;function cut(r){EW=r.wasm.cwrap(ji,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 put(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m,!0),g=Zu[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,P=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,K=h.padInfo.type===\"SAME\"?1:0,X=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,\"float32\"),st=n.dataIdMap.get(nt.dataId).id,at=a==null?0:n.dataIdMap.get(a.dataId).id;return EW(x,X,Z,et,b,N,E,I,A,D,F,P,K,V,G,W,q,H,w,g,at,d||0,st),nt}var AW={kernelName:ji,backendName:\"wasm\",setupFunc:cut,kernelFunc:put};var DW;function mut(r){DW=r.wasm.cwrap(Va,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function fut(r){let{backend:t,inputs:e}=r,{params:n,indices:o}=e,[s,i,a,u]=Ny.prepareAndValidate(n,o),l=t.makeOutput(s,n.dtype);if(i===0)return l;let c=o.shape,p=c[c.length-1],f=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(o.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),x=t.dataIdMap.get(l.dataId).id;return DW(f,kt[n.dtype],h,i,p,a,g,x),l}var $W={kernelName:Va,backendName:\"wasm\",setupFunc:mut,kernelFunc:fut};var RW;function dut(r){RW=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function hut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,indices:s}=e,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0],l=t.readSync(s.dataId),c=o.shape[u];for(let F=0;F=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),m=pr({inputs:{x:o},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),f=y.sizeFromShape(s.shape),d=pr({inputs:{x:s},attrs:{shape:[p.batchSize,f/p.batchSize]},backend:t}),h=[p.batchSize,p.outerSize,f/p.batchSize,p.sliceSize],g=t.makeOutput(h,o.dtype);if(y.sizeFromShape(o.shape)===0)return g;let x=m.shape.length-1,w=t.dataIdMap.get(m.dataId).id,N=t.dataIdMap.get(d.dataId).id,E=t.dataIdMap.get(g.dataId).id,A=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),D=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return RW(w,kt[o.dtype],A,x,N,p.batchSize,D,E),t.disposeData(m.dataId),t.disposeData(d.dataId),g.shape=p.outputShape,g}var FW={kernelName:Fi,backendName:\"wasm\",setupFunc:dut,kernelFunc:hut};var gut=!1,OW=ae(Ga,gut,\"bool\");var xut=!1,PW=ae(gs,xut,\"bool\");var MW=Ct(xs,\"bool\");var LW=Ct(ys,\"bool\");var zW=Ct(bs,\"bool\");var BW;function yut(r){BW=r.wasm.cwrap(ws,null,[\"number\",\"number\",\"number\",\"number\"])}function but(r){let{inputs:{x:t},attrs:{alpha:e},backend:n}=r,o=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,\"float32\");if(y.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;BW(o,kt[t.dtype],e,i)}return s}var VW={kernelName:ws,backendName:\"wasm\",setupFunc:yut,kernelFunc:but};var wut=!1,GW=ae(Wa,wut,\"bool\");var Iut=!1,WW=ae(Ua,Iut,\"bool\");var UW;function Cut(r){UW=r.wasm.cwrap(Ha,null,[\"number\",\"number\",\"number\",\"number\"])}function vut(r){let{attrs:t,backend:e}=r,{start:n,stop:o,num:s}=t,i=Math.floor(s),a=e.makeOutput([i],\"float32\");return UW(e.dataIdMap.get(a.dataId).id,n,o,i),a}var HW={kernelName:Ha,backendName:\"wasm\",setupFunc:Cut,kernelFunc:vut};var qW=Ct(Is);var KW=Ct(Cs);var Sut=!1,jW=ae(qa,Sut,\"bool\");var XW=Ct(Ka);var Nut=!1,YW=ae(ja,Nut,\"bool\");var kut=!1,ZW=ae(l_,kut,\"bool\");var JW;function Tut(r){JW=r.wasm.cwrap(vs,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function _ut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;if(o.dtype!==\"float32\")throw new Error(\"LRN error: x must have dtype float32\");let l=e.makeOutput(o.shape,o.dtype);return JW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(l.dataId).id,o.shape[3],s,i,a,u),l}var QW={kernelName:vs,backendName:\"wasm\",setupFunc:Tut,kernelFunc:_ut};var tU;function Eut(r){tU=r.wasm.cwrap(Xa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Aut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n;if(o.dtype!==\"float32\"||s.dtype!==\"float32\"||i.dtype!==\"float32\")throw new Error(\"LRNGrad error: x, y, and dy must have dtype float32\");let p=e.makeOutput(o.shape,o.dtype);return tU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,i.shape[3],a,u,l,c),p}var eU={kernelName:Xa,backendName:\"wasm\",setupFunc:Eut,kernelFunc:Aut};var rU;function Dut(r){rU=r.wasm.cwrap(Ss,null,[\"number\",\"number\",\"number\",\"number\"])}function $ut(r){let{backend:t,inputs:e,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=In(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims(\"max\",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;rU(u,kt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var nU={kernelName:Ss,backendName:\"wasm\",setupFunc:Dut,kernelFunc:$ut};var Rut=!1,oU=ae(Ns,Rut);var sU;function Fut(r){sU=r.wasm.cwrap(ks,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Out(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id;y.assert(o.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${o.dtype}.`);let{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=e,c=S.computePool2DInfo(o.shape,i,a,1,u,l),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,w=c.strideHeight,I=c.strideWidth,N=c.inChannels,E=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let A=n.makeOutput(c.outShape,\"float32\"),D=n.dataIdMap.get(A.dataId).id;return sU(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,b,w,I,N,E,D),A}var iU={kernelName:ks,backendName:\"wasm\",setupFunc:Fut,kernelFunc:Out};var aU;function Put(r){aU=r.wasm.cwrap(\"MaxPool3D\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Mut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.makeOutput(c.outShape,o.dtype);return aU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var lU={kernelName:Oi,backendName:\"wasm\",setupFunc:Put,kernelFunc:Mut};var uU;function Lut(r){uU=r.wasm.cwrap(\"MaxPool3DGrad\",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 zut(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return uU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var cU={kernelName:nu,backendName:\"wasm\",setupFunc:Lut,kernelFunc:zut};var pU;function But(r){pU=r.wasm.cwrap(Ts,null,[\"number, number, number\"])}function Vut(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=In(i,o,t),d=p;if(f){let I=t.dataIdMap.get(c.dataId).id;I!==a&&(l=c,u=I,d=S.getInnerMostAxes(d.length,l.shape.length))}S.assertAxesAreInnerMostDims(\"mean\",d,l.shape.length);let[h,g]=S.computeOutAndReduceShapes(l.shape,d),x=y.sizeFromShape(g),b=l;l.dtype!==\"float32\"&&(b=Rn({backend:t,inputs:{x:l},attrs:{dtype:\"float32\"}}),u=t.dataIdMap.get(b.dataId).id);let w=t.makeOutput(h,\"float32\");if(y.sizeFromShape(l.shape)!==0){let I=t.dataIdMap.get(w.dataId).id;pU(u,x,I)}if(f&&t.disposeData(c.dataId),s){let I=S.expandShapeToKeepDim(w.shape,m);w.shape=I}return l.dtype!==\"float32\"&&t.disposeData(b.dataId),w}var mU={kernelName:Ts,backendName:\"wasm\",setupFunc:But,kernelFunc:Vut};var fU;function Gut(r){fU=r.wasm.cwrap(_s,null,[\"number\",\"number\",\"number\",\"number\"])}function Wut(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=In(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;w!==a&&(l=c,u=w)}let d=l.shape.length;S.assertAxesAreInnerMostDims(\"min\",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,l.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;fU(u,kt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var dU={kernelName:_s,backendName:\"wasm\",setupFunc:Gut,kernelFunc:Wut};var Uut=!1,hU=ae(Es,Uut);var D1;(function(r){r[r.reflect=0]=\"reflect\",r[r.symmetric=1]=\"symmetric\"})(D1||(D1={}));var gU;function Hut(r){gU=r.wasm.cwrap(As,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function qut(r){let{inputs:{x:t},backend:e,attrs:{paddings:n,mode:o}}=r,s=n.map((d,h)=>d[0]+t.shape[h]+d[1]),i=e.dataIdMap.get(t.dataId).id,a=e.makeOutput(s,t.dtype),u=e.dataIdMap.get(a.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=n.map(d=>d[0]),p=n.map(d=>d[1]),m=new 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BroadcastArgs,a_ as BroadcastTo,Db as Callback,Uy as CallbackList,fo as Cast,ts as Ceil,ho as ClipByValue,Op as Complex,Zl as ComplexAbs,$i as Concat,es as Conv2D,Pp as Conv2DBackpropFilter,rs as Conv2DBackpropInput,ns as Conv3D,$a as Conv3DBackpropFilterV2,Ra as Conv3DBackpropInputV2,os as Cos,ss as Cosh,Oa as CropAndResize,Fa as Cumprod,is as Cumsum,qy as CustomCallback,Ta as DataStorage,Jl as DenseBincount,Pa as DepthToSpace,as as DepthwiseConv2dNative,Mp as DepthwiseConv2dNativeBackpropFilter,Lp as DepthwiseConv2dNativeBackpropInput,Ql as Diag,ls as Dilation2D,eu as Dilation2DBackpropFilter,tu as Dilation2DBackpropInput,r0 as ENV,$b as EarlyStopping,zp as Einsum,cs as Elu,Ma as EluGrad,th as Environment,za as Equal,La as Erf,ps as Exp,Ri as ExpandDims,ms as Expm1,Bp as FFT,ru as Fill,Ba as FlipLeftRight,fs as Floor,ds as FloorDiv,rh as FromPixels,hs as FusedBatchNorm,Ki as FusedConv2D,ji as FusedDepthwiseConv2D,hp as GPGPUContext,Va as GatherNd,Fi as GatherV2,Uh as GraphModel,Ga as Greater,gs as GreaterEqual,Hy as History,Vp as IFFT,go as Identity,Gp as Imag,Ie as InputSpec,xs as IsFinite,ys as IsInf,bs as IsNan,Go as KernelBackend,vs as LRN,Xa as LRNGrad,Eh as LayerVariable,Un as LayersModel,ws as LeakyRelu,Wa as Less,Ua as LessEqual,Ha as LinSpace,Is as Log,Cs as Log1p,u_ as LogSoftmax,qa as LogicalAnd,Ka as LogicalNot,ja as LogicalOr,l_ as LogicalXor,Ppt as LowerBound,Wu as MathBackendCPU,Ku as MathBackendWebGL,Mpt as MatrixBandPart,Ss as Max,ks as MaxPool,Oi as MaxPool3D,nu as MaxPool3DGrad,Wp as MaxPoolGrad,Up as MaxPoolWithArgmax,Ns as Maximum,Ts as Mean,_s as Min,Es as Minimum,As as MirrorPad,Ya as Mod,Ac as MomentumOptimizer,Za as Multinomial,Ds as Multiply,Pi as Neg,Qa as NonMaxSuppressionV3,tl as NonMaxSuppressionV4,el as NonMaxSuppressionV5,Ja as NotEqual,v0 as OP_SCOPE_SUFFIX,$s as OneHot,Mi as OnesLike,qr as Optimizer,vh as OptimizerConstructors,Li as Pack,Rs as PadV2,Lpt as Pool,Fs as Pow,Os as Prelu,Ps as Prod,Dc as RMSPropOptimizer,_n as RNN,Hp as RaggedGather,qp as RaggedRange,Kp as RaggedTensorToTensor,ou as Range,f0 as Rank,jp as Real,us as RealDiv,Ms as Reciprocal,Ze as Reduction,Ls as Relu,Vs as Relu6,zi as Reshape,Bs as ResizeBilinear,nl as ResizeBilinearGrad,zs as ResizeNearestNeighbor,rl as ResizeNearestNeighborGrad,Gs as Reverse,fl as RotateWithOffset,Ws as Round,Us as Rsqrt,Cl as SGDOptimizer,ol as ScatterNd,il as SearchSorted,Bi as Select,Hs as Selu,xa as Sequential,js as Sigmoid,Ks as Sign,qs as Sin,al as Sinh,Vi as Slice,Js as Softmax,Xs as Softplus,Gi as SpaceToBatchND,su as SparseFillEmptyRows,ll as SparseReshape,iu as SparseSegmentMean,au as SparseSegmentSum,ul as SparseToDense,Wi as SplitV,Ys as Sqrt,lu as Square,Qs as SquaredDifference,oc as StaticRegexReplace,xo as Step,cl as StridedSlice,uu as StringNGrams,cu as StringSplit,pu as StringToHashBucketFast,ti as Sub,Zs as Sum,rn as SymbolicTensor,ei as Tan,ri as Tanh,Pt as Tensor,le as TensorBuffer,sl as TensorScatterUpdate,no as Tile,pl as TopK,ml as Transform,oo as Transpose,mu as Unique,Ui as Unpack,fu as UnsortedSegmentSum,zpt as UpperBound,dl as Variable,Hi as ZerosLike,qi as _FusedMatMul,Ee as abs,ux as acos,cx as acosh,Y as add,sE as addN,am as all,fc as any,Qi as argMax,px as argMin,mx as asin,fx as asinh,dx as atan,hx as atan2,gx as atanh,bu as avgPool,xx as avgPool3d,oE as backend,S as backend_util,lE as basicLSTMCell,ra as batchNorm,yx as batchNorm2d,bx as batchNorm3d,wx as batchNorm4d,wu as batchToSpaceND,Ix as bincount,jX as booleanMaskAsync,cE as broadcastArgs,na as broadcastTo,Ur as broadcast_util,Sy as browser,bt as buffer,lQ as callbacks,J as cast,Cx as ceil,vr as clipByValue,ln as clone,vn as complex,oe as concat,vx as concat1d,Sx as concat2d,Nx as concat3d,kx as concat4d,q$ as constraints,um as conv1d,Sn as conv2d,pm as conv2dTranspose,Tx as conv3d,Ex as conv3dTranspose,qpt as copyRegisteredKernels,Iu as cos,mm as cosh,bh as cosineWindow,hc as cumprod,fm as cumsum,pn as customGrad,EF as data,dh as denseBincount,R0 as deprecationWarn,Ax as depthToSpace,oa as depthwiseConv2d,mQ as deregisterOp,xu as device_util,pE as diag,Dx as dilation2d,tdt as disableDeprecationWarnings,Tt as dispose,edt as disposeVariables,ut as div,$x as divNoNan,Rx as dot,Y0 as dropout,fE as einsum,sa as elu,Qft as enableDebugMode,Jft as enableProdMode,Z0 as enclosingPowerOfTwo,Bn as engine,L as env,Rr as equal,Fx as erf,Ox as euclideanNorm,sr as exp,ir as expandDims,Px as expm1,gc as eye,Au as fft,Co as fill,adt as findBackend,ldt as findBackendFactory,ia as floor,im as floorDiv,yz as forceHalfFloat,Ru as fused,aa as gather,o5 as gatherND,Ny as gather_util,sdt as getBackend,s0 as getGradient,oh as getKernel,Kg as getKernelsForBackend,vpt as getThreadsCount,s1 as gpgpu_util,Jj as grad,Qj as grads,Fe as greater,cn as greaterEqual,Il as ifft,Cu as imag,lo as image,a5 as inTopKAsync,K$ as initializers,RN as input,Mr as io,km as irfft,Mx as isFinite,Lx as isInf,zx as isNaN,$e as keep,Kr as kernel_impls,kR as layers,vu as leakyRelu,bl as less,Vn as lessEqual,Q0 as linalg,gE as linspace,l9 as loadGraphModel,u9 as loadGraphModelSync,fR as loadLayersModel,Bx as localResponseNormalization,Nr as log,Su as log1p,Wx as logSigmoid,dm as logSoftmax,hm as logSumExp,Or as logicalAnd,Nu as logicalNot,gm as logicalOr,Ux as logicalXor,l8 as losses,xE as lowerBound,Bt as matMul,a2 as math,Sr as max,ku as maxPool,qx as maxPool3d,yE as maxPoolWithArgmax,Nn as maximum,ke as mean,ph as memory,bE as meshgrid,TR as metrics,xl as min,ao as minimum,Kx as mirrorPad,jx as mod,c7 as model,_R as models,xc as moments,ZX as movingAverage,$ as mul,wE as multiRNNCell,IE as multinomial,Ut as neg,Sh as nextFrame,yl as norm,ai as notEqual,la as oneHot,dr as ones,Ir as onesLike,k as op,CE as outerProduct,mn as pad,vE as pad1d,SE as pad2d,NE as pad3d,kE as pad4d,Xx as pool,un as pow,_u as prelu,lx as print,Yx as prod,rdt as profile,TE as raggedGather,_E as raggedRange,EE as raggedTensorToTensor,AE as rand,ZE as randomGamma,wc as randomNormal,JE as randomStandardNormal,ua as randomUniform,ca as range,odt as ready,wl as real,ry as reciprocal,sm as registerBackend,m7 as registerCallbackConstructor,p_ as registerGradient,sc as registerKernel,pQ as registerOp,ER as regularizers,Pr as relu,xm as relu6,idt as removeBackend,R as reshape,hr as reverse,QE as reverse1d,tA as reverse2d,eA as reverse3d,rA as reverse4d,Du as rfft,ym as round,bm as rsqrt,ft as scalar,QX as scatterND,$u as scatter_util,gh as searchSorted,wm as selu,Im as separableConv2d,p7 as sequential,Q as serialization,iK as setBackend,udt as setPlatform,Cpt as setThreadsCount,wpt as setWasmPath,Ipt as setWasmPaths,bT as setWebGLContext,nA as setdiff1dAsync,bw as shared,tn as sigmoid,ny as sign,a8 as signal,Cm as sin,vm as sinh,Ot as slice,Sm as slice1d,yh as slice2d,Nm as slice3d,Ic as slice4d,ze as slice_util,Eu as softmax,ii as softplus,Tu as spaceToBatchND,u8 as sparse,r5 as sparseToDense,i8 as spectral,gr as split,Ne as sqrt,Gt as square,Tm as squaredDifference,Gn as squeeze,qe as stack,So as step,oy as stridedSlice,c8 as string,lt as sub,pt as sum,pc as sumOutType,sy as tan,ea as tanh,or as tensor,Ke as tensor1d,li as tensor2d,iy as tensor3d,oA as tensor4d,sA as tensor5d,iA as tensor6d,lA as tensorScatterUpdate,Io as tensor_util,YE as test_util,B as tidy,Fr as tile,ndt as time,ay as topk,Rc as train,Vt as transpose,Em as truncatedNormal,ly as unique,Hpt as unregisterGradient,Upt as unregisterKernel,Am as unsortedSegmentSum,xr as unstack,lr as upcastType,uA as upperBound,y as util,t6 as valueAndGrad,e6 as valueAndGrads,uy as variable,Vx as variableGrads,Apt as version,uF as version_converter,b2 as version_core,dO as version_cpu,tf as version_layers,Spt as version_wasm,xz as version_webgl,n2e as webgl,kd as webgl_util,be as where,py as whereAsync,Te as zeros,vt as zerosLike};\n", "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 = 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 = 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 ${JSON.stringify({ width, height })}`);\n }\n\n this._width = width;\n this._height = height;\n }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public reverse(): Dimensions {\n return new Dimensions(1 / this.width, 1 / this.height);\n }\n}\n", "export interface IPoint {\n x: number\n y: number\n}\n\nexport class Point implements IPoint {\n private _x: number;\n\n private _y: number;\n\n constructor(x: number, y: number) {\n this._x = x;\n this._y = y;\n }\n\n get x(): number { return this._x; }\n\n get y(): number { return this._y; }\n\n public add(pt: IPoint): Point {\n return new Point(this.x + pt.x, this.y + pt.y);\n }\n\n public sub(pt: IPoint): Point {\n return new Point(this.x - pt.x, this.y - pt.y);\n }\n\n public mul(pt: IPoint): Point {\n return new Point(this.x * pt.x, this.y * pt.y);\n }\n\n public div(pt: IPoint): Point {\n return new Point(this.x / pt.x, this.y / pt.y);\n }\n\n public abs(): Point {\n return new Point(Math.abs(this.x), Math.abs(this.y));\n }\n\n public magnitude(): number {\n return Math.sqrt((this.x ** 2) + (this.y ** 2));\n }\n\n public floor(): Point {\n return new Point(Math.floor(this.x), Math.floor(this.y));\n }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box implements IBoundingBox, IRect {\n public static isRect(rect: any): boolean {\n return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n }\n\n public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions = false) {\n if (!Box.isRect(box)) {\n throw new Error(`${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 = 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 {\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 {\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 {\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({ x, y, width, height });\n }\n\n public rescale(s: IDimensions | number): Box {\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 {\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({ x, y, width, height });\n }\n\n public clipAtImageBorders(imgWidth: number, imgHeight: number): Box {\n const { x, y, right, bottom } = 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({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();\n }\n\n public shift(sx: number, sy: number): Box {\n const { width, height } = this;\n const x = this.x + sx;\n const y = this.y + sy;\n\n return new Box({ x, y, width, height });\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 { dy, edy, dx, edx, y, ey, x, ex, w, h };\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 implements IBoundingBox {\n constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions = false) {\n super({ left, top, right, bottom }, 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 override 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 = 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 = 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(imgTensor: tf.Tensor4D, isCenterImage = false): tf.Tensor4D {\n return tf.tidy(() => {\n const [height, width] = imgTensor.shape.slice(1);\n if (height === width) return imgTensor;\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 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 const paddingTensorAppend = createPaddingTensor(paddingAmount);\n const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null;\n const tensorsToStack = [paddingTensorPrepend, imgTensor, paddingTensorAppend]\n .filter((t) => !!t)\n .map((t) => tf.cast(t as tf.Tensor4D, '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 implements IRect {\n constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions = false) {\n super({ x, y, width, height }, 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(width: number, height: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n { width, height },\n );\n }\n\n public shiftBy(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(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 override 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 override 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 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 = 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 {\n public static assertIsValidLabeledBox(box: any, callee: string) {\n Box.assertIsValidBox(box, callee);\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 & {\n detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection(sourceObj: TSource, detection: FaceDetection): WithFaceDetection {\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 createVideoElement: () => document.createElement('video'),\n fetch,\n readFile,\n };\n}\n", "export function isNodejs(): boolean {\n return typeof global === 'object'\n && typeof process !== 'undefined'\n && process.versions != null\n && process.versions.node != null;\n}\n", "import { FileSystem } from './types';\nimport { isNodejs } from './isNodejs';\n\nexport function createFileSystem(fs?: any): FileSystem {\n let requireFsError = '';\n if (!fs && isNodejs()) {\n try {\n // eslint-disable-next-line global-require\n fs = require('fs');\n } catch (err) {\n requireFsError = (err as any).toString();\n }\n }\n\n const readFile = fs\n ? (filePath: string) => new Promise((resolve, reject) => { fs.readFile(filePath, (err: any, buffer) => (err ? reject(err) : resolve(buffer))); })\n : () => { throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`); };\n return { readFile };\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 // eslint-disable-next-line dot-notation\n const Video = global['Video'] || global.HTMLVideoElement;\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 createVideoElement = () => {\n if (Video) return new Video();\n throw new Error('createVideoElement - missing Video 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 createVideoElement,\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) {\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 { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } 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,\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)) resolve(null);\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 {\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 {\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 = 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 if (inputSize <= 0) return createCanvas({ width: 1, height: 1 });\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 if (inputCanvas.width > 0 && inputCanvas.height > 0) 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 { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n private _imageTensors: Array = [];\n\n private _canvases: HTMLCanvasElement[] = [];\n\n private _batchSize: number;\n\n private _treatAsBatchInput = false;\n\n private _inputDimensions: number[][] = [];\n\n private _inputSize = 0;\n\n constructor(inputs: Array, treatAsBatchInput = false) {\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 // @ts-ignore\n const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n this._canvases[idx] = canvas as HTMLCanvasElement;\n this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n });\n }\n\n public get imageTensors(): Array {\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 = 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 let imgTensor = isTensor4D(input) ? input : tf.expandDims(input);\n imgTensor = padToSquare(imgTensor as tf.Tensor4D, isCenterInputs);\n\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n imgTensor = tf['image'].resizeBilinear(imgTensor as tf.Tensor4D, [inputSize, inputSize], false, false);\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) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n // const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))) as tf.Tensor4D;\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 {\n if (inputs instanceof NetInput) return inputs;\n const inputArgArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArgArray.length) throw new Error('toNetInput - empty array passed as input');\n const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n const inputArray = inputArgArray.map(resolveInput);\n inputArray.forEach((input, i) => {\n if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n if (typeof inputArgArray[i] === 'string') throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n }\n 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) throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n });\n // wait for all media elements being loaded\n await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)));\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(input: TNetInput, detections: Array): Promise {\n const { Canvas } = env.getEnv();\n let canvas = input as HTMLCanvasElement;\n if (!(input instanceof Canvas)) {\n const netInput = await toNetInput(input);\n if (netInput.batchSize > 1) throw new Error('extractFaces - batchSize > 1 not supported');\n const tensorOrCanvas = netInput.getInput(0);\n canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);\n }\n const ctx = getContext2dOrThrow(canvas);\n const boxes = detections\n .map((det) => (det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det))\n .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n return boxes.map(({ x, y, width, height }) => {\n const faceImg = createCanvas({ width, height });\n if (width > 0 && height > 0) getContext2dOrThrow(faceImg).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(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array): Promise {\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 const boxes = detections.map((det) => (det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det))\n .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n const faceTensors = boxes\n .filter((box) => box.width > 0 && box.height > 0)\n .map(({ x, y, width, height }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\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 {\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 {\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(uri: string): Promise {\n return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise {\n return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "import { env } from '../env/index';\n\nexport function bufferToVideo(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToVideo - expected buf to be of type: Blob'));\n\n const video = env.getEnv().createVideoElement();\n video.oncanplay = () => resolve(video);\n video.onerror = reject;\n video.playsInline = true;\n video.muted = true;\n video.src = URL.createObjectURL(buf);\n video.play();\n });\n}\n", "import { bufferToVideo } from './bufferToVideo';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchVideo(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('video/')) {\n throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToVideo(blob);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n const defaultManifestFilename = `${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 {\n const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n // @ts-ignore\n const manifest = await fetchJson(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 = 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 {\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 = true) {\n this.getParamList().forEach((param) => {\n if (throwOnRedispose && param.tensor.isDisposed) {\n throw new Error(`param tensor has already been disposed for path ${param.path}`);\n }\n param.tensor.dispose();\n });\n this._params = undefined;\n }\n\n public serializeParams(): Float32Array {\n return new Float32Array(\n this.getParamList()\n .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n .reduce((flat, arr) => flat.concat(arr)),\n );\n }\n\n public async load(weightsOrUrl: Float32Array | string | undefined): Promise {\n if (weightsOrUrl instanceof Float32Array) {\n this.extractWeights(weightsOrUrl);\n return;\n }\n await this.loadFromUri(weightsOrUrl);\n }\n\n public async loadFromUri(uri: string | undefined) {\n if (uri && typeof uri !== 'string') {\n throw new Error(`${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 = 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 = false,\n isScaleDown = 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 = 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: (originalPath: string, paramRank: number) => T,\n) {\n return (prefix: string): SeparableConvParams => {\n const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4);\n const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n return (originalPath: string, paramRank: number, mappedPath?: string) => {\n const tensor = weightMap[originalPath];\n\n if (!isTensor(tensor, paramRank)) {\n throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n }\n\n paramMappings.push(\n { originalPath, paramPath: mappedPath || originalPath },\n );\n\n return tensor;\n };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n let remainingWeights = weights;\n\n function extractWeights(numWeights: number): Float32Array {\n const ret = remainingWeights.slice(0, numWeights);\n remainingWeights = remainingWeights.slice(numWeights);\n return ret;\n }\n\n function getRemainingWeights(): Float32Array {\n return remainingWeights;\n }\n\n return {\n extractWeights,\n getRemainingWeights,\n };\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock4Params {\n const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${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: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: {\n dense0, dense1, dense2, dense3,\n },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: (originalPath: string, paramRank: number) => T) {\n return (prefix: string): ConvParams => {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return { filters, bias };\n };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractDenseBlock3Params(prefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(prefix: string, isFirstLayer = false): DenseBlock4Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${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 implements IFaceFeatureExtractor {\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(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 {\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: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: extractFcParams('fc'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n const featureExtractorMap: tf.NamedTensorMap = {};\n const classifierMap: tf.NamedTensorMap = {};\n\n Object.keys(weightMap).forEach((key) => {\n const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n map[key] = weightMap[key];\n });\n\n return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends NeuralNetwork {\n protected _faceFeatureExtractor: IFaceFeatureExtractor;\n\n constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor) {\n super(_name);\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): IFaceFeatureExtractor {\n return this._faceFeatureExtractor;\n }\n\n protected abstract override getDefaultModelName(): string\n\n protected abstract getClassifierChannelsIn(): number\n\n protected abstract getClassifierChannelsOut(): number\n\n public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${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 override dispose(throwOnRedispose = 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 = 0;\n public happy = 0;\n public sad = 0;\n public angry = 0;\n public fearful = 0;\n public disgusted = 0;\n public surprised = 0;\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 {\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 {\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 = t.dataSync();\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 & { expressions: FaceExpressions }\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions(sourceObj: TSource, expressions: FaceExpressions): WithFaceExpressions {\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(canvasArg: string | HTMLCanvasElement, faceExpressions: DrawFaceExpressionsInput | Array, minConfidence = 0.1, textFieldAnchor?: IPoint) {\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\n> = TSource & {\n landmarks: TFaceLandmarks;\n unshiftedLandmarks: TFaceLandmarks;\n alignedRect: FaceDetection;\n angle: {\n roll: number | undefined;\n pitch: number | undefined;\n yaw: number | undefined;\n };\n};\n\nexport function isWithFaceLandmarks(\n obj: any,\n): obj is WithFaceLandmarks, FaceLandmarks> {\n return (\n 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}\n\nfunction calculateFaceAngle(mesh) {\n // Helper to convert radians to degrees\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const degrees = (radians) => (radians * 180) / Math.PI;\n const calcLengthBetweenTwoPoints = (a, b) => Math.sqrt((a._x - b._x) ** 2 + (a._y - b._y) ** 2);\n\n const angle = {\n roll: undefined,\n pitch: undefined,\n yaw: undefined,\n };\n\n const calcYaw = (leftPoint, midPoint, rightPoint) => {\n // Calc x-distance from left side of the face (\"ear\") to facial midpoint (\"nose\")\n const leftToMidpoint = Math.floor(leftPoint._x - midPoint._x);\n // Calc x-distance from facial midpoint (\"nose\") to the right side of the face (\"ear\")\n const rightToMidpoint = Math.floor(midPoint._x - rightPoint._x);\n // Difference in distances coincidentally approximates to angles\n return leftToMidpoint - rightToMidpoint;\n };\n\n const calcRoll = (lever, pivot) => {\n // When rolling, the head seems to pivot from the nose/lips/chin area.\n // So, we'll choose any two points from the facial midline, where the first point should be the pivot, and the other \"lever\"\n // Plan/Execution: get the hypotenuse & opposite sides of a 90deg triangle ==> Calculate angle in radians\n const hypotenuse = Math.hypot(pivot._x - lever._x, pivot._y - lever._y);\n const opposite = pivot._y - lever._y;\n const angleInRadians = Math.asin(opposite / hypotenuse);\n const angleInDegrees = degrees(angleInRadians);\n const normalizeAngle = Math.floor(90 - angleInDegrees);\n // If lever more to the left of the pivot, then we're tilting left\n // \"-\" is negative direction. \"+\", or absence of a sign is positive direction\n const tiltDirection = pivot._x - lever._x < 0 ? -1 : 1;\n const result = normalizeAngle * tiltDirection;\n return result;\n };\n\n const calcPitch = (leftPoint, midPoint, rightPoint) => {\n // Theory: While pitching, the nose is the most salient point --> That's what we'll use to make a trianle.\n // The \"base\" is between point that don't move when we pitch our head (i.e. an imaginary line running ear to ear through the nose).\n // Executuin: Get the opposite & adjacent lengths of the triangle from the ear's perspective. Use it to get angle.\n\n const base = calcLengthBetweenTwoPoints(leftPoint, rightPoint);\n // adjecent is base/2 technically.\n const baseCoords = {\n _x: (leftPoint._x + rightPoint._x) / 2,\n _y: (leftPoint._y + rightPoint._y) / 2,\n };\n const midToBaseLength = calcLengthBetweenTwoPoints(midPoint, baseCoords);\n const angleInRadians = Math.atan(midToBaseLength / base);\n const angleInDegrees = Math.floor(degrees(angleInRadians));\n // Account for directionality.\n // pitch forwards (_i.e. tilting your head forwards) is positive (or no sign); backward is negative.\n const direction = baseCoords._y - midPoint._y < 0 ? -1 : 1;\n const result = angleInDegrees * direction;\n return result;\n };\n\n if (!mesh || !mesh._positions || mesh._positions.length !== 68) return angle;\n const pt = mesh._positions;\n angle.roll = calcRoll(pt[27], pt[66]);\n angle.pitch = calcPitch(pt[14], pt[30], pt[2]);\n angle.yaw = calcYaw(pt[14], pt[33], pt[2]);\n return angle;\n}\n\nexport function extendWithFaceLandmarks, TFaceLandmarks extends FaceLandmarks = FaceLandmarks68>(\n sourceObj: TSource,\n unshiftedLandmarks: TFaceLandmarks,\n): WithFaceLandmarks {\n const { box: shift } = sourceObj.detection;\n const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);\n const rect = landmarks.align();\n const { imageDims } = sourceObj.detection;\n const alignedRect = new FaceDetection(\n sourceObj.detection.score,\n rect.rescale(imageDims.reverse()),\n imageDims,\n );\n const angle = calculateFaceAngle(unshiftedLandmarks);\n const extension = { landmarks, unshiftedLandmarks, alignedRect, angle };\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>\n\nexport function drawFaceLandmarks(\n canvasArg: string | HTMLCanvasElement,\n faceLandmarks: DrawFaceLandmarksInput | Array,\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> or array thereof');\n }\n\n new DrawFaceLandmarks(landmarks).draw(canvasArg);\n });\n}\n", "{\n \"name\": \"@vladmandic/face-api\",\n \"version\": \"1.7.10\",\n \"description\": \"FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS\",\n \"sideEffects\": false,\n \"main\": \"dist/face-api.node.js\",\n \"module\": \"dist/face-api.esm.js\",\n \"browser\": \"dist/face-api.esm.js\",\n \"types\": \"types/face-api.d.ts\",\n \"author\": \"Vladimir Mandic \",\n \"bugs\": {\n \"url\": \"https://github.com/vladmandic/face-api/issues\"\n },\n \"homepage\": \"https://vladmandic.github.io/face-api/demo/webcam.html\",\n \"license\": \"MIT\",\n \"engines\": {\n \"node\": \">=14.0.0\"\n },\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/vladmandic/face-api.git\"\n },\n \"scripts\": {\n \"start\": \"node --no-warnings demo/node.js\",\n \"dev\": \"build --profile development\",\n \"build\": \"node build.js\",\n \"lint\": \"eslint src/ demo/\",\n \"test\": \"node --trace-warnings test/test-node.js\",\n \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n },\n \"keywords\": [\n \"face-api\",\n \"faceapi\",\n \"face-detection\",\n \"age-gender\",\n \"emotion-detection\",\n \"face-recognition\",\n \"face\",\n \"face-description\",\n \"tensorflow\",\n \"tensorflowjs\",\n \"tfjs\"\n ],\n \"devDependencies\": {\n \"@canvas/image\": \"^1.0.1\",\n \"@microsoft/api-extractor\": \"^7.34.4\",\n \"@tensorflow/tfjs\": \"^4.3.0\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.3.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.3.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.3.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"0.0.1-alpha.18\",\n \"@tensorflow/tfjs-converter\": \"^4.3.0\",\n \"@tensorflow/tfjs-core\": \"^4.3.0\",\n \"@tensorflow/tfjs-data\": \"^4.3.0\",\n \"@tensorflow/tfjs-layers\": \"^4.3.0\",\n \"@tensorflow/tfjs-node\": \"^4.3.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.3.0\",\n \"@types/node\": \"^18.15.5\",\n \"@types/offscreencanvas\": \"^2019.7.0\",\n \"@typescript-eslint/eslint-plugin\": \"^5.56.0\",\n \"@typescript-eslint/parser\": \"^5.56.0\",\n \"@vladmandic/build\": \"^0.8.2\",\n \"@vladmandic/pilogger\": \"^0.4.7\",\n \"esbuild\": \"^0.17.12\",\n \"eslint\": \"^8.36.0\",\n \"eslint-config-airbnb-base\": \"^15.0.0\",\n \"eslint-plugin-import\": \"^2.27.5\",\n \"eslint-plugin-json\": \"^3.1.0\",\n \"eslint-plugin-node\": \"^11.1.0\",\n \"eslint-plugin-promise\": \"^6.1.1\",\n \"rimraf\": \"^4.4.0\",\n \"seedrandom\": \"^3.0.5\",\n \"tslib\": \"^2.5.0\",\n \"typedoc\": \"^0.23.28\",\n \"typescript\": \"5.0.2\"\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: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { entry_flow, middle_flow, exit_flow },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${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 = 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 {\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(255) 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 {\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: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc: { age, gender } },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: {\n age: extractFcParams('fc/age'),\n gender: extractFcParams('fc/gender'),\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n // eslint-disable-next-line no-unused-vars\n FEMALE = 'female',\n // eslint-disable-next-line no-unused-vars\n MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n age: number\n gender: Gender\n genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n fc: {\n age: FCParams\n gender: FCParams\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork {\n private _faceFeatureExtractor: TinyXception;\n\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n super('AgeGenderNet');\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): TinyXception {\n return this._faceFeatureExtractor;\n }\n\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${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 {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictAgeAndGender(input: TNetInput): Promise {\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 = (ageTensor.dataSync())[0];\n const probMale = (genderTensor.dataSync())[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 override dispose(throwOnRedispose = 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 {\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 {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async detectLandmarks(input: TNetInput): Promise {\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(landmarkTensor.dataSync());\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 {\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 implements IFaceFeatureExtractor {\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(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 {\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 {\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 { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { isFloat } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } 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, `${mappedPrefix}/scale`);\n\n return { conv, scale };\n }\n\n function extractResidualLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n isDown = false,\n ): ResidualLayerParams {\n const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${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(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(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(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 {\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(255) 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 as tf.Tensor2D;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async computeFaceDescriptor(input: TNetInput): Promise {\n // @ts-ignore\n if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n const netInput = await toNetInput(input);\n const faceDescriptorTensors = tf.tidy(() => tf.unstack(this.forwardInput(netInput)));\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 & {\n descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n TSource\n>(\n sourceObj: TSource,\n descriptor: Float32Array,\n): WithFaceDescriptor {\n const extension = { descriptor };\n return { ...sourceObj, ...extension };\n}\n", "export type WithAge = 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 {\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 & {\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 {\n const extension = { gender, genderProbability };\n return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common/index';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } 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: ${getRemainingWeights().length}`);\n }\n\n return {\n params: {\n mobilenetv1,\n prediction_layer,\n output_layer,\n },\n paramMappings,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n const filters = extractWeightEntry(`${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(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]) {\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(x: tf.Tensor4D, params: MobileNetV1.DepthwiseConvParams, strides: [number, number]) {\n return tf.tidy(() => {\n let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n out = tf.batchNorm(\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) conv11 = out;\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) return 0.0;\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) * 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(maxOutputSize, numBoxes);\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], 2)),\n tf.add(vec[1], tf.div(sizes[1], 2)),\n ];\n return { sizes, centers };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n const { sizes, centers } = 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], 5)), sizes[0]), 2);\n const add0_out = tf.add(tf.mul(tf.div(vec[0], 10), sizes[0]), centers[0]);\n const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], 5)), sizes[1]), 2);\n const add1_out = tf.add(tf.mul(tf.div(vec[1], 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(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams) {\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(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);\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(scores, [batchSize, scores.shape[1] as number]);\n\n const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n return { boxes: boxesByBatch, scores: scoresByBatch };\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 = '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 {\n constructor() {\n super('SsdMobilenetv1');\n }\n\n public forwardInput(input: NetInput) {\n const { params } = this;\n if (!params) throw new Error('SsdMobilenetv1 - load model before inference');\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n const x = tf.sub(tf.div(batchTensor, 127.5), 1) as tf.Tensor4D; // input is normalized -1..1\n const features = mobileNetV1(x, params.mobilenetv1);\n const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);\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(input: TNetInput, options: ISsdMobilenetv1Options = {}): Promise {\n const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n const netInput = await toNetInput(input);\n const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput);\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 const scoresData = Array.from(scores.dataSync());\n const iouThreshold = 0.5;\n const indices = nonMaxSuppression(boxes, scoresData as number[], maxResults, iouThreshold, minConfidence);\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 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(left, top, right - left, bottom - top),\n { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) },\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 = '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 {\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(255) 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 {\n return this.forwardInput(await toNetInput(input), inputSize);\n }\n\n public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise {\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 = 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 {\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 override getDefaultModelName(): string {\n return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n }\n\n protected override 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 = true) {\n const net = new TinyYolov2(withSeparableConvs);\n net.extractWeights(weights);\n return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport type ITinyFaceDetectorOptions = ITinyYolov2Options\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n protected override _name = 'TinyFaceDetectorOptions';\n}\n", "export class ComposableTask {\n // eslint-disable-next-line no-unused-vars\n public async then(onfulfilled: (value: T) => T | PromiseLike): Promise {\n return onfulfilled(await this.run());\n }\n\n public async run(): Promise {\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, TResult>(\n parentResults: TSource[],\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResults: (faces: Array) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment: (parentResult: WithFaceLandmarks) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n ? getRectForAlignment(parentResult)\n : parentResult.detection));\n const faces: Array = extractedFaces || (\n input instanceof tf.Tensor\n ? await extractFaceTensors(input, faceBoxes)\n : await extractFaces(input, faceBoxes)\n );\n const results = await computeResults(faces);\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return results;\n}\n\nexport async function extractSingleFaceAndComputeResult, TResult>(\n parentResult: TSource,\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment?: (parentResultLocal: WithFaceLandmarks) => FaceDetection,\n) {\n return extractAllFacesAndComputeResults(\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 {\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 override getDefaultModelName(): string {\n return 'tiny_face_detector_model';\n }\n\n protected override 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 => 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 => 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 => 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 => 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 => 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 => 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 => 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 => 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 { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllFaceExpressionsTask> extends PredictFaceExpressionsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n\n const faceExpressionsByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(\n faces.map((face) => nets.faceExpressionNet.predictExpressions(face) as Promise),\n ),\n this.extractedFaces,\n );\n\n return parentResults.map(\n (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]),\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsTask> extends PredictFaceExpressionsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const faceExpressions = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceExpressionNet.predictExpressions(face) as Promise,\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>> extends PredictAllFaceExpressionsTask {\n override 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>> extends PredictSingleFaceExpressionsTask {\n override 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 { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllAgeAndGenderTask> extends PredictAgeAndGenderTaskBase>[], TSource[]> {\n public override async run(): Promise>[]> {\n const parentResults = await this.parentTask;\n const ageAndGenderByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise)),\n this.extractedFaces,\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> extends PredictAgeAndGenderTaskBase> | undefined, TSource | undefined> {\n public override async run(): Promise> | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise,\n this.extractedFaces,\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>> extends PredictAllAgeAndGenderTask {\n override 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>> extends PredictSingleAgeAndGenderTask {\n override 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 { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n ) {\n super();\n }\n}\n\nexport class ComputeAllFaceDescriptorsTask>> extends ComputeFaceDescriptorsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const descriptors = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise)),\n null,\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n return descriptors.map((descriptor, i) => extendWithFaceDescriptor(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>> extends ComputeFaceDescriptorsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const descriptor = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise,\n null,\n // eslint-disable-next-line no-shadow, @typescript-eslint/no-shadow\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\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 { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\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> extends DetectFaceLandmarksTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const detections = parentResults.map((res) => res.detection);\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, detections)\n : await extractFaces(this.input, detections);\n const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face))) as FaceLandmarks68[];\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n const result = parentResults\n .filter((_parentResult, i) => faceLandmarksByFace[i])\n .map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));\n return result;\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> extends DetectFaceLandmarksTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n const { detection } = parentResult;\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, [detection])\n : await extractFaces(this.input, [detection]);\n const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return extendWithFaceLandmarks(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 extends ComposableTask {\n // eslint-disable-next-line no-unused-vars\n constructor(protected input: TNetInput, protected options: FaceDetectionOptions = new SsdMobilenetv1Options()) {\n super();\n }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase {\n public override async run(): Promise {\n const { input, options } = this;\n let result;\n if (options instanceof TinyFaceDetectorOptions) result = nets.tinyFaceDetector.locateFaces(input, options);\n else if (options instanceof SsdMobilenetv1Options) result = nets.ssdMobilenetv1.locateFaces(input, options);\n else if (options instanceof TinyYolov2Options) result = nets.tinyYolov2.locateFaces(input, options);\n else throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options');\n return result;\n }\n\n private runAndExtendWithFaceDetections(): Promise[]> {\n return new Promise[]>((resolve, reject) => {\n this.run()\n .then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection))))\n .catch((err) => reject(err));\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = 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 {\n public override async run(): Promise {\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) faceDetectionWithHighestScore = faceDetection;\n });\n return faceDetectionWithHighestScore;\n }\n\n private runAndExtendWithFaceDetection(): Promise | undefined> {\n // eslint-disable-next-line no-async-promise-executor\n return new Promise | undefined>(async (resolve) => {\n const detection = await this.run();\n resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = 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(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectSingleFaceTask {\n return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): 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\nexport async function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise>>[]> {\n return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise>>[]> {\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 const desc1 = Array.from(arr1);\n const desc2 = Array.from(arr2);\n return Math.sqrt(\n desc1\n .map((val, i) => val - desc2[i])\n .reduce((res, diff) => res + (diff * diff), 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 private _distanceThreshold: number;\n\n constructor(inputs: LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>, distanceThreshold = 0.6) {\n this._distanceThreshold = distanceThreshold;\n const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArray.length) throw new Error('FaceRecognizer.constructor - expected atleast one input');\n let count = 1;\n const createUniqueLabel = () => `person ${count++}`;\n this._labeledDescriptors = inputArray.map((desc) => {\n if (desc instanceof LabeledFaceDescriptors) return desc;\n if (desc instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n if (desc.descriptor && desc.descriptor instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | 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) / (descriptors.length || 1);\n }\n\n public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n return this.labeledDescriptors\n .map(({ descriptors, label }) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors)))\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) ? bestMatch : 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.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(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).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\nexport const version = pkg.version as string;\n\n// set webgl defaults\n// if (browser) tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n"], - "mappings": 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g=this.backend.numDataIds();u=this.tidy(()=>d(this.backend,h));let x=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,g,x),x}}let{inputs:c,attrs:p}=t,m=k0(t)?null:t.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?e=a():(f=this.profiler.profileKernel(l,c,()=>a()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(f),e=f.outputs)}),o&&this.addTapeNode(l,c,e,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:e.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?e:e[0]}saveTensorsForBackwardMode(t){return 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Ot=k({slice_:XK});function YK(r){let e={x:v(r,\"x\",\"tanh\",\"float32\")};return T.runKernel(ni,e)}var ra=k({tanh_:YK});function ZK(r,t,e,n,o,s){let i=v(r,\"forgetBias\",\"basicLSTMCell\"),a=v(t,\"lstmKernel\",\"basicLSTMCell\"),u=v(e,\"lstmBias\",\"basicLSTMCell\"),l=v(n,\"data\",\"basicLSTMCell\"),c=v(o,\"c\",\"basicLSTMCell\"),p=v(s,\"h\",\"basicLSTMCell\"),m=se([l,p],1),f=Bt(m,a),d=Y(f,u),h=d.shape[0],g=d.shape[1]/4,x=[h,g],b=Ot(d,[0,0],x),w=Ot(d,[0,g],x),I=Ot(d,[0,g*2],x),N=Ot(d,[0,g*3],x),E=Y($(tn(b),ra(w)),$(c,tn(Y(i,I)))),A=$(ra(E),tn(N));return[E,A]}var gE=k({basicLSTMCell_:ZK});function JK(r,t,e){let n=v(r,\"x\",\"batchToSpaceND\"),o=t.reduce((a,u)=>a*u);_(n.rank>=1+t.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${t.length}`),_(e.length===t.length,()=>`crops.length is ${e.length} but should be equal to blockShape.length ${t.length}`),_(n.shape[0]%o===0,()=>`input tensor batch is ${n.shape[0]} but is not divisible by the product of the elements 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na=k({batchNorm_:QK});function tj(r,t,e,n,o,s){let i=v(r,\"x\",\"batchNorm\"),a=v(t,\"mean\",\"batchNorm\"),u=v(e,\"variance\",\"batchNorm\"),l;o!=null&&(l=v(o,\"scale\",\"batchNorm\"));let c;return n!=null&&(c=v(n,\"offset\",\"batchNorm\")),_(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),_(a.rank===2||a.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${a.rank}.`),_(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`),l!=null&&_(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&_(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),na(i,a,u,c,l,s)}var vx=k({batchNorm2d_:tj});function ej(r,t,e,n,o,s){let i=v(r,\"x\",\"batchNorm\"),a=v(t,\"mean\",\"batchNorm\"),u=v(e,\"variance\",\"batchNorm\"),l;o!=null&&(l=v(o,\"scale\",\"batchNorm\"));let c;return n!=null&&(c=v(n,\"offset\",\"batchNorm\")),_(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),_(a.rank===3||a.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${a.rank}.`),_(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),l!=null&&_(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&_(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),na(i,a,u,c,l,s)}var Sx=k({batchNorm3d_:ej});function rj(r,t,e,n,o,s){let i=v(r,\"x\",\"batchNorm\"),a=v(t,\"mean\",\"batchNorm\"),u=v(e,\"variance\",\"batchNorm\"),l;o!=null&&(l=v(o,\"scale\",\"batchNorm\"));let c;return n!=null&&(c=v(n,\"offset\",\"batchNorm\")),_(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),_(a.rank===4||a.rank===1,()=>`Error in batchNorm4D: mean must 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e=v(r,\"x\",\"bitwiseAnd\"),n=v(t,\"y\",\"bitwiseAnd\");if(!sn(e.shape,n.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${e.shape}, y: ${n.shape}`);if(e.dtype!==\"int32\"||n.dtype!==\"int32\")throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${e.dtype} and type of y: ${n.dtype}`);let o={a:e,b:n};return T.runKernel(nh,o)}var yE=k({bitwiseAnd_:oj});function sj(r,t){let e=v(r,\"s0\",\"broadcastArgs\",\"int32\"),n=v(t,\"s1\",\"broadcastArgs\",\"int32\");if(e.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). 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s={x:v(r,\"x\",\"cumsum\")},i={axis:t,exclusive:e,reverse:n};return T.runKernel(as,s,i)}var fm=k({cumsum_:vj});function Sj(r,t,e,n=!1){let o=v(r,\"x\",\"denseBincount\"),s=v(t,\"weights\",\"denseBincount\");_(o.dtype===\"int32\",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),_(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${o.rank}.`),_(e>=0,()=>`size must be non-negative, but got ${e}.`),_(s.size===o.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${o.shape}, weights shape: ${s.shape}.`);let i={x:o,weights:s},a={size:e,binaryOutput:n};return T.runKernel(Jl,i,a)}var gh=k({denseBincount_:Sj});function Nj(r,t,e=\"NHWC\"){let n=v(r,\"x\",\"depthToSpace\",\"float32\"),o=e===\"NHWC\"?n.shape[1]:n.shape[2],s=e===\"NHWC\"?n.shape[2]:n.shape[3],i=e===\"NHWC\"?n.shape[3]:n.shape[1];_(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: 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i=R(n,[-1,1]),a=Bt(e,i);return R(a,[a.size])}else{let i=R(n,[n.shape[0],n.shape[1]]);return Bt(e,i)}}var Lx=k({dot_:Rj});function Fj(r,...t){let e=t.map((o,s)=>v(o,`tensors${s}`,\"einsum\")),n={equation:r};return T.runKernel(zp,e,n)}var CE=k({einsum_:Fj});function Oj(r){let e={x:v(r,\"x\",\"elu\",\"float32\")};return T.runKernel(ps,e)}var ia=k({elu_:Oj});function Pj(r,t){let e=v(r,\"x\",\"ensureShape\",\"string_or_numeric\");if(!n0(e.shape,t))throw new Error(`EnsureShape: Shape of tensor ${e.shape} is not compatible with expected shape ${t}`);return r}var vE=k({ensureShape_:Pj});function Mj(r){let t=v(r,\"x\",\"erf\");_(t.dtype===\"int32\"||t.dtype===\"float32\",()=>\"Input dtype must be `int32` or `float32`.\"),t.dtype===\"int32\"&&(t=Q(t,\"float32\"));let e={x:t};return T.runKernel(La,e)}var zx=k({erf_:Mj});function U0(r,t){for(let e=0;er[s]);return[e,o]}function So(r,t){let e=t.map(n=>1);return SE(r,e,t)}function Lj(r,t,e){_(U0(t,e),()=>`${r} supports only inner-most axes for now. 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T.runKernel(Pp,p,m)}var Dm=k({conv2DBackpropFilter_:S5});function vc(r,t,e){if(e==null||e===\"linear\")return r;if(e===\"relu\")return $(r,No(t));throw new Error(`Cannot compute gradient for fused activation ${e}.`)}function Sc(r,t){let e=t,n=ye(r.shape,t.shape);return n.length>0&&(e=pt(e,n)),R(e,r.shape)}function Nc(r,t,e,n){if(t===\"linear\")return r;if(t===\"relu\")return Pr(r);if(t===\"elu\")return ia(r);if(t===\"relu6\")return xm(r);if(t===\"prelu\")return _u(r,e);if(t===\"leakyrelu\")return vu(r,n);if(t===\"sigmoid\")return tn(r);throw new Error(`Unknown fused activation ${t}.`)}var kc=(r,t)=>!(r>0)||t===\"linear\";function N5({x:r,filter:t,strides:e,pad:n,dataFormat:o=\"NHWC\",dilations:s=[1,1],dimRoundingMode:i,bias:a,activation:u=\"linear\",preluActivationWeights:l,leakyreluAlpha:c}){if(u=u||\"linear\",kc(T.state.gradientDepth,u)===!1){_(o===\"NHWC\",()=>`Error in fused conv2d: got dataFormat of ${o} but only NHWC is currently supported for the case of gradient depth is 0 and 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Got strides ${e} and dilations '${s}'`),Se(\"fused depthwiseConv2d\",n,i);let h=hc(f.shape,m.shape,e,s,n,i,!0),g;a!=null&&(g=v(a,\"bias\",\"fused conv2d\"),[g]=jt(g,p),Mt(h.outShape,g.shape));let x;l!=null&&(x=v(l,\"prelu weights\",\"fused depthwiseConv2d\"));let b=(N,E)=>{_(ao(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[A,D,F,P]=E,V=vc(N,F,u),G=yy(D.shape,V,A,e,n,s,i),W=xy(D,V,A.shape,e,n,s,i);if(P!=null){let q=Sc(g,V);return[G,W,q]}return[G,W]},w={x:f,filter:m,bias:g,preluActivationWeights:x},I={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i,activation:u,leakyreluAlpha:c};return a==null?mn((E,A,D)=>{let F=T.runKernel(Xi,w,I);return D([A,E,F]),d&&(F=R(F,[F.shape[1],F.shape[2],F.shape[3]])),{value:F,gradFunc:b}})(f,m):mn((E,A,D,F)=>{let P=T.runKernel(Xi,w,I);return F([A,E,P,D]),d&&(P=R(P,[P.shape[1],P.shape[2],P.shape[3]])),{value:P,gradFunc:b}})(f,m,g)}var SA=k({fusedDepthwiseConv2d_:_5});function E5({a:r,b:t,transposeA:e=!1,transposeB:n=!1,bias:o,activation:s=\"linear\",preluActivationWeights:i,leakyreluAlpha:a=.2}){if(kc(T.state.gradientDepth,s)===!1){let V=Bt(r,t,e,n);return o!=null&&(V=Y(V,o)),Nc(V,s,i,a)}let u=v(r,\"a\",\"fused matMul\"),l=v(t,\"b\",\"fused matMul\");[u,l]=jt(u,l);let c=e?u.shape[u.rank-2]:u.shape[u.rank-1],p=n?l.shape[l.rank-1]:l.shape[l.rank-2],m=e?u.shape[u.rank-1]:u.shape[u.rank-2],f=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=u.shape.slice(0,-2),h=l.shape.slice(0,-2),g=te(d),x=te(h);_(c===p,()=>`Error in fused matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${u.shape} and ${l.shape} and transposeA=${e} and transposeB=${n} must match.`);let w=Mt(u.shape.slice(0,-2),l.shape.slice(0,-2)).concat([m,f]),I=e?R(u,[g,c,m]):R(u,[g,m,c]),N=n?R(l,[x,f,p]):R(l,[x,p,f]),E;o!=null&&(E=v(o,\"bias\",\"fused matMul\"),[E]=jt(E,u),Mt(w,E.shape));let A;i!=null&&(A=v(i,\"prelu weights\",\"fused matMul\"));let D=(V,G)=>{let[W,q,H,K]=G,X=vc(R(V,H.shape),H,s),Z,et;if(!e&&!n?(Z=Bt(X,q,!1,!0),et=Bt(W,X,!0,!1)):!e&&n?(Z=Bt(X,q,!1,!1),et=Bt(X,W,!0,!1)):e&&!n?(Z=Bt(q,X,!1,!0),et=Bt(W,X,!1,!1)):(Z=Bt(q,X,!0,!0),et=Bt(X,W,!0,!0)),o!=null){let nt=Sc(K,X);return[Z,et,nt]}else return[Z,et]},F={a:I,b:N,bias:E,preluActivationWeights:A},P={transposeA:e,transposeB:n,activation:s,leakyreluAlpha:a};return o==null?mn((G,W,q)=>{let H=T.runKernel(Ki,F,P);return q([G,W,H]),{value:R(H,w),gradFunc:D}})(I,N):mn((G,W,q,H)=>{let K=T.runKernel(Ki,F,P);return H([G,W,K,q]),{value:R(K,w),gradFunc:D}})(I,N,E)}var NA=k({fusedMatMul_:E5});function A5(r){return Ih(r,.54,.46)}var kA=k({hammingWindow_:A5});function D5(r){return Ih(r,.5,.5)}var by=k({hannWindow_:D5});function $5(r,t,e,n=!1,o=0){let s=0,i=[];for(;s+t<=r.size;)i.push(Ot(r,s,t)),s+=e;if(n)for(;s`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),_(a.rank===2&&a.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${l},4] but had shape ${a.shape}.`),_(u.rank===1&&u.shape[0]===l,()=>`Error in cropAndResize: boxInd must be have size [${l}] but had shape ${a.shape}.`),_(n.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${n.length}.`),_(n[0]>=1&&n[1]>=1,()=>`cropSize must be atleast [1,1], but was ${n}`),_(o===\"bilinear\"||o===\"nearest\",()=>`method must be bilinear or nearest, but was ${o}`);let c={image:i,boxes:a,boxInd:u},p={method:o,extrapolationValue:s,cropSize:n};return T.runKernel(Oa,c,p)}var _A=k({cropAndResize_:F5});function O5(r){let t=v(r,\"image\",\"flipLeftRight\",\"float32\");_(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let e={image:t};return T.runKernel(Ba,e,{})}var EA=k({flipLeftRight_:O5});function P5(r){let t=v(r,\"image\",\"grayscaleToRGB\"),e=t.rank-1,n=t.shape[e];_(t.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`),_(n===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${n}.`);let o=new Array(t.rank);return o.fill(1,0,e),o[e]=3,Fr(t,o)}var AA=k({grayscaleToRGB_:P5});function M5(r,t,e=0,n=.5){let o=v(r,\"image\",\"rotateWithOffset\",\"float32\");_(o.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${o.rank}.`);let s={image:o},i={radians:t,fillValue:e,center:n};return T.runKernel(fl,s,i)}var DA=k({rotateWithOffset_:M5});function ko(r,t,e,n,o,s){n==null&&(n=.5),o==null&&(o=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=r.shape[0];return e=Math.min(e,i),_(0<=n&&n<=1,()=>`iouThreshold must be in [0, 1], but was '${n}'`),_(r.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${r.rank}'`),_(r.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`),_(t.rank===1,()=>\"scores must be a 1D tensor\"),_(t.shape[0]===i,()=>`scores has incompatible shape with boxes. 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g=0;go&&l.push({score:t[g],boxIndex:g,suppressBeginIndex:0});l.sort(FA);let c=s>0?-.5/s:0,p=[],m=[];for(;p.length0;){let g=l.pop(),{score:x,boxIndex:b,suppressBeginIndex:w}=g;if(x=w;--N){let E=G5(r,b,p[N]);if(E>=n){I=!0;break}if(g.score=g.score*W5(n,c,E),g.score<=o)break}g.suppressBeginIndex=p.length,I||(g.score===x?(p.push(b),m.push(g.score)):g.score>o&&RA(l,g,FA))}let f=p.length,d=e-f;a&&d>0&&(p.push(...new Array(d).fill(0)),m.push(...new Array(d).fill(0)));let h={selectedIndices:p};return i&&(h.selectedScores=m),u&&(h.validOutputs=f),h}function G5(r,t,e){let n=r.subarray(t*4,t*4+4),o=r.subarray(e*4,e*4+4),s=Math.min(n[0],n[2]),i=Math.min(n[1],n[3]),a=Math.max(n[0],n[2]),u=Math.max(n[1],n[3]),l=Math.min(o[0],o[2]),c=Math.min(o[1],o[3]),p=Math.max(o[0],o[2]),m=Math.max(o[1],o[3]),f=(a-s)*(u-i),d=(p-l)*(m-c);if(f<=0||d<=0)return 0;let h=Math.max(s,l),g=Math.max(i,c),x=Math.min(a,p),b=Math.min(u,m),w=Math.max(x-h,0)*Math.max(b-g,0);return w/(f+d-w)}function W5(r,t,e){let n=Math.exp(t*e*e);return e<=r?n:0}function FA(r,t){return r.score-t.score||r.score===t.score&&t.boxIndex-r.boxIndex}async function U5(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY){let s=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),i=v(t,\"scores\",\"nonMaxSuppressionAsync\"),a=ko(s,i,e,n,o);e=a.maxOutputSize,n=a.iouThreshold,o=a.scoreThreshold;let u=await Promise.all([s.data(),i.data()]),l=u[0],c=u[1],{selectedIndices:p}=Iy(l,c,e,n,o);return s!==r&&s.dispose(),i!==t&&i.dispose(),Ke(p,\"int32\")}var OA=U5;function H5(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let i=v(r,\"boxes\",\"nonMaxSuppression\"),a=v(t,\"scores\",\"nonMaxSuppression\"),u=ko(i,a,e,n,o,s);e=u.maxOutputSize,n=u.iouThreshold,o=u.scoreThreshold,s=u.softNmsSigma;let l={boxes:i,scores:a},c={maxOutputSize:e,iouThreshold:n,scoreThreshold:o,softNmsSigma:s},p=T.runKernel(el,l,c);return{selectedIndices:p[0],selectedScores:p[1]}}var PA=k({nonMaxSuppressionWithScore_:H5});async function q5(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let i=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),a=v(t,\"scores\",\"nonMaxSuppressionAsync\"),u=ko(i,a,e,n,o,s);e=u.maxOutputSize,n=u.iouThreshold,o=u.scoreThreshold,s=u.softNmsSigma;let l=await Promise.all([i.data(),a.data()]),c=l[0],p=l[1],{selectedIndices:m,selectedScores:f}=vy(c,p,e,n,o,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Ke(m,\"int32\"),selectedScores:Ke(f)}}var MA=q5;function K5(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=v(r,\"boxes\",\"nonMaxSuppression\"),a=v(t,\"scores\",\"nonMaxSuppression\"),u=ko(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,m={boxes:i,scores:a},f={maxOutputSize:l,iouThreshold:c,scoreThreshold:p,padToMaxOutputSize:s},d=T.runKernel(tl,m,f);return{selectedIndices:d[0],validOutputs:d[1]}}var LA=k({nonMaxSuppressionPadded_:K5});async function j5(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let 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e=!1;this.accumulations=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var $c=class extends qr{static get className(){return\"RMSProp\"}constructor(t,e=.9,n=0,o=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=T.backend.epsilon()),t==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let 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For multi-output layers, use the functional API.\");this.checkShape(t),this.outputs=[t.inboundNodes[0].outputTensors[0]],this.inputs=DN(this.outputs[0])}this.inboundNodes=[],new Tl({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:_o(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(o=>o.shape),outputShapes:this.outputs[0].shape})}else{let o=t.apply(this.outputs[0]);if(Array.isArray(o))throw new TypeError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");this.checkShape(t),this.outputs=[o],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(t),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError(\"There are no layers in the model.\");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let t=this.layers.length-1;this.layers[t].outboundNodes=[],this.outputs=[this.layers[t].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(t,e){return this.model==null&&this.build(),this.model.call(t,e)}build(t){if(Gt(t),this.inputs.length===0||this.outputs.length===0)throw new TypeError(\"Sequential model cannot be built: model is empty. Add some layers first.\");this.model=new qn({inputs:this.inputs,outputs:this.outputs[0],name:this.name+\"_model\"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(t,e,n=console.log){this.built||this.build(),super.summary(t,e,n)}setWeights(t){this.model==null&&this.build(),this.model.setWeights(t)}evaluate(t,e,n={}){if(!this.built)throw new jr(\"The model needs to be compiled before being used.\");return this.model.evaluate(t,e,n)}async evaluateDataset(t,e){if(!this.built)throw new jr(\"The model needs to be compiled before being used.\");return this.model.evaluateDataset(t,e)}predict(t,e={}){return this.model==null&&this.build(),this.model.predict(t,e)}predictOnBatch(t){return this.model==null&&this.build(),this.model.predictOnBatch(t)}compile(t){this.build(),this.model.compile(t),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(t){this.model.optimizer=t}async fit(t,e,n={}){if(!this.built)throw new jr(\"The model needs to be compiled before being used.\");return this.model.fit(t,e,n)}async fitDataset(t,e){if(!this.built)throw new jr(\"The model needs to be compiled before being used.\");return this.model.fitDataset(t,e)}async trainOnBatch(t,e){return this.model.trainOnBatch(t,e)}static fromConfig(t,e,n={},o=!1){let s,i={};if(e instanceof Array){if(e[0].className==null||e[0].className===\"Merge\")throw new z(\"Legacy serialization format not supported yet.\");s=e}else y.assert(e.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=e.layers,delete e.layers,i=e;let a=new t(i);if(!(a instanceof xa))throw new Nt(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(let u of s){let c=In(u,void 0,o);o&&c.setFastWeightInitDuringBuild(!0),a.add(c)}return a}set stopTraining(t){if(this.model==null)throw new z(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new z(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");return this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let n={};n.className=e.getClassName(),n.config=e.getConfig(),t.push(n)}return{name:this.name,layers:t}}};xa.className=\"Sequential\";J.registerClass(xa);function T7(r){return new qn(r)}function _7(r){return new xa(r)}function BN(r){return qy(r)}function E7(r,t){wn.registerCallbackConstructor(r,t)}var nn=class extends J.Serializable{getConfig(){return{}}},fb=class extends nn{apply(t,e=1){return U$(t,e)}};fb.className=\"elu\";J.registerClass(fb);var db=class extends nn{apply(t){return wm(t)}};db.className=\"selu\";J.registerClass(db);var hb=class extends nn{apply(t){return Pr(t)}};hb.className=\"relu\";J.registerClass(hb);var gb=class extends nn{apply(t){return B(()=>uo(6,Pr(t)))}};gb.className=\"relu6\";J.registerClass(gb);var xb=class extends nn{apply(t){return t}};xb.className=\"linear\";J.registerClass(xb);var yb=class extends nn{apply(t){return tn(t)}};yb.className=\"sigmoid\";J.registerClass(yb);var bb=class extends nn{apply(t){return q$(t)}};bb.className=\"hardSigmoid\";J.registerClass(bb);var wb=class extends nn{apply(t){return ai(t)}};wb.className=\"softplus\";J.registerClass(wb);var Ib=class extends nn{apply(t){return H$(t)}};Ib.className=\"softsign\";J.registerClass(Ib);var Cb=class extends nn{apply(t){return ra(t)}};Cb.className=\"tanh\";J.registerClass(Cb);var rf=class extends nn{apply(t,e=-1){return Eu(t,e)}};rf.className=\"softmax\";J.registerClass(rf);var vb=class extends nn{apply(t,e=-1){return dm(t,e)}};vb.className=\"logSoftmax\";J.registerClass(vb);var Sb=class extends nn{apply(t,e=1){return B(()=>$(tn($(t,e)),t))}};Sb.className=\"swish\";J.registerClass(Sb);var Nb=class extends nn{apply(t){return B(()=>$(t,ra(ai(t))))}};Nb.className=\"mish\";J.registerClass(Nb);function di(r){return r.getClassName()}function VN(r,t={}){return fa(r,J.SerializationMap.getMap().classNameMap,t,\"activation\")}function hi(r){if(r==null){let t={};return t.className=\"linear\",t.config={},VN(t)}if(typeof r==\"string\"){let t={};return t.className=r,t.config={},VN(t)}else return r instanceof nn?r:VN(r)}function GN(r){if(r!=null&&typeof r!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var kb=class extends J.Serializable{},Lu=class extends kb{constructor(t){super(),GN(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=Te([1]);return this.hasL1&&(e=Y(e,pt($(this.l1,Ee(t))))),this.hasL2&&(e=Y(e,pt($(this.l2,Pc(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Lu.className=\"L1L2\";J.registerClass(Lu);function TR(r){return GN(r),new Lu({l1:r!=null?r.l1:null,l2:0})}function _R(r){return GN(r),new Lu({l2:r!=null?r.l2:null,l1:0})}var NR={l1l2:\"L1L2\"};function me(r){return Rm(r)}function kR(r,t={}){return fa(r,J.SerializationMap.getMap().classNameMap,t,\"regularizer\")}function Ce(r){if(r==null)return null;if(typeof r==\"string\"){let e={className:r in NR?NR[r]:r,config:{}};return kR(e)}else return r instanceof kb?r:kR(r)}var nf=class extends _t{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=St(t);let n=Pr(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};nf.className=\"ReLU\";J.registerClass(nf);var of=class extends _t{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return vu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};of.className=\"LeakyReLU\";J.registerClass(of);var sf=class extends _t{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER=\"zeros\",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=he(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ce(t.alphaRegularizer),this.alphaConstraint=Ve(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes==\"number\")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Gt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight(\"alpha\",e,\"float32\",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o(Oe(t),t===\"channelsFirst\"?Vt(r,[0,2,3,1]):r))}function WN(r,t){return B(()=>(Oe(t),t===\"channelsFirst\"?Vt(r,[0,2,3,4,1]):r))}function D7(r,t,e,n=1,o=\"valid\",s,i=1){return B(()=>{if(s==null&&(s=xn()),Oe(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.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(e!=null&&e.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s===\"channelsFirst\"&&(r=Vt(r,[0,2,1])),o===\"causal\")throw new Nt(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");let a=um(r,t,n,o===\"same\"?\"same\":\"valid\",\"NWC\",i);return e!=null&&(a=yn(a,e)),a})}function ER(r,t,e,n=[1,1],o=\"valid\",s,i,a=null){return B(()=>{if(s==null&&(s=xn()),Oe(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(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Bh(r,s);if(o===\"causal\")throw new Nt(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=Ru.conv2d({x:u,filter:t,strides:n,pad:o===\"same\"?\"same\":\"valid\",dilations:i,dataFormat:\"NHWC\",bias:e,activation:a}),s===\"channelsFirst\"&&(u=Vt(u,[0,3,1,2])),u})}function $7(r,t,e,n=[1,1,1],o=\"valid\",s,i){return B(()=>{if(s==null&&(s=xn()),Oe(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(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=WN(r,s);if(o===\"causal\")throw new Nt(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return a=$x(a,t,n,o===\"same\"?\"same\":\"valid\",\"NDHWC\",i),e!=null&&(a=yn(a,e)),s===\"channelsFirst\"&&(a=Vt(a,[0,4,1,2,3])),a})}var Kc=class extends _t{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",Kc.verifyArgs(e),this.rank=t,Qe(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Nt(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zu(e.kernelSize,t,\"kernelSize\"),this.strides=zu(e.strides==null?1:e.strides,t,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,hn(this.padding),this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Oe(this.dataFormat),this.activation=hi(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=he(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ve(e.biasConstraint),this.biasRegularizer=Ce(e.biasRegularizer),this.activityRegularizer=Ce(e.activityRegularizer),this.dilationRate=zu(e.dilationRate==null?1:e.dilationRate,t,\"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(t){if(co(\"kernelSize\"in t,\"required key 'kernelSize' not in config\"),typeof t.kernelSize!=\"number\"&&!Oy(t.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(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:di(this.activation),useBias:this.useBias,biasInitializer:_e(this.biasInitializer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},Bu=class extends Kc{constructor(t,e){super(t,e),this.kernel=null,Bu.verifyArgs(e),this.filters=e.filters,Qe(this.filters,\"filters\"),this.kernelInitializer=he(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ve(e.kernelConstraint),this.kernelRegularizer=Ce(e.kernelRegularizer)}build(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",o,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:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=Py(this.activation.getClassName());if(s!=null&&this.rank===2)n=ER(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=D7(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=ER(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=$7(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Nt(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Gt(t);let e=[],n=this.dataFormat===\"channelsLast\"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},El=class extends Bu{constructor(t){super(2,t),El.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Oy(t.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(t.kernelSize)}.`)}};El.className=\"Conv2D\";J.registerClass(El);var Al=class extends Bu{constructor(t){super(3,t),Al.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Al.className=\"Conv3D\";J.registerClass(Al);var cf=class extends El{constructor(t){if(super(t),this.inputSpec=[new Ie({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==4)throw new z(\"Input should have rank 4; Received input shape: \"+JSON.stringify(t));let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",o,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat===\"channelsFirst\"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=gi(u,m,c,this.padding),h=gi(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Vt(n,[0,2,3,1]));let x=pm(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(x=Vt(x,[0,3,1,2])),this.bias!=null&&(x=yn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s;this.dataFormat===\"channelsFirst\"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=gi(e[o],u,i,this.padding),e[s]=gi(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};cf.className=\"Conv2DTranspose\";J.registerClass(cf);var pf=class extends Al{constructor(t){if(super(t),this.inputSpec=[new Ie({ndim:5})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==5)throw new z(\"Input should have rank 5; Received input shape: \"+JSON.stringify(t));let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",o,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat===\"channelsFirst\"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=gi(l,h,m,this.padding),w=gi(c,g,f,this.padding),I=gi(p,x,d,this.padding),N=[s,b,w,I,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Vt(n,[0,2,3,4,1]));let E=Fx(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(E=Vt(E,[0,4,1,2,3])),this.bias!==null&&(E=yn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s,i;this.dataFormat===\"channelsFirst\"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=gi(e[o],c,a,this.padding),e[s]=gi(e[s],p,u,this.padding),e[i]=gi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};pf.className=\"Conv3DTranspose\";J.registerClass(pf);var Tb=class extends Bu{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(e.padding!=null&&e.padding!==\"same\"&&e.padding!==\"valid\")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=he(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ce(e.depthwiseRegularizer),this.depthwiseConstraint=Ve(e.depthwiseConstraint),this.pointwiseInitializer=he(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ce(e.pointwiseRegularizer),this.pointwiseConstraint=Ve(e.pointwiseConstraint)}build(t){if(t=Gt(t),t.length{t=St(t);let n;if(this.rank===1)throw new Nt(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(t=Vt(t,[0,2,3,1])),n=Im(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=Vt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=_e(this.depthwiseInitializer),t.pointwiseInitializer=_e(this.pointwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.pointwiseRegularizer=me(this.pointwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseConstraint),t.pointwiseConstraint=Be(this.pointwiseConstraint),t}};Tb.className=\"SeparableConv\";var mf=class extends Tb{constructor(t){super(2,t)}};mf.className=\"SeparableConv2D\";J.registerClass(mf);var Vu=class extends Bu{constructor(t){super(1,t),Vu.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Oy(t.kernelSize,\"number\",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};Vu.className=\"Conv1D\";J.registerClass(Vu);var ff=class extends _t{constructor(t){super(t),typeof t.cropping==\"number\"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]==\"number\"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?\"channelsLast\":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat===\"channelsFirst\"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat===\"channelsLast\"){let n=Ah(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ah(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ah(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ah(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};ff.className=\"Cropping2D\";J.registerClass(ff);var df=class extends _t{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Oe(this.dataFormat),this.interpolation=t.interpolation==null?\"nearest\":t.interpolation,L$(this.interpolation)}computeOutputShape(t){if(this.dataFormat===\"channelsFirst\"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat===\"channelsFirst\"){n=Vt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation===\"nearest\"?dn.resizeNearestNeighbor(n,[s,i]):dn.resizeBilinear(n,[s,i]);return Vt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation===\"nearest\"?dn.resizeNearestNeighbor(n,[s,i]):dn.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};df.className=\"UpSampling2D\";J.registerClass(df);function R7(r,t,e=[1,1],n=\"valid\",o,s){return B(()=>{o==null&&(o=xn()),Oe(o);let i=Bh(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=sa(i,t,e,n===\"same\"?\"same\":\"valid\",\"NHWC\",s),o===\"channelsFirst\"&&(i=Vt(i,[0,3,1,2])),i})}var hf=class extends Kc{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=he(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ve(t.depthwiseConstraint),this.depthwiseRegularizer=Ce(t.depthwiseRegularizer)}build(t){if(t=Gt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat===\"channelsFirst\"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",o,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(t,e){return B(()=>{t=St(t);let n=R7(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=En(e,this.kernelSize[0],this.padding,this.strides[0]),i=En(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=_e(this.depthwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseRegularizer),t}};hf.className=\"DepthwiseConv2D\";J.registerClass(hf);function UN(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z(\"When inputs is an array, neither initialState or constants should be provided\");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function HN(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(gn(2,u));if(t=Vt(t,l),s!=null)throw new Nt(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");i&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),o!=null&&(o=Q(Q(o,\"bool\"),\"float32\"),o.rank===u-1&&(o=ar(o,-1)),o=Vt(o,l)),n&&(t=hr(t,0),o!=null&&(o=hr(o,0)));let c=[],p,m=e,f=t.shape[0],d=xr(t),h;o!=null&&(h=xr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=lt(Ir(N),N),A=Y($(w[0],N),$(m[0],E)),D=m.map((F,P)=>Y($(w[1][P],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=qe(c,1)),[p,g,m]})}var An=class extends _t{constructor(t){super(t);let e;if(t.cell==null)throw new z(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(t.cell)?e=new Yc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ie({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return gn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Hy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ie({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new _n(\"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(o=>Te([n,o])):this.states_=[Te([n,this.cell.stateSize])];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Te([n,o])):this.states_[0]=Te([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let o=0;o$e(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=UN(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ie({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof rn){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} 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 a={training:o},l=HN((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=Te(t.shape);return e=pt(e,[1,2]),e=kl(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Gy(e,[1,n]):e):this.cell.stateSize>1?[Gy(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===An.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=In(o,n);return new t(Object.assign(e,{cell:s}))}};An.className=\"RNN\";J.registerClass(An);var Dl=class extends _t{},jc=class extends Dl{constructor(t){super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=t.units,Qe(this.units,\"units\"),this.activation=hi(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Oc([1,mi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Oc([1,mi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t),this.kernel=this.addWeight(\"kernel\",[t[t.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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0Ir(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0Ir(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=$o($(t,i),this.kernel.read()):s=$o(t,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=Y(s,$o(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:di(this.activation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),recurrentInitializer:_e(this.recurrentInitializer),biasInitializer:_e(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};jc.className=\"SimpleRNNCell\";J.registerClass(jc);var gf=class extends An{constructor(t){t.cell=new jc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};gf.className=\"SimpleRNN\";J.registerClass(gf);var Xc=class extends Dl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.resetAfter)throw new z(\"GRUCell does not support reset_after parameter set to true.\");this.units=t.units,Qe(this.units,\"units\"),this.activation=hi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=hi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Oc([1,mi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Oc([1,mi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t);let e=t[t.length-1];this.kernel=this.addWeight(\"kernel\",[e,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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0Ir(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0Ir(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};xf.className=\"GRU\";J.registerClass(xf);var $l=class extends Dl{constructor(t){super(t),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=t.units,Qe(this.units,\"units\"),this.activation=hi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=hi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Oc([1,mi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Oc([1,mi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Gt(t);let n=t[t.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 o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends bn{apply(u,l){let c=s.apply([i]),p=new Pu().apply([i]),m=s.apply([i*2]);return AN(AN(c,p),m)}},e.className=\"CustomInit\",e)}else o=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0Ir(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0Ir(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};yf.className=\"LSTM\";J.registerClass(yf);var Yc=class extends Dl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{pi(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(In(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return $h(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Uy(t(),e),a=()=>Ou(i,t,n);return!o||o<=1?$e(a().clone()):Array(o).fill(void 0).map(a).map(l=>$e(l.clone()))}var F7=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols==\"function\")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z(\"ConvRNN2D cell does not support constants\");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=Te(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new _n(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.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(()=>Te(s)):this.states_=[Te(s)];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Te(s)):this.states_[0]=Te(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let a=0;a$e(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e===\"channelsFirst\",l=t[u?3:2],c=t[u?4:3],p=En(l,o[0],s,i[0],a[0]),m=En(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};_b.className=\"ConvRNN2D\";var Zc=class extends $l{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,Qe(this.filters,\"filters\"),this.kernelSize=zu(n,2,\"kernelSize\"),this.kernelSize.forEach(u=>Qe(u,\"kernelSize\")),this.strides=zu(o||1,2,\"strides\"),this.strides.forEach(u=>Qe(u,\"strides\")),this.padding=s||\"valid\",hn(this.padding),this.dataFormat=i||\"channelsLast\",Oe(this.dataFormat),this.dilationRate=zu(a||1,2,\"dilationRate\"),this.dilationRate.forEach(u=>Qe(u,\"dilationRate\"))}build(t){var e;t=Gt(t);let n=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight(\"kernel\",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends bn{apply(m,f){let d=l.apply([c]),h=dr([c]),g=l.apply([c*2]);return Om([d,h,g])}},e.className=\"CustomInit\",e)}else u=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0Ir(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(nt,st,at)=>!st||!st[at]?nt:$(st[at],nt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0Ir(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[I,N,E,A]=gr(this.kernel.read(),a,w),[D,F,P,V]=this.useBias?gr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,I,D,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,P,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=gr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let K=this.recurrentActivation.apply(Y(c,h)),X=this.recurrentActivation.apply(Y(p,g)),Z=Y($(X,i),$(K,this.activation.apply(Y(m,x)))),et=$(this.recurrentActivation.apply(Y(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=F7(t,[\"units\"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=kn(t,e,this.strides,o||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?yn(s,n,this.dataFormat):s}recurrentConv(t,e){return kn(t,e,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Zc.className=\"ConvLSTM2DCell\";J.registerClass(Zc);var bf=class extends _b{constructor(t){let e=new Zc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};bf.className=\"ConvLSTM2D\";J.registerClass(bf);var Jc=class extends _t{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);if(0Uy(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Jc.className=\"Dropout\";J.registerClass(Jc);var wf=class extends Jc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};wf.className=\"SpatialDropout1D\";J.registerClass(wf);var If=class extends _t{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,Qe(this.units,\"units\"),this.activation=hi(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ve(t.kernelConstraint),this.biasConstraint=Ve(t.biasConstraint),this.kernelRegularizer=Ce(t.kernelRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Gt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[e,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]:e}}],this.built=!0}computeOutputShape(t){t=Gt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=Py(this.activation.getClassName()),s;return o!=null?s=$o(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=$o(n,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:di(this.activation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),biasInitializer:_e(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};If.className=\"Dense\";J.registerClass(If);var Cf=class extends _t{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Gt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to \"Flatten\" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[t[0],Do(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let o=[0];for(let s=2;s{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:di(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};vf.className=\"Activation\";J.registerClass(vf);var Sf=class extends _t{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),V$(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};Sf.className=\"RepeatVector\";J.registerClass(Sf);var Nf=class extends _t{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e{this.invokeCallHook(t,e);let n=St(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};Nf.className=\"Reshape\";J.registerClass(Nf);var kf=class extends _t{constructor(t){if(super(t),t.dims==null)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${t.dims} instead.`);let e=gn(1,t.dims.length+1);if(!y.arraysEqual(t.dims.slice().sort(),e))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(t.dims)+\" `dims` must contain consecutive integers starting from 1.\");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ie({ndim:this.dims.length+1})]}computeOutputShape(t){t=Gt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Vt(St(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};kf.className=\"Permute\";J.registerClass(kf);var Tf=class extends _t{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=St(t),o=-1;return dc(li(n,this.maskValue),o)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=-1,s=!0,i=dc(li(n,this.maskValue),o,s);return $(n,Q(i,n.dtype))})}};Tf.className=\"Masking\";J.registerClass(Tf);var _f=class extends _t{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(we(t.inputLength))}this.inputDim=t.inputDim,Qe(this.inputDim,\"inputDim\"),this.outputDim=t.outputDim,Qe(this.outputDim,\"outputDim\"),this.embeddingsInitializer=he(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ce(t.embeddingsRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.embeddingsConstraint=Ve(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight(\"embeddings\",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=St(t),li(t,vt(t))):null)}computeOutputShape(t){if(t=Gt(t),this.inputLength==null)return[...t,this.outputDim];let e=we(this.inputLength);if(e.length!==t.length-1)throw new z(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);n.dtype!==\"int32\"&&(n=en(n,\"int32\"));let o=Wy(this.embeddings.read(),R(n,[n.size]));return R(o,Gt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:_e(this.embeddingsInitializer),embeddingsRegularizer:me(this.embeddingsRegularizer),activityRegularizer:me(this.activityRegularizer),embeddingsConstraint:Be(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};_f.className=\"Embedding\";J.registerClass(_f);var Fl=class extends _t{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new Nt}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length1)throw new z(`Can not merge tensors with different batch sizes. 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${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e==\"number\"&&(e=[e,e]),r.dtype===\"complex64\"||t.dtype===\"complex64\")throw new Nt(\"batchDot is not implemented for complex64-type Tensors yet.\");let n=r.shape.length,o=t.shape.length;e==null&&(e=[n-1,o-2]);let s=e;return B(()=>{let i;if(n>o){i=n-o;let u=[];for(let l=0;ln){i=o-n;let u=[];for(let l=0;l0){let u;n>o?u=n+o-3:u=n-1;let l=[];for(let c=u;c\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let e=t[0],n=t[1];if(e.length>3||n.length>3)throw new Nt(\"Dot layer does not support tensors of 4D or higher rank yet.\");let o=this.interpretAxes(e,n);if(e[o[0]]!==n[o[1]])throw new z(`Dimension incompatibility: ${e[o[0]]} !== ${n[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new z(`A \\`Dot\\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],n=t[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,i)=>Vh(s,t[i].shape.length)):o=[Vh(this.axes,e.shape.length),Vh(this.axes,n.shape.length)],this.normalize&&(e=Rh(e,o[0]),n=Rh(n,o[1])),O7(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Vh(this.axes,t.length),Vh(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new Nt(\"Dot layer does not support tensors of 4D or higher rank yet.\");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Of.className=\"Dot\";J.registerClass(Of);var Pf=class extends _t{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return Ou(()=>Y(Pm(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};Pf.className=\"GaussianNoise\";J.registerClass(Pf);var Mf=class extends _t{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?Ou(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,Pm(n.shape,1,s))},()=>n,e.training||!1):n})}};Mf.className=\"GaussianDropout\";J.registerClass(Mf);var Lf=class extends _t{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||St(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(t);return Ou(()=>{let s=St(t),i=1.6732632423543772,a=1.0507009873554805,u=-i*a,l=pn(Wn(n),this.rate);l=en(l,\"float32\");let c=((1-this.rate)*(1+this.rate*u**2))**-.5,p=-c*u*this.rate,m=Y($(s,l),$(Y(l,-1),u));return Y($(m,c),p)},()=>St(t),e.training||!1)}return t})}};Lf.className=\"AlphaDropout\";J.registerClass(Lf);function Gh(r,t,e,n,o,s=.001){let i;if(r.rank===2)i=vx(r,t,e,n,o,s);else if(r.rank===3)i=Sx(r,t,e,n,o,s);else if(r.rank===4)i=Nx(r,t,e,n,o,s);else throw new Nt(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return i}function P7(r,t,e,n,o=.001){return B(()=>{let s=yc(r,n),i=s.mean,a=s.variance;return[Gh(r,i,a,e,t,o),i,a]})}function M7(r,t,e,n,o=.001){return B(()=>{let s=yc(r,n),i=s.mean,a=s.variance,u=[];for(let d of gn(0,r.rank))n.indexOf(d)!==-1?u.push(1):u.push(r.shape[d]);let l=R(i,u),c=R(a,u),p=t==null?null:R(t,u),m=e==null?null:R(e,u);return[Gh(r,l,c,m,p,o),i,a]})}function L7(r,t,e,n,o=.001){return y.arraysEqual(n.slice().sort(),gn(0,r.rank-1))?P7(r,t,e,n,o):M7(r,t,e,n,o)}var zf=class extends _t{constructor(t){t==null&&(t={}),super(t),this.supportsMasking=!0,this.axis=t.axis==null?-1:t.axis,this.momentum=t.momentum==null?.99:t.momentum,this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=he(t.betaInitializer||\"zeros\"),this.gammaInitializer=he(t.gammaInitializer||\"ones\"),this.movingMeanInitializer=he(t.movingMeanInitializer||\"zeros\"),this.movingVarianceInitializer=he(t.movingVarianceInitializer||\"ones\"),this.betaConstraint=Ve(t.betaConstraint),this.gammaConstraint=Ve(t.gammaConstraint),this.betaRegularizer=Ce(t.betaRegularizer),this.gammaRegularizer=Ce(t.gammaRegularizer)}build(t){t=Gt(t);let e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ie({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=gn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=_o(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,gn(0,i).slice(0,i-1)),m=()=>{if(p){let b=R(this.movingMean.read(),l),w=R(this.movingVariance.read(),l),I=this.center?R(this.beta.read(),l):null,N=this.scale?R(this.gamma.read(),l):null;return Gh(o,b,w,I,N,this.epsilon)}else return Gh(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=L7(o,this.gamma.read(),this.beta.read(),a,this.epsilon),g=(b,w,I)=>{B(()=>{let N=1-I,E=b.read(),A=$(lt(E,w),N);b.write(lt(E,A))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_e(this.betaInitializer),gammaInitializer:_e(this.gammaInitializer),movingMeanInitializer:_e(this.movingMeanInitializer),movingVarianceInitializer:_e(this.movingVarianceInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer),betaConstraint:Be(this.betaConstraint),gammaConstraint:Be(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};zf.className=\"BatchNormalization\";J.registerClass(zf);var Bf=class extends _t{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.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 e of this.axis)if(!Number.isInteger(e))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=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=he(t.betaInitializer||\"zeros\"),this.gammaInitializer=he(t.gammaInitializer||\"ones\"),this.betaRegularizer=Ce(t.betaRegularizer),this.gammaRegularizer=Ce(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Gt(t);let e=t.length;typeof this.axis==\"number\"&&(this.axis=[this.axis]);for(let s=0;s=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==Ao(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight(\"gamma\",n,\"float32\",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight(\"beta\",n,\"float32\",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=yc(n,this.axis,!0),l=_o(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.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(e==null&&(e=xn()),e!==\"channelsLast\"&&e!==\"channelsFirst\")throw new z(`Unknown data format: ${e}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return e===\"channelsFirst\"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],fn(r,n)})}var Vf=class extends _t{constructor(t){if(t==null&&(t={}),super(t),this.dataFormat=t.dataFormat==null?xn():t.dataFormat,t.padding==null)this.padding=[[1,1],[1,1]];else if(typeof t.padding==\"number\")this.padding=[[t.padding,t.padding],[t.padding,t.padding]];else{if(t.padding=t.padding,t.padding.length!==2)throw new z(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${t.padding.length} array.`);let e,n;if(typeof t.padding[0]==\"number\")e=[t.padding[0],t.padding[0]],n=[t.padding[1],t.padding[1]];else{if(t.padding=t.padding,t.padding[0].length!==2)throw new z(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${t.padding[0].length} array.`);if(e=t.padding[0],t.padding[1].length!==2)throw new z(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){t=Gt(t);let e,n;return this.dataFormat===\"channelsFirst\"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?n=t[3]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],t[1],e,n]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?n=t[2]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],e,n,t[3]])}call(t,e){return B(()=>z7(St(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};Vf.className=\"ZeroPadding2D\";J.registerClass(Vf);function Fb(r,t,e,n,o,s){return B(()=>{Oe(o),kN(s),hn(n),e==null&&(e=[1,1]),n==null&&(n=\"valid\"),o==null&&(o=xn()),s==null&&(s=\"max\"),r=Bh(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=ku(r,t,e,a):i=bu(r,t,e,a),o===\"channelsFirst\"&&(i=Vt(i,[0,3,1,2])),i})}function AR(r,t,e,n,o,s){return B(()=>{Oe(o),kN(s),hn(n),e==null&&(e=[1,1,1]),n==null&&(n=\"valid\"),o==null&&(o=xn()),s==null&&(s=\"max\"),r=WN(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=Zx(r,t,e,a):i=Cx(r,t,e,a),o===\"channelsFirst\"&&(i=Vt(i,[0,4,1,2,3])),i})}var Eb=class extends _t{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize==\"number\")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]==\"number\")this.poolSize=t.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(t.poolSize)}`);if(Qe(this.poolSize,\"poolSize\"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides==\"number\")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]==\"number\")this.strides=t.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(t.strides)}`);Qe(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,hn(this.padding),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){t=Gt(t);let e=En(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=kl(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return Un(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Gf=class extends Eb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),Fb(t,e,n,o,s,\"max\")}};Gf.className=\"MaxPooling1D\";J.registerClass(Gf);var Wf=class extends Eb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),Fb(t,e,n,o,s,\"avg\")}};Wf.className=\"AveragePooling1D\";J.registerClass(Wf);var Ab=class extends _t{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.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 ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];Qe(this.poolSize,\"poolSize\"),Qe(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Oe(this.dataFormat),hn(this.padding),this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2];return e=En(e,this.poolSize[0],this.padding,this.strides[0]),n=En(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Uf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),Fb(t,e,n,o,s,\"max\")}};Uf.className=\"MaxPooling2D\";J.registerClass(Uf);var Hf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),Fb(t,e,n,o,s,\"avg\")}};Hf.className=\"AveragePooling2D\";J.registerClass(Hf);var Db=class extends _t{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.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 ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];Qe(this.poolSize,\"poolSize\"),Qe(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Oe(this.dataFormat),hn(this.padding),this.inputSpec=[new Ie({ndim:5})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[4]:t[3];return e=En(e,this.poolSize[0],this.padding,this.strides[0]),n=En(n,this.poolSize[1],this.padding,this.strides[1]),o=En(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},qf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),AR(t,e,n,o,s,\"max\")}};qf.className=\"MaxPooling3D\";J.registerClass(qf);var Kf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),AR(t,e,n,o,s,\"avg\")}};Kf.className=\"AveragePooling3D\";J.registerClass(Kf);var $b=class extends _t{constructor(t){super(t),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new Nt}},jf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return ke(n,1)})}};jf.className=\"GlobalAveragePooling1D\";J.registerClass(jf);var Xf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Sr(n,1)})}};Xf.className=\"GlobalMaxPooling1D\";J.registerClass(Xf);var Rb=class extends _t{constructor(t){super(t),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Oe(this.dataFormat),this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat===\"channelsLast\"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new Nt}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Yf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?ke(n,[1,2]):ke(n,[2,3])})}};Yf.className=\"GlobalAveragePooling2D\";J.registerClass(Yf);var Zf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?Sr(n,[1,2]):Sr(n,[2,3])})}};Zf.className=\"GlobalMaxPooling2D\";J.registerClass(Zf);var Ob=class extends _t{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}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(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=In(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},Jf=class extends Ob{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Gt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Gt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),HN((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Jf.className=\"TimeDistributed\";J.registerClass(Jf);function B7(r){da(P$,\"BidirectionalMergeMode\",r)}var V7=\"concat\",Qf=class extends Ob{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=In(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=In(o),this.forwardLayer.name=\"forward_\"+this.forwardLayer.name,this.backwardLayer.name=\"backward_\"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?V7:t.mergeMode,B7(this.mergeMode),t.weights)throw new Nt(\"weights support is not implemented for Bidirectional layer yet.\");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode===\"concat\"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):kr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=UN(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];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.\");e.initialState=n,i.push(...n);let c=n.map(p=>new Ie({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new Nt(\"Support for constants in Bidirectional layers is not implemented yet.\");let u=i[0]instanceof rn;for(let l of i)if(l instanceof rn!==u)throw new z(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=hr(s,1));let a;return this.mergeMode===\"concat\"?a=Om([o,s]):this.mergeMode===\"sum\"?a=Y(o,s):this.mergeMode===\"ave\"?a=$(.5,Y(o,s)):this.mergeMode===\"mul\"?a=$(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){pi(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),pi(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=In(e.layer);if(delete e.layer,e.numConstants!=null)throw new Nt(\"Deserialization of a Bidirectional layer with numConstants present is not supported yet.\");let o=e;return o.layer=n,new t(o)}};Qf.className=\"Bidirectional\";J.registerClass(Qf);var td=class extends _t{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>(t=St(t),t.dtype!==\"float32\"&&(t=en(t,\"float32\")),Y($(t,this.scale),this.offset)))}};td.className=\"Rescaling\";J.registerClass(td);var{resizeBilinear:G7,cropAndResize:W7}=dn,ed=class extends _t{constructor(t){super(t),this.height=t.height,this.width=t.width}centerCrop(t,e,n,o,s,i,a,u){return B(()=>{let l,c=!1,p=e/i,m=n/a,f=(o+e)/i,d=(s+n)/a,h=[p,m,f,d],g=[];t.rank===3?(c=!0,l=qe([t])):l=t;for(let N=0;N{let s=G7(t,[e,n]);return en(s,o)})}call(t,e){return B(()=>{let n=St(t),o=n.dtype,s=n.shape,i=s[s.length-3],a=s[s.length-2],u=0;i!==this.height&&(u=Math.floor((i-this.height)/2));let l=0;return a!==this.width&&(l=Math.floor((a-this.width)/2),l===0&&(l=1)),u>=0&&l>=0?this.centerCrop(n,u,l,this.height,this.width,i,a,o):this.upsize(t,this.height,this.width,o)})}getConfig(){let t={height:this.height,width:this.width},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Gt(t);let e=t.length-3,n=t.length-2;return t[e]=this.height,t[n]=this.width,t}};ed.className=\"CenterCrop\";J.registerClass(ed);function DR(r,t,e,n){let o=St(r);if(o.dtype!==\"int32\"&&(o=en(o,\"int32\")),t===\"int\")return o;let s=o.shape;if(o.rank===0&&(o=ar(o,-1)),t===\"oneHot\"&&o.shape[o.shape.length-1]!==1&&(o=ar(o,-1)),o.rank>2)throw new z(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${o.rank}.`);let i=[\"multiHot\",\"oneHot\"].includes(t),a=o,u;if(typeof n!=\"undefined\"&&t===\"count\"?u=gh(a,n,e,i):u=gh(a,[],e,i),t!==\"tfIdf\")return u;if(n)return $(u,n);throw new z(\"When outputMode is 'tfIdf', weights must be provided.\")}var rd=class extends _t{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode=\"multiHot\"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return t=Gt(t),t==null?[this.numTokens]:this.outputMode===\"oneHot\"&&t[t.length-1]!==1?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)}call(t,e){return B(()=>{t=St(t),t.dtype!==\"int32\"&&(t=en(t,\"int32\"));let n;if(typeof e.countWeights!=\"undefined\"){if(this.outputMode!==\"count\")throw new z(`countWeights is not used when outputMode !== count.\n Received countWeights=${e.countWeights}`);n=St(e.countWeights)}let o=Sr(t),s=xl(t),i=Fe(this.numTokens,o).bufferSync().get(0),a=pn(s,0).bufferSync().get(0);if(!(i&&a))throw new z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return DR(t,this.outputMode,this.numTokens,n)})}};rd.className=\"CategoryEncoding\";J.registerClass(rd);var H7=[\"bilinear\",\"nearest\"],$R=new Set(H7),nd=class extends _t{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if($R.has(t.interpolation))this.interpolation=t.interpolation;else throw new z(`Invalid interpolation parameter: ${t.interpolation} is not implemented`);else this.interpolation=\"bilinear\";this.cropToAspectRatio=!!t.cropToAspectRatio}computeOutputShape(t){t=Gt(t);let e=t[2];return[this.height,this.width,e]}getConfig(){let t={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>{let n=[this.height,this.width];if(this.interpolation===\"bilinear\")return dn.resizeBilinear(t,n,!this.cropToAspectRatio);if(this.interpolation===\"nearest\")return dn.resizeNearestNeighbor(t,n,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...$R]} are supported`)})}};nd.className=\"Resizing\";J.registerClass(nd);var Wh=class{constructor(t){this.seed=t}next(){if(this.seed!==void 0)return this.seed++}};Wh.className=\"RandomSeed\";var Uh=class extends _t{constructor(t){super(t),this.randomGenerator=new Wh(t.seed)}getConfig(){let t={seed:this.randomGenerator.seed},e=super.getConfig();return Object.assign(t,e),t}};Uh.className=\"BaseRandomLayer\";var q7=[\"bilinear\",\"nearest\"],RR=new Set(q7),od=class extends Uh{constructor(t){super(t);let{factor:e,interpolation:n=\"bilinear\"}=t;if(this.factor=e,Array.isArray(this.factor)&&this.factor.length===2)this.widthLower=this.factor[0],this.widthUpper=this.factor[1];else if(!Array.isArray(this.factor)&&this.factor>0)this.widthLower=-this.factor,this.widthUpper=this.factor;else throw new z(`Invalid factor: ${this.factor}. 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n=C(\"tensorArrayId\",r,t,e),o=C(\"indices\",r,t,e),s=C(\"dtype\",r,t,e);return[e.getTensorArray(n.id).gather(o,s)]}case\"TensorArrayScatterV3\":{let n=C(\"tensorArrayId\",r,t,e),o=C(\"indices\",r,t,e),s=C(\"tensor\",r,t,e),i=e.getTensorArray(n.id);return i.scatter(o,s),[i.idTensor]}case\"TensorArrayConcatV3\":{let n=C(\"tensorArrayId\",r,t,e),o=e.getTensorArray(n.id),s=C(\"dtype\",r,t,e);return[o.concat(s)]}case\"TensorArraySplitV3\":{let n=C(\"tensorArrayId\",r,t,e),o=C(\"tensor\",r,t,e),s=C(\"lengths\",r,t,e),i=e.getTensorArray(n.id);return i.split(s,o),[i.idTensor]}case\"TensorArraySizeV3\":{let n=C(\"tensorArrayId\",r,t,e),o=e.getTensorArray(n.id);return[ft(o.size(),\"int32\")]}case\"TensorArrayCloseV3\":{let n=C(\"tensorArrayId\",r,t,e),o=e.getTensorArray(n.id);return o.clearAndClose(),[o.idTensor]}case\"TensorListSetItem\":{let n=C(\"tensorListId\",r,t,e),o=C(\"index\",r,t,e),s=C(\"tensor\",r,t,e),i=e.getTensorList(n.id);return 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n=C(\"tensorListId\",r,t,e),o=C(\"elementShape\",r,t,e),s=C(\"elementDType\",r,t,e),i=C(\"numElements\",r,t,e);return[e.getTensorList(n.id).stack(o,s,i)]}case\"TensorListFromTensor\":{let n=C(\"tensor\",r,t,e),o=C(\"elementShape\",r,t,e),s=C(\"elementDType\",r,t,e),i=YR(n,o,s);return e.addTensorList(i),[i.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let n=C(\"tensorListId\",r,t,e),o=e.getTensorList(n.id),s=C(\"dtype\",r,t,e),i=C(\"elementShape\",r,t,e);return[o.concat(s,i)]}case\"TensorListPushBack\":{let n=C(\"tensorListId\",r,t,e),o=C(\"tensor\",r,t,e),s=e.getTensorList(n.id);return s.pushBack(o),[s.idTensor]}case\"TensorListPopBack\":{let n=C(\"tensorListId\",r,t,e),o=C(\"elementShape\",r,t,e),s=C(\"elementDType\",r,t,e);return[e.getTensorList(n.id).popBack(o,s)]}case\"TensorListSplit\":{let n=C(\"tensor\",r,t,e),o=C(\"elementShape\",r,t,e),s=C(\"lengths\",r,t,e),i=QR(n,s,o);return e.addTensorList(i),[i.idTensor]}case\"TensorListLength\":{let n=C(\"tensorListId\",r,t,e),o=e.getTensorList(n.id);return[ft(o.size(),\"int32\")]}case\"TensorListResize\":{let n=C(\"tensorListId\",r,t,e),o=C(\"size\",r,t,e),i=e.getTensorList(n.id).resize(o);return e.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function eF(r,t,e){let[n,o]=C(\"fusedOps\",r,t,e),s=n===\"biasadd\",i=!s,a=o===\"prelu\",u=n===\"fusedbatchnorm\",l=C(\"numArgs\",r,t,e);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&&s&&l!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(u)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=C(\"strides\",r,t,e),p=Hh(r,t,e),m=C(\"dataFormat\",r,t,e).toUpperCase(),f=C(\"dilations\",r,t,e),[d,h]=C(\"args\",r,t,e);i&&(h=d,d=void 0);let g=C(\"leakyreluAlpha\",r,t,e);return{stride:c,pad:p,dataFormat:m,dilations:f,biasArg:d,preluArg:h,activationFunc:o,leakyreluAlpha:g}}var rF=(r,t,e,n=ie)=>{switch(r.op){case\"Conv1D\":{let o=C(\"stride\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"dataFormat\",r,t,e).toUpperCase(),a=C(\"dilation\",r,t,e);return[n.conv1d(C(\"x\",r,t,e),C(\"filter\",r,t,e),o,s,i,a)]}case\"Conv2D\":{let o=C(\"strides\",r,t,e),s=Hh(r,t,e),i=C(\"dataFormat\",r,t,e).toUpperCase(),a=C(\"dilations\",r,t,e);return[n.conv2d(C(\"x\",r,t,e),C(\"filter\",r,t,e),[o[1],o[2]],s,i,[a[1],a[2]])]}case\"_FusedConv2D\":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=eF(r,t,e);return[n.fused.conv2d({x:C(\"x\",r,t,e),filter:C(\"filter\",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case\"FusedDepthwiseConv2dNative\":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=eF(r,t,e);return[n.fused.depthwiseConv2d({x:C(\"x\",r,t,e),filter:C(\"filter\",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let o=C(\"outputShape\",r,t,e),s=C(\"strides\",r,t,e),i=Hh(r,t,e);return[n.conv2dTranspose(C(\"x\",r,t,e),C(\"filter\",r,t,e),o,[s[1],s[2]],i)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let o=C(\"strides\",r,t,e),s=Hh(r,t,e),i=C(\"dilations\",r,t,e),a=C(\"dataFormat\",r,t,e).toUpperCase();return[n.depthwiseConv2d(C(\"input\",r,t,e),C(\"filter\",r,t,e),[o[1],o[2]],s,a,[i[1],i[2]])]}case\"Conv3D\":{let o=C(\"strides\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"dataFormat\",r,t,e).toUpperCase(),a=C(\"dilations\",r,t,e);return[n.conv3d(C(\"x\",r,t,e),C(\"filter\",r,t,e),[o[1],o[2],o[3]],s,i,[a[1],a[2],a[3]])]}case\"AvgPool\":{let o=C(\"strides\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"kernelSize\",r,t,e);return[n.avgPool(C(\"x\",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case\"MaxPool\":{let o=C(\"strides\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"kernelSize\",r,t,e);return[n.maxPool(C(\"x\",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case\"MaxPoolWithArgmax\":{let o=C(\"strides\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"kernelSize\",r,t,e),a=C(\"includeBatchInIndex\",r,t,e),{result:u,indexes:l}=n.maxPoolWithArgmax(C(\"x\",r,t,e),[i[1],i[2]],[o[1],o[2]],s,a);return[u,l]}case\"AvgPool3D\":{let o=C(\"strides\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"kernelSize\",r,t,e);return[n.avgPool3d(C(\"x\",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case\"MaxPool3D\":{let o=C(\"strides\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"kernelSize\",r,t,e);return[n.maxPool3d(C(\"x\",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case\"Dilation2D\":{let o=C(\"strides\",r,t,e),s=C(\"pad\",r,t,e),i=C(\"dilations\",r,t,e),a=o[1],u=o[2],l=i[1],c=i[2];return[n.dilation2d(C(\"x\",r,t,e),C(\"filter\",r,t,e),[a,u],s,[l,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var nF=(r,t,e,n=ie)=>{switch(r.op){case\"Fill\":{let o=C(\"shape\",r,t,e),s=C(\"dtype\",r,t,e),i=C(\"value\",r,t,e);return[n.fill(o,i,s)]}case\"LinSpace\":{let o=C(\"start\",r,t,e),s=C(\"stop\",r,t,e),i=C(\"num\",r,t,e);return[n.linspace(o,s,i)]}case\"Multinomial\":{let o=C(\"logits\",r,t,e),s=C(\"numSamples\",r,t,e),i=C(\"seed\",r,t,e);return[n.multinomial(o,s,i)]}case\"OneHot\":{let o=C(\"indices\",r,t,e),s=C(\"depth\",r,t,e),i=C(\"onValue\",r,t,e),a=C(\"offValue\",r,t,e),u=C(\"dtype\",r,t,e);return[n.oneHot(o,s,i,a,u)]}case\"Ones\":return[n.ones(C(\"shape\",r,t,e),C(\"dtype\",r,t,e))];case\"OnesLike\":return[n.onesLike(C(\"x\",r,t,e))];case\"RandomStandardNormal\":return[n.randomStandardNormal(C(\"shape\",r,t,e),C(\"dtype\",r,t,e),C(\"seed\",r,t,e))];case\"RandomUniform\":return[n.randomUniform(C(\"shape\",r,t,e),C(\"minval\",r,t,e),C(\"maxval\",r,t,e),C(\"dtype\",r,t,e))];case\"RandomUniformInt\":return[n.randomUniformInt(C(\"shape\",r,t,e),C(\"minval\",r,t,e),C(\"maxval\",r,t,e),C(\"seed\",r,t,e))];case\"Range\":{let o=C(\"start\",r,t,e),s=C(\"stop\",r,t,e),i=C(\"step\",r,t,e);return[n.range(o,s,i,C(\"dtype\",r,t,e))]}case\"TruncatedNormal\":{let o=C(\"shape\",r,t,e),s=C(\"mean\",r,t,e),i=C(\"stdDev\",r,t,e),a=C(\"seed\",r,t,e);return[n.truncatedNormal(o,s,i,C(\"dtype\",r,t,e),a)]}case\"Zeros\":return[n.zeros(C(\"shape\",r,t,e),C(\"dtype\",r,t,e))];case\"ZerosLike\":return[n.zerosLike(C(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function xk(r,t,e){let n=C(\"boxes\",r,t,e),o=C(\"scores\",r,t,e),s=C(\"maxOutputSize\",r,t,e),i=C(\"iouThreshold\",r,t,e),a=C(\"scoreThreshold\",r,t,e),u=C(\"softNmsSigma\",r,t,e);return{boxes:n,scores:o,maxOutputSize:s,iouThreshold:i,scoreThreshold:a,softNmsSigma:u}}var oF=async(r,t,e,n,o=ie)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l,softNmsSigma:c}=xk(r,t,e),p=await 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C(\"x\",r,t,e).map(c=>n.tensor1d(c.shape));case\"Size\":return[n.scalar(C(\"x\",r,t,e).size,\"int32\")];case\"Rank\":return[n.scalar(C(\"x\",r,t,e).rank,\"int32\")];case\"NoOp\":return[n.scalar(1)];case\"Print\":let i=C(\"x\",r,t,e),a=C(\"data\",r,t,e),u=C(\"message\",r,t,e),l=C(\"summarize\",r,t,e);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(u);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ft(this.size(),\"int32\")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),B(()=>{let o=xr(e),s=n.length,i=o.length;y.assert(s===i,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${i} elements.`);for(let a=0;a{let o=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let o=n.getHashTableHandleByName(r.name);if(o!=null)return[o];{let s=C(\"keyDType\",r,t,e),i=C(\"valueDType\",r,t,e),a=new tw(s,i);return n.addHashTable(r.name,a),[a.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let o=C(\"tableHandle\",r,t,e,n),s=C(\"keys\",r,t,e),i=C(\"values\",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let o=C(\"tableHandle\",r,t,e,n),s=C(\"keys\",r,t,e),i=C(\"defaultValue\",r,t,e);return[await n.getHashTableById(o.id).find(s,i)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let o=C(\"tableHandle\",r,t,e,n);return[n.getHashTableById(o.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var lF=(r,t,e,n=ie)=>{switch(r.op){case\"ResizeBilinear\":{let o=C(\"images\",r,t,e),s=C(\"size\",r,t,e),i=C(\"alignCorners\",r,t,e),a=C(\"halfPixelCenters\",r,t,e);return[n.image.resizeBilinear(o,[s[0],s[1]],i,a)]}case\"ResizeNearestNeighbor\":{let o=C(\"images\",r,t,e),s=C(\"size\",r,t,e),i=C(\"alignCorners\",r,t,e),a=C(\"halfPixelCenters\",r,t,e);return[n.image.resizeNearestNeighbor(o,[s[0],s[1]],i,a)]}case\"CropAndResize\":{let o=C(\"image\",r,t,e),s=C(\"boxes\",r,t,e),i=C(\"boxInd\",r,t,e),a=C(\"cropSize\",r,t,e),u=C(\"method\",r,t,e),l=C(\"extrapolationValue\",r,t,e);return[n.image.cropAndResize(o,s,i,a,u,l)]}case\"ImageProjectiveTransformV3\":{let o=C(\"images\",r,t,e),s=C(\"transforms\",r,t,e),i=C(\"outputShape\",r,t,e),a=C(\"fillValue\",r,t,e),u=C(\"interpolation\",r,t,e),l=C(\"fillMode\",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var uF=(r,t,e,n=ie)=>{switch(r.op){case\"Equal\":return[n.equal(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"NotEqual\":return[n.notEqual(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"Greater\":return[n.greater(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"GreaterEqual\":return[n.greaterEqual(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"Less\":return[n.less(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"LessEqual\":return[n.lessEqual(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"LogicalAnd\":return[n.logicalAnd(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"LogicalNot\":return[n.logicalNot(C(\"a\",r,t,e))];case\"LogicalOr\":return[n.logicalOr(C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"Select\":case\"SelectV2\":return[n.where(C(\"condition\",r,t,e),C(\"a\",r,t,e),C(\"b\",r,t,e))];case\"BitwiseAnd\":return[n.bitwiseAnd(C(\"a\",r,t,e),C(\"b\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var cF=(r,t,e,n=ie)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[n.matMul(C(\"a\",r,t,e),C(\"b\",r,t,e),C(\"transposeA\",r,t,e),C(\"transposeB\",r,t,e))];case\"Einsum\":return[n.einsum(C(\"equation\",r,t,e),...C(\"tensors\",r,t,e))];case\"Transpose\":return[n.transpose(C(\"x\",r,t,e),C(\"perm\",r,t,e))];case\"_FusedMatMul\":let[o,s]=C(\"fusedOps\",r,t,e),i=o===\"biasadd\",a=s===\"prelu\",u=C(\"numArgs\",r,t,e),l=C(\"leakyreluAlpha\",r,t,e);if(i){if(a&&u!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!a&&u!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,p]=C(\"args\",r,t,e);return[n.fused.matMul({a:C(\"a\",r,t,e),b:C(\"b\",r,t,e),transposeA:C(\"transposeA\",r,t,e),transposeB:C(\"transposeB\",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case\"MatrixBandPart\":return[n.linalg.bandPart(C(\"a\",r,t,e),C(\"numLower\",r,t,e),C(\"numUpper\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var pF=(r,t,e,n=ie)=>{switch(r.op){case\"EuclideanNorm\":return[n.euclideanNorm(C(\"x\",r,t,e),C(\"axis\",r,t,e),C(\"keepDims\",r,t,e))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[n.batchNorm(C(\"x\",r,t,e),C(\"mean\",r,t,e),C(\"variance\",r,t,e),C(\"offset\",r,t,e),C(\"scale\",r,t,e),C(\"epsilon\",r,t,e))];case\"FusedBatchNormV3\":return[n.batchNorm(C(\"x\",r,t,e),C(\"mean\",r,t,e),C(\"variance\",r,t,e),C(\"offset\",r,t,e),C(\"scale\",r,t,e),C(\"epsilon\",r,t,e))];case\"LRN\":return[n.localResponseNormalization(C(\"x\",r,t,e),C(\"radius\",r,t,e),C(\"bias\",r,t,e),C(\"alpha\",r,t,e),C(\"beta\",r,t,e))];case\"Softmax\":return[n.softmax(C(\"x\",r,t,e))];case\"LogSoftmax\":return[n.logSoftmax(C(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var mF=(r,t,e,n=ie)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(C(\"paramsNestedSplits\",r,t,e),C(\"paramsDenseValues\",r,t,e),C(\"indices\",r,t,e),C(\"outputRaggedRank\",r,t,e));return o.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(C(\"starts\",r,t,e),C(\"limits\",r,t,e),C(\"splits\",r,t,e));return[o,s]}case\"RaggedTensorToTensor\":return[n.raggedTensorToTensor(C(\"shape\",r,t,e),C(\"values\",r,t,e),C(\"defaultValue\",r,t,e),C(\"rowPartitionTensors\",r,t,e),C(\"rowPartitionTypes\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var fF=(r,t,e,n=ie)=>{switch(r.op){case\"Max\":{let a=C(\"axis\",r,t,e),u=C(\"keepDims\",r,t,e);return[n.max(C(\"x\",r,t,e),a,u)]}case\"Mean\":{let a=C(\"axis\",r,t,e),u=C(\"keepDims\",r,t,e);return[n.mean(C(\"x\",r,t,e),a,u)]}case\"Min\":{let a=C(\"axis\",r,t,e),u=C(\"keepDims\",r,t,e);return[n.min(C(\"x\",r,t,e),a,u)]}case\"Sum\":{let a=C(\"axis\",r,t,e),u=C(\"keepDims\",r,t,e);return[n.sum(C(\"x\",r,t,e),a,u)]}case\"All\":{let a=C(\"axis\",r,t,e),u=C(\"keepDims\",r,t,e);return[n.all(C(\"x\",r,t,e),a,u)]}case\"Any\":{let a=C(\"axis\",r,t,e),u=C(\"keepDims\",r,t,e);return[n.any(C(\"x\",r,t,e),a,u)]}case\"ArgMax\":{let a=C(\"axis\",r,t,e);return[n.argMax(C(\"x\",r,t,e),a)]}case\"ArgMin\":{let a=C(\"axis\",r,t,e);return[n.argMin(C(\"x\",r,t,e),a)]}case\"Prod\":{let a=C(\"axis\",r,t,e),u=C(\"keepDims\",r,t,e);return[n.prod(C(\"x\",r,t,e),a,u)]}case\"Cumprod\":{let a=C(\"axis\",r,t,e),u=C(\"exclusive\",r,t,e),l=C(\"reverse\",r,t,e);return[n.cumprod(C(\"x\",r,t,e),a,u,l)]}case\"Cumsum\":{let a=C(\"axis\",r,t,e),u=C(\"exclusive\",r,t,e),l=C(\"reverse\",r,t,e);return[n.cumsum(C(\"x\",r,t,e),a,u,l)]}case\"Bincount\":let o=C(\"x\",r,t,e),s=C(\"weights\",r,t,e),i=C(\"size\",r,t,e);return[n.bincount(o,s,i)];case\"DenseBincount\":{let a=C(\"x\",r,t,e),u=C(\"weights\",r,t,e),l=C(\"size\",r,t,e),c=C(\"binaryOutput\",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var dF=(r,t,e,n=ie)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let o=C(\"n\",r,t,e),s=C(\"axis\",r,t,e),i=C(\"tensors\",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case\"Gather\":{let o=C(\"x\",r,t,e),s=C(\"indices\",r,t,e);return[n.gather(o,n.cast(s,\"int32\"),0)]}case\"GatherV2\":{let o=C(\"axis\",r,t,e),s=C(\"batchDims\",r,t,e),i=C(\"x\",r,t,e),a=C(\"indices\",r,t,e);return[n.gather(i,n.cast(a,\"int32\"),o,s)]}case\"Reverse\":{let o=C(\"dims\",r,t,e),s=[];for(let a=0;a{let o=C(\"axis\",r,t,e),s=C(\"tensors\",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error(\"the input tensors shape does not match\");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case\"Unpack\":{let o=C(\"axis\",r,t,e),s=C(\"tensor\",r,t,e);return n.unstack(s,o)}case\"Tile\":{let o=C(\"reps\",r,t,e);return[n.tile(C(\"x\",r,t,e),o)]}case\"Split\":case\"SplitV\":{let o=C(\"axis\",r,t,e),s=C(\"numOrSizeSplits\",r,t,e),i=C(\"x\",r,t,e);return n.split(i,s,o)}case\"ScatterNd\":{let o=C(\"indices\",r,t,e),s=C(\"values\",r,t,e),i=C(\"shape\",r,t,e);return[n.scatterND(o,s,i)]}case\"GatherNd\":{let o=C(\"x\",r,t,e),s=C(\"indices\",r,t,e);return[n.gatherND(o,s)]}case\"SparseToDense\":{let o=C(\"sparseIndices\",r,t,e),s=C(\"outputShape\",r,t,e),i=C(\"sparseValues\",r,t,e),a=C(\"defaultValue\",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case\"TensorScatterUpdate\":{let o=C(\"indices\",r,t,e),s=C(\"values\",r,t,e),i=C(\"tensor\",r,t,e);return[n.tensorScatterUpdate(i,o,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var hF=(r,t,e,n=ie)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(C(\"indices\",r,t,e),C(\"values\",r,t,e),C(\"denseShape\",r,t,e),C(\"defaultValue\",r,t,e));return[o,s,i,a]}case\"SparseReshape\":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(C(\"inputIndices\",r,t,e),C(\"inputShape\",r,t,e),C(\"newShape\",r,t,e));return[o,s]}case\"SparseSegmentMean\":return[n.sparse.sparseSegmentMean(C(\"data\",r,t,e),C(\"indices\",r,t,e),C(\"segmentIds\",r,t,e))];case\"SparseSegmentSum\":return[n.sparse.sparseSegmentSum(C(\"data\",r,t,e),C(\"indices\",r,t,e),C(\"segmentIds\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var gF=(r,t,e,n=ie)=>{switch(r.op){case\"FFT\":return[n.fft(C(\"x\",r,t,e))];case\"IFFT\":return[n.ifft(C(\"x\",r,t,e))];case\"RFFT\":return[n.rfft(C(\"x\",r,t,e))];case\"IRFFT\":return[n.irfft(C(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var xF=(r,t,e,n=ie)=>{switch(r.op){case\"StaticRegexReplace\":return[n.string.staticRegexReplace(C(\"input\",r,t,e),C(\"pattern\",r,t,e),C(\"rewrite\",r,t,e),C(\"replaceGlobal\",r,t,e))];case\"StringNGrams\":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(C(\"data\",r,t,e),C(\"dataSplits\",r,t,e),C(\"separator\",r,t,e),C(\"nGramWidths\",r,t,e),C(\"leftPad\",r,t,e),C(\"rightPad\",r,t,e),C(\"padWidth\",r,t,e),C(\"preserveShortSequences\",r,t,e));return[o,s]}case\"StringSplit\":{let{indices:o,values:s,shape:i}=n.string.stringSplit(C(\"input\",r,t,e),C(\"delimiter\",r,t,e),C(\"skipEmpty\",r,t,e));return[o,s,i]}case\"StringToHashBucketFast\":return[n.string.stringToHashBucketFast(C(\"input\",r,t,e),C(\"numBuckets\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var yF=(r,t,e,n=ie)=>{switch(r.op){case\"Cast\":return[n.cast(C(\"x\",r,t,e),C(\"dtype\",r,t,e))];case\"ExpandDims\":{let o=C(\"axis\",r,t,e);return[n.expandDims(C(\"x\",r,t,e),o)]}case\"Squeeze\":{let o=C(\"axis\",r,t,e);return[n.squeeze(C(\"x\",r,t,e),o)]}case\"Reshape\":return[n.reshape(C(\"x\",r,t,e),C(\"shape\",r,t,e))];case\"EnsureShape\":return[n.ensureShape(C(\"x\",r,t,e),C(\"shape\",r,t,e))];case\"MirrorPad\":return[n.mirrorPad(C(\"x\",r,t,e),C(\"padding\",r,t,e),C(\"mode\",r,t,e))];case\"PadV2\":case\"Pad\":return[n.pad(C(\"x\",r,t,e),C(\"padding\",r,t,e),C(\"constantValue\",r,t,e))];case\"SpaceToBatchND\":{let o=C(\"blockShape\",r,t,e),s=C(\"paddings\",r,t,e);return[n.spaceToBatchND(C(\"x\",r,t,e),o,s)]}case\"BatchToSpaceND\":{let o=C(\"blockShape\",r,t,e),s=C(\"crops\",r,t,e);return[n.batchToSpaceND(C(\"x\",r,t,e),o,s)]}case\"DepthToSpace\":{let o=C(\"blockSize\",r,t,e),s=C(\"dataFormat\",r,t,e).toUpperCase();return[n.depthToSpace(C(\"x\",r,t,e),o,s)]}case\"BroadcastTo\":return[n.broadcastTo(C(\"x\",r,t,e),C(\"shape\",r,t,e))];case\"BroadcastArgs\":return[n.broadcastArgs(C(\"s0\",r,t,e),C(\"s1\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function yk(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case\"arithmetic\":return o(()=>KR(i,a,u));case\"basic_math\":return o(()=>jR(i,a,u));case\"control\":return tF(i,a,u);case\"convolution\":return o(()=>rF(i,a,u));case\"creation\":return o(()=>nF(i,a,u));case\"dynamic\":return oF(i,a,u);case\"evaluation\":return o(()=>sF(i,a,u));case\"image\":return o(()=>lF(i,a,u));case\"graph\":return o(()=>iF(i,a,u));case\"logical\":return o(()=>uF(i,a,u));case\"matrices\":return o(()=>cF(i,a,u));case\"normalization\":return o(()=>pF(i,a,u));case\"ragged\":return o(()=>mF(i,a,u));case\"reduction\":return o(()=>fF(i,a,u));case\"slice_join\":return o(()=>dF(i,a,u));case\"sparse\":return o(()=>hF(i,a,u));case\"spectral\":return o(()=>gF(i,a,u));case\"string\":return o(()=>xF(i,a,u));case\"transformation\":return o(()=>yF(i,a,u));case\"hash_table\":return aF(i,a,u,n);case\"custom\":let l=zb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Zb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Kh=class{constructor(t={},e={},n={},o={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?\"\":`${e.frameName}-${e.iterationId}`).join(\"/\"):\"\"}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),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 t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function bk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>Cn(m)[0]));n=n||[];let c=new Set(n.map(m=>Cn(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Gu(m)||vQ(m)||SQ(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&!l.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function bF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>Cn(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],i=g=>e.has(typeof g==\"string\"?g:g.name);function a(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let u=a([...o,...r.weights,...s]).filter(i),l=a([...u,...Object.values(r.nodes)]).filter(i),c=new Map(l.map(g=>[g.name,g])),p={};for(let g of l){p[g.name]=p[g.name]||0;for(let x of g.children)i(x)||(p[x.name]=Number.POSITIVE_INFINITY),p[x.name]=(p[x.name]||0)+1}let m=Object.entries(p).filter(([,g])=>g===0).map(([g])=>g),f=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(i))--p[b.name]===0&&(f.push(b.name),m.push(b.name))}let d=f.map(g=>c.get(g)),h=yQ(d,u);return bQ(h,u),h}function yQ(r,t){let e=new Map(r.map(i=>[i.name,i])),n=t.map(i=>i.name),o=new Set(n);for(;n.length>0;){let i=n.pop(),a=e.get(i);for(let u of a.children)!e.has(u.name)||o.has(u.name)||(o.add(u.name),n.push(u.name))}return r.filter(i=>o.has(i.name))}var id=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function bQ(r,t){let e=new Map(r.map((a,u)=>[a.name,u])),n=new Set(t.map(a=>a.name)),o=a=>n.has(typeof a==\"string\"?a:a.name),s=new Set(r.map(a=>a.name)),i=a=>s.has(typeof a==\"string\"?a:a.name);for(let a of r){for(let u of a.children.filter(i)){if(!e.has(u.name))throw new id(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new id(`Node ${a.name} is scheduled to run after its child ${u.name}.`)}if(!o(a))for(let u of a.inputs){if(!e.has(u.name))throw new id(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new id(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function wF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Gu(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;at[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new Qc(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+\"--\"+o.join(this.SEPARATOR)}compile(t,e){let n=bk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let l=e.map(p=>p.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${c}]. Missing the following inputs: [${o}]`)}let a=bF(this.graph,n),u=wF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return $e(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[Cn(m)[0]]),s=e.map(m=>Cn(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Kh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=Cn(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=yk(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);f[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,f,m,d,i,g.get(x.name))}return this.parent==null&&m.dispose(d),e.map(x=>pr(x,f,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){if(!(Gu(e)||i.has(t))){for(let u of n[t])u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length);for(let u of e.inputs){if(Gu(u))continue;let l=YN(u.name,n,o);if(l!=null)for(let c of l){if(!c||c.kept||s.has(c.id))continue;let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,n,o,s,i){function a(u){return Gu(u)||s.has(u.name)}if(!(Gu(t)||i==null))for(let u of i){if(a(u))continue;let l=YN(u.name,e,n);for(let c of l)!c||c.kept||o.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=L().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Kh(this.weightMap,o,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>pr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(I=>this.graph.nodes[Cn(I)[0]]),a=n.map(I=>Cn(I)[0]),u=new Set(a),l=a.map(I=>this.graph.nodes[I]);l.length===0&&(l=this._outputs);let{usedNodes:c,missingInputs:p,dynamicNode:m,syncInputs:f}=bk(t,l,this.weightMap,this._initNodes),d=[...i,...this.graph.weights,...this._initNodes||[]].map(I=>({node:I,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(I=>{let[N,E]=Cn(I),A=[];A[E]=t[I],h[N]=A});let g={},x=this.getFrozenTensorIds(h),b={};for(;d.length>0;){let I=this.processStack(i,d,e,h,b,x,u,g,c);await Promise.all(I)}m==null&&!o&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let w=l.filter(I=>!Gu(I)&&!pr(I.name,h,e)).map(I=>I.name);if(w.length>0){let I=\"\";throw m!=null&&(I=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${f}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${p}]. ${I}`)}return h}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m=\"\";if(p.node.op===\"Enter\"&&C(\"isConstant\",p.node,o,n)&&([m]=xi(p.node.name,n)),o[p.node.name]==null){let f=yk(p.node,o,n,this._resourceManager);m||([m]=xi(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=xi(a.name,n);s[u]||!i.has(a.name)||(a.op===\"Merge\"?a.inputNames.some(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=Cn(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=Cn(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=Cn(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var ew=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var NQ=\"?tfjs-format=file\",kQ=\"model.json\",jh=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},n=Mr){this.modelUrl=t,this.loadOptions=e,this.version=\"n/a\",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new ew}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let t=this.handler.load();return y.isPromise(t)?t.then(e=>this.loadSync(e)):this.loadSync(t)}loadSync(t){this.artifacts=t;let e=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=n,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let o=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Qc(qh.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=qh.Instance.transformGraph(t.modelInitializer);this.initializer=new Qc(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t==\"string\"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${t}'`);t=n[0]}if(t.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof Pt?[t]:t,n={};return e.forEach((o,s)=>n[this.structuredOutputKeys[s]]=o),n}return t}predict(t,e){let n=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(t,e){let n=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(t){var e;if(!(t instanceof Pt)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let i in s){let a=s[i];a.resourceId!=null&&(t[i]=this.resourceIdToCapturedInput[a.resourceId])}return t}t=Array.isArray(t)?t:[t];let n=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${t.length} input tensors provided.`);let o=0;return this.inputNodes.reduce((s,i)=>{var a,u,l;let c=(l=(u=(a=this.signature)===null||a===void 0?void 0:a.inputs)===null||u===void 0?void 0:u[i])===null||l===void 0?void 0:l.resourceId;return c!=null?s[i]=this.resourceIdToCapturedInput[c]:s[i]=t[o++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(t){if(this.resourceIdToCapturedInput={},this.initializerSignature){let 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tr{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},_k=class extends tr{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},Ek=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Co.getTensorsInContainer(t.value),n=this.transform(t.value),o=Co.getTensorsInContainer(n);for(let s of e)Co.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Ak=class extends tr{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},ow=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Co.getTensorsInContainer(t.value),n=await this.transform(t.value),o=Co.getTensorsInContainer(n);for(let s of e)Co.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},ep=class extends tr{constructor(){super(),this.outputQueue=new tp,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}}},Dk=class extends ep{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=Co.getTensorsInContainer(t.value),n=this.transform(t.value),o=Co.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)Co.isTensorInList(s,o)||s.dispose();return!0}},sw=class extends tr{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return\"TODO: fill in upstream of chained summaries 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At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(t={}){if(!L().get(\"IS_BROWSER\"))throw new Error(\"microphone API is only supported in browser environment.\");let e=new cd(t);return await e.start(),e}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 t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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 e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.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 t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},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(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),sr(n,e)}};var pd=class extends tr{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ke([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=ui([i,s,u,a],[1,4])}else this.cropBox=ui([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(t,e={}){if(!L().get(\"IS_BROWSER\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!t){if(t=document.createElement(\"video\"),!e.resizeWidth||!e.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new pd(t,e);return await n.start(),n}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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error(\"Could not obtain video from webcam.\");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=Ay.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=ar(Q(t,\"float32\"),0),n;n=dn.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,\"bilinear\");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error(\"Can not convert infinite video stream to array.\")}};var md=class{};var Zh=class extends tr{split(t){return new Pk(this,t)}},Pk=class extends Zh{constructor(t,e){super(),this.upstream=t,this.impl=new Mk(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Mk=class extends ep{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=\"\"}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===\"\"?!1:(this.outputQueue.push(this.carryover),this.carryover=\"\",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var uw=class extends tr{decodeUTF8(){return new Lk(this)}},Lk=class extends Zh{constructor(t){super(),this.upstream=t,this.impl=new zk(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},zk=class extends ep{constructor(t){if(super(),this.upstream=t,L().get(\"IS_BROWSER\"))this.decoder=new TextDecoder(\"utf-8\");else{let{StringDecoder:e}=wk();this.decoder=new e(\"utf8\")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let n;return L().get(\"IS_BROWSER\")?n=this.decoder.decode(e,{stream:!0}):n=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(n),!0}};var fd=class extends uw{constructor(t,e={}){super(),this.file=t,this.options=e,y.assert(t instanceof Uint8Array||(L().get(\"IS_BROWSER\")?t instanceof File||t instanceof Blob:!1),()=>\"FileChunkIterator only supports File, Blob and Uint8Array right now.\"),this.offset=e.offset||0,this.chunkSize=e.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,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new 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IT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],\"conv2d\");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat===\"channelsLast\",I=new le(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],D=w?N[1]:N[2],F=w?N[2]:1,P=w?1:N[1],V=I.strides[0],G=w?I.strides[1]:I.strides[2],W=w?I.strides[2]:1,q=w?1:I.strides[1],H=e.data.get(o.dataId).values,K=e.data.get(s.dataId).values,X=I.values;for(let Z=0;Z=m.inHeight)continue;let gt=it*E[0],It=et+mt*D;for(let Rt=0;Rt=m.inWidth)continue;let ge=gt+qt*E[1],ee=It+ce*F,xe=ge;for(let fe=0;fe=l.inDepth)continue;let Z=K*F[0],et=V+X*D[1];for(let nt=0;nt=l.inHeight)continue;let mt=Z+ot*F[1],gt=et+it*D[2];for(let It=0;It=l.inWidth)continue;let ce=mt+Ht*F[2],ge=gt+qt*l.inChannels,ee=ce;for(let xe=0;xeMath.cos(r)),fP={kernelName:ss,backendName:\"cpu\",kernelFunc:Gtt};var Wtt=At(is,r=>Math.cosh(r)),dP={kernelName:is,backendName:\"cpu\",kernelFunc:Wtt};function Utt(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=bt([d,h,g,f],\"float32\"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,I=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-P)*(m-1)/(g-1):0;for(let K=0;K1?F*(p-1)+K*q:.5*(F+V)*(p-1);if(X<0||X>p-1){for(let Z=0;Z1?P*(m-1)+st*H:.5*(P+G)*(m-1);if(at<0||at>m-1){for(let gt=0;gt1?P*(m-1)+Z*H:.5*(P+G)*(m-1);if(et<0||et>m-1){for(let at=0;atx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. 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at=y.locToIndex([H,K,Z,nt],G,y.computeStrides(P));W[at]=st}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),P,n.dtype),shape:P,dtype:n.dtype}}};var NP={kernelName:eu,backendName:\"cpu\",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,\"NHWC\",u);y.assert(s.rank===F.length,()=>`Error in ${eu}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let 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_P={kernelName:zp,backendName:\"cpu\",kernelFunc:Jtt};function Qtt(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],\"eluGrad\");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=0?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,\"float32\",s)}var EP={kernelName:Ma,backendName:\"cpu\",kernelFunc:Qtt};var tet=S.ERF_P,eet=S.ERF_A1,ret=S.ERF_A2,net=S.ERF_A3,oet=S.ERF_A4,set=S.ERF_A5,iet=At(La,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+tet*e);return t*(1-((((set*n+oet)*n+net)*n+ret)*n+eet)*n*Math.exp(-e*e))}),AP={kernelName:La,backendName:\"cpu\",kernelFunc:iet};function vd(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),Qt({inputs:{x:o},backend:e,attrs:{shape:a}})}var DP={kernelName:Fi,backendName:\"cpu\",kernelFunc:vd};var aet=Jt((r,t)=>r/t),eg=ne(cs,aet),rg={kernelName:cs,backendName:\"cpu\",kernelFunc:eg};function _w(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType(\"float32\",c),m=y.getTypedArrayFromDType(\"float32\",c);for(let g=0;g{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m=0&&w=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=S.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=Qt({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=Qt({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=dw(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var LP={kernelName:Oi,backendName:\"cpu\",kernelFunc:xet};function yet(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=Qt({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=_w(a,!0,e),l=Qt({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var zP={kernelName:Vp,backendName:\"cpu\",kernelFunc:yet};var bet=At(ys,r=>Number.isFinite(r)?1:0,\"bool\"),BP={kernelName:ys,backendName:\"cpu\",kernelFunc:bet};var wet=At(bs,r=>Math.abs(r)===1/0?1:0,\"bool\"),VP={kernelName:bs,backendName:\"cpu\",kernelFunc:wet};var Iet=At(ws,r=>Number.isNaN(r)?1:0,\"bool\"),GP={kernelName:ws,backendName:\"cpu\",kernelFunc:Iet};function Cet(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=hw(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var WP={kernelName:Ha,backendName:\"cpu\",kernelFunc:Cet};var vet=At(vs,r=>Math.log1p(r)),UP={kernelName:vs,backendName:\"cpu\",kernelFunc:vet};var Net=Jt((r,t)=>r&&t),ket=ne(qa,Net,null,\"bool\"),HP={kernelName:qa,backendName:\"cpu\",kernelFunc:ket};var Tet=At(Ka,r=>r?0:1,\"bool\"),qP={kernelName:Ka,backendName:\"cpu\",kernelFunc:Tet};var _et=Jt((r,t)=>r||t),Eet=ne(ja,_et,null,\"bool\"),KP={kernelName:ja,backendName:\"cpu\",kernelFunc:Eet};function Aet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;tt(o,\"LRN\");let l=o.shape[3],c=l-1,p=e.data.get(o.dataId).values,m=y.sizeFromShape(o.shape),f=new Float32Array(m);function d(h){let g=h%l,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),w=0;for(;x<=b;x++){let I=p[x];w+=I*I}return w}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. 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r=Et.getNumber(\"WEBGL_VERSION\");return r===0?0:qT(r)});Et.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",()=>Et.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!xu.isMobile());Et.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\",()=>KT(Et.getNumber(\"WEBGL_VERSION\")));Et.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\",()=>Et.getBool(\"WEBGL_FORCE_F16_TEXTURES\")?!1:Et.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\"));Et.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",()=>jT(Et.getNumber(\"WEBGL_VERSION\")));Et.registerFlag(\"WEBGL_FENCE_API_ENABLED\",()=>XT(Et.getNumber(\"WEBGL_VERSION\")));Et.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\",()=>Et.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0);Et.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});Et.registerFlag(\"WEBGL_FLUSH_THRESHOLD\",()=>xu.isMobile()?1:-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});Et.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\",()=>128);Et.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\",()=>!1);Et.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e5);Et.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\",()=>128);Et.registerFlag(\"WEBGL_EXP_CONV\",()=>!1);Et.registerFlag(\"SOFTWARE_WEBGL_ENABLED\",()=>Et.getBool(\"IS_TEST\"));Et.registerFlag(\"WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE\",()=>1/0);Et.registerFlag(\"WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE\",()=>!1);Et.registerFlag(\"WEBGL2_ISNAN_CUSTOM\",()=>!1);Et.registerFlag(\"ENGINE_COMPILE_ONLY\",()=>!1);function We(){let r,t,e,n,o,s,i,a,u,l;return L().getNumber(\"WEBGL_VERSION\")===2?(r=\"#version 300 es\",t=\"in\",e=\"out\",n=\"in\",o=\"texture\",s=\"outputColor\",i=\"out vec4 outputColor;\",a=L().getBool(\"WEBGL2_ISNAN_CUSTOM\")?`\n bool isnan_custom(float val) {\n uint floatToUint = floatBitsToUint(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan_custom(val.x),\n isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n }\n\n #define isnan(value) isnan_custom(value)\n `:\"\",u=\"\",l=`\n #define round(value) newRound(value)\n int newRound(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 newRound(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `):(r=\"\",t=\"attribute\",e=\"varying\",n=\"varying\",o=\"texture2D\",s=\"gl_FragColor\",i=\"\",a=`\n #define isnan(value) isnan_custom(value)\n bool isnan_custom(float val) {\n return (val > 0. || val < 1. || val == 0.) ? false : true;\n }\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n }\n `,u=`\n uniform float INFINITY;\n\n bool isinf(float val) {\n return abs(val) == INFINITY;\n }\n bvec4 isinf(vec4 val) {\n return equal(abs(val), vec4(INFINITY));\n }\n `,l=`\n int round(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 round(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function Ci(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join(\"\")}function fp(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join(\"\")}function lnt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function gL(r,t,e=\"index\"){let n=r.map((s,i)=>i),o=lnt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join(\"\")}function _d(r){let t=y.computeStrides(r).map(e=>e.toString());return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;\n }\n`}function Ed(){return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n`}var Lw=`\n const float FLOAT_MAX = 1.70141184e38;\n const float FLOAT_MIN = 1.17549435e-38;\n\n lowp vec4 encode_float(highp float v) {\n if (isnan(v)) {\n return vec4(255, 255, 255, 255);\n }\n\n highp float av = abs(v);\n\n if(av < FLOAT_MIN) {\n return vec4(0.0, 0.0, 0.0, 0.0);\n } else if(v > FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n } else if(v < -FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\n }\n\n highp vec4 c = vec4(0,0,0,0);\n\n highp float e = floor(log2(av));\n highp float m = exp2(fract(log2(av))) - 1.0;\n\n c[2] = floor(128.0 * m);\n m -= c[2] / 128.0;\n c[1] = floor(32768.0 * m);\n m -= c[1] / 32768.0;\n c[0] = floor(8388608.0 * m);\n\n highp float ebias = e + 127.0;\n c[3] = floor(ebias / 2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n`;var{getBroadcastDims:xL}=S;function yL(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:\"\"};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=zw(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push(\"uniform int outShape;\");break;case 2:n.push(\"uniform ivec2 outShape;\"),n.push(\"uniform int outShapeStrides;\");break;case 3:n.push(\"uniform ivec3 outShape;\"),n.push(\"uniform ivec2 outShapeStrides;\");break;case 4:n.push(\"uniform ivec4 outShape;\"),n.push(\"uniform ivec3 outShapeStrides;\");break;default:break}n.push(\"uniform ivec2 outTexShape;\")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:\"\"};`)});let o=n.join(`\n`),s=r.map(f=>unt(f,t,e.packedInputs,e.enableShapeUniforms)).join(`\n`),i=t.texShape,a=We(),u=mnt(a),l,c,p=hnt(a);return t.isPacked?(l=cnt(t.logicalShape,i,e.enableShapeUniforms),c=dnt(a)):(l=pnt(t.logicalShape,i,e.enableShapeUniforms),c=fnt(a)),e.packedInputs&&(p+=bnt),[p,u,c,o,l,s,e.userCode].join(`\n`)}function Dd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return Dnt(r,t);case 1:return Rnt(r,t);case 2:return Ont(r,t);case 3:return Mnt(r,t);case 4:return znt(r,t);case 5:return Bnt(r);case 6:return Vnt(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function bL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return Ant(r);case 1:return $nt(r,t);case 2:return Fnt(r,t);case 3:return Pnt(r,t);default:return Lnt(r,t)}}function unt(r,t,e=!1,n){let o=\"\";e?o+=bL(r,n):o+=Dd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=Gnt(r,t):o+=Wnt(r,t)),o}function cnt(r,t,e){switch(r.length){case 0:return wL();case 1:return wnt(r,t,e);case 2:return _nt(r,t,e);case 3:return Cnt(r,t,e);default:return Snt(r,t,e)}}function pnt(r,t,e){switch(r.length){case 0:return wL();case 1:return Int(r,t,e);case 2:return Ent(r,t,e);case 3:return vnt(r,t,e);case 4:return Nnt(r,t,e);case 5:return knt(r,t);case 6:return Tnt(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function mnt(r){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${r.texture2D}(textureSampler, uv).r;\n }\n `}function fnt(r){return`\n void setOutput(float val) {\n ${r.output} = vec4(val, 0, 0, 0);\n }\n `}function dnt(r){return`\n void setOutput(vec4 val) {\n ${r.output} = val;\n }\n `}function hnt(r){return`${r.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${r.varyingFs} vec2 resultUV;\n ${r.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${r.defineSpecialNaN}\n ${r.defineSpecialInf}\n ${r.defineRound}\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n int idiv(int a, int b, float sign) {\n int res = a / b;\n int mod = imod(a, b);\n if (sign < 0. && mod != 0) {\n res -= 1;\n }\n return res;\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${gnt}\n ${xnt}\n ${ynt}\n `}var gnt=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n int texelIndex = index / 2;\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,xnt=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n int texNumC, int row, int col) {\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,ynt=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n int texelsInBatch, int texelsInLogicalRow, int b,\n int row, int col) {\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,bnt=`\n float getChannel(vec4 frag, vec2 innerDims) {\n vec2 modCoord = mod(innerDims, 2.);\n return modCoord.x == 0. ?\n (modCoord.y == 0. ? frag.r : frag.g) :\n (modCoord.y == 0. ? frag.b : frag.a);\n }\n float getChannel(vec4 frag, int dim) {\n float modCoord = mod(float(dim), 2.);\n return modCoord == 0. ? frag.r : frag.g;\n }\n`;function wL(){return`\n int getOutputCoords() {\n return 0;\n }\n `}function wnt(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${n[1]}.0);\n }\n `:n[1]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${n[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);\n }\n `}function Int(r,t,e){return t[0]===1?e?`\n int getOutputCoords() {\n return int(resultUV.x * float(outTexShape[1]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.x * ${t[1]}.0);\n }\n `:t[1]===1?e?`\n int getOutputCoords() {\n return int(resultUV.y * float(outTexShape[0]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.y * ${t[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n return resTexRC.x * outTexShape[1] + resTexRC.y;\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n return resTexRC.x * ${t[1]} + resTexRC.y;\n }\n `}function Cnt(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec3(b, r, c);\n }\n `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n int index = resTexRC.x * ${n[1]} + resTexRC.y;\n\n int b = index / ${s};\n index -= b * ${s};\n\n int r = 2 * (index / ${o});\n int c = imod(index, ${o}) * 2;\n\n return ivec3(b, r, c);\n }\n `}function vnt(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n ${fp([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n`;let n=Ci([\"r\",\"c\",\"d\"],r);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n int index = resTexRC.x * ${t[1]} + resTexRC.y;\n ${n}\n return ivec3(r, c, d);\n }\n `}function Snt(r,t,e){if(e)return`\n ivec4 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatchN = texelsInBatch * outShape[1];\n\n int b2 = index / texelsInBatchN;\n index -= b2 * texelsInBatchN;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec4(b2, b, r, c);\n }\n `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[r.length-1]/2),s=o*Math.ceil(r[r.length-2]/2),i=s,a=\"\",u=\"b, r, c\";for(let l=2;l=1?c=\"coords = 0;\":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(`\n`);let m=\"\";i<2&&s>0?m=\"coords\":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+l]}`).join(\", \");let f=\"return outputValue;\",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(t.logicalShape)===1;if(s===1&&!h&&!x)f=`\n return vec4(outputValue.xy, outputValue.xy);\n `;else if(h&&!x)i===1?f=`\n return vec4(outputValue.x, outputValue.x, 0., 0.);\n `:f=`\n return vec4(outputValue.x);\n `;else if(a.length){let b=s-2,w=s-1;a.indexOf(b)>-1&&a.indexOf(w)>-1?f=\"return vec4(outputValue.x);\":a.indexOf(b)>-1?f=\"return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);\":a.indexOf(w)>-1&&(f=\"return vec4(outputValue.xx, outputValue.zz);\")}return`\n vec4 ${o}() {\n ${u} coords = getOutputCoords();\n ${c}\n vec4 outputValue = get${n}(${m});\n ${f}\n }\n 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L().get(\"ENGINE_COMPILE_ONLY\")?{program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(l),Object.assign({program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i},YT(r,t,l)))}function YT(r,t,e){let n=[],o=[],s,i,a,u=null,l=null;l=r.getUniformLocation(e,\"NAN\",!1),L().getNumber(\"WEBGL_VERSION\")===1&&(u=r.getUniformLocation(e,\"INFINITY\",!1));let c=!1;for(let p of t.variableNames){let m={name:p,uniform:r.getUniformLocation(e,p,c),offset:r.getUniformLocation(e,`offset${p}`,c)};t.enableShapeUniforms&&(m.shape=r.getUniformLocation(e,`${p}Shape`,c),m.texShape=r.getUniformLocation(e,`${p}TexShape`,c)),n.push(m)}if(t.enableShapeUniforms&&(s=r.getUniformLocation(e,\"outShape\",c),a=r.getUniformLocation(e,\"outShapeStrides\",c),i=r.getUniformLocation(e,\"outTexShape\",c)),t.customUniforms)for(let p of t.customUniforms)o.push(r.getUniformLocation(e,p.name,c));return{variablesLocations:n,customUniformLocations:o,infLoc:u,nanLoc:l,outShapeLocation:s,outShapeStridesLocation:a,outTexShapeLocation:i}}function IL(r,t){if(r.length!==t.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t.length} inputs`);r.forEach((e,n)=>{let o=e.logicalShape,s=t[n],i=s.shape;if(!y.arraysEqual(o,i))throw Error(`Binary was compiled with different shapes than the current args. 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Shape ${a} and ${u} must match`)})}function vL(r,t,e,n,o){t.program.enableShapeUniforms||(IL(t.inShapeInfos,e),IL([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):r.setOutputMatrixTexture(s.texture,i[0],i[1]),r.setProgram(t.webGLProgram),r.bindVertexArray(t.webGLProgram.vao),L().getNumber(\"WEBGL_VERSION\")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN);for(let u=0;u{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=zw(r.packedInputs,i.shape,u),m=\"\",f=\"\",d=\"\";if(c.length===1&&r.packedInputs){let N=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${N[0]>1}_${N[1]>1}`}else if(c.length===2&&!r.packedInputs)f=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let N=y.computeStrides(c);d=`${N[0]===u[1]}_${N[N.length-1]===u[1]}`}let h=i.shape.length,g=c.length===2&&y.arraysEqual(i.shape,u),x=y.sizeFromShape(i.shape)===1,b=S.getBroadcastDims(i.shape,e.shape),w=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(u,e.texData.texShape),I=r.packedInputs||c.length>2?\"\":`${u[0]>1}_${u[1]>1}`;n+=`${h}_${w}_${l?p:\"\"}_${c.length}_${x}_${b}_${g}_${m}_${f}_${d}_${I}_${a}`}else{let u=i.isUniform?\"uniform\":i.texData.texShape;n+=`${i.shape}_${u}_${a}`}});let o=r.userCode,s=r.constructor.name;return s+=\"_\"+n+\"_\"+o+`${L().getNumber(\"WEBGL_VERSION\")}`,s}function de(r){return L().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&r<=4}var Bw=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=qu.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let e=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n 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This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let t=this.gl;ht(t,()=>t.finish()),ht(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ht(t,()=>t.deleteFramebuffer(this.framebuffer)),ht(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ht(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ht(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),t1(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),e1(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),r1(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),a1(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),i1(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),o1(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),n1(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(Pw(this.gl,this.framebuffer),this.outputTexture=null),ht(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,n){return this.downloadMatrixDriver(t,()=>c1(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return p1(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return u1(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=l1(this.gl,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,n;if(L().getBool(\"WEBGL_FENCE_API_ENABLED\")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=()=>{let i=o.clientWaitSync(s,0,0);return i===o.ALREADY_SIGNALED||i===o.CONDITION_SATISFIED},e=s}else L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(e=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(e,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):n=()=>!0;return{query:e,isFencePassed:n}}downloadMatrixFromPackedTexture(t,e,n){return this.downloadMatrixDriver(t,()=>m1(this.gl,e,n))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=ZT(e));let n=RT(e);ht(e,()=>e.attachShader(n,this.vertexShader)),ht(e,()=>e.attachShader(n,t)),FT(e,n);let o=Object.assign(n,{vao:this.createVertexArray()});return this.debug&&lg(e,o),o}buildVao(t){this.setProgram(t),this.bindVertexArray(t.vao);let e=this.gl;ht(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),s1(e,t,this.vertexBuffer)}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ht(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&lg(this.gl,this.program),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?BT(this.gl,t,e):VT(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ht(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),GT(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=wa(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&lg(this.gl,this.program),Nd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}ht(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ht(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Sd(this.gl,L().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(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(t,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(t,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(t,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=Knt(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let n;\"setTimeoutCustom\"in L().platform&&(n=L().platform.setTimeoutCustom.bind(L().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),ug(this.gl,t,this.framebuffer),this.debug&&Nd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ug(this.gl,this.outputTexture,this.framebuffer),this.debug&&Nd(this.gl)):Pw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;ug(o,t,this.framebuffer),this.debug&&Nd(o),this.outputTexture=t,ht(o,()=>o.viewport(0,0,e,n)),ht(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),ht(this.gl,()=>this.gl.scissor(t,e,n,o))}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 Knt(r){let t=0;for(;t`${r}.${e}`)}function er(r,t){return t===1?[r]:d1(r,t)}function hz(r,t){if(r===1)return\"rc\";let e=\"\";for(let n=0;n ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let e=\"\";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n= ${n};\n bool rEdge = rp1 >= ${o};\n `}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}),\n cEdge ? 0. : getA(${e[1]}),\n rEdge ? 0. : getA(${e[2]}),\n rEdge || cEdge ? 0. : getA(${e[3]})`}};var Od=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let n=\"\";for(let o=0;o<4;o++){let s=\"thisRC = rc;\";o%2===1&&(s+=\"thisRC.z += 1;\"),o>1&&(s+=\"thisRC.y += 1;\"),n+=`\n ${s}\n ${o>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${o}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${o>0?\"}\":\"\"}\n `}this.userCode=`\n ${jnt(e,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?Ed():_d(t)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":t[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":t[2]};\n\n ${n}\n\n setOutput(result);\n }\n `}};function jnt(r,t){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${t?gL([\"r\",\"c\",\"d\"],\"inputShape\"):Ci([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var tI=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=xz(e,n),s=yz(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=gz(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===Lr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Lr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Lr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=xz(n,o),i=yz(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=gz(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}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 t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Xnt(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function gz(r,t,e,n,o){let s=Ynt(t,n),i;if(o){let[u,l]=wa(r[0],r[1]);i=u*l}else{let[u,l]=mp(r[0],r[1]);i=u*l}let a=Xnt(e,s);return i*a}function Ynt(r,t){switch(r){case Lr.PACKED_2X2_FLOAT32:return jw(t);case Lr.PACKED_2X2_FLOAT16:return Xw(t);case Lr.UNPACKED_FLOAT32:return Hw(t);case Lr.UNPACKED_FLOAT16:return qw(t);case Lr.PACKED_4X1_UNSIGNED_BYTE:return Kw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function Znt(r){return L().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?Lr.PACKED_2X2_FLOAT32:Lr.UNPACKED_FLOAT32:r?Lr.PACKED_2X2_FLOAT16:Lr.UNPACKED_FLOAT16}function xz(r,t){if(r===Zr.UPLOAD)return Lr.PACKED_2X2_FLOAT32;if(r===Zr.RENDER||r==null)return Znt(t);if(r===Zr.DOWNLOAD||r===Zr.PIXELS)return Lr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function yz(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var zr=class{constructor(t,e){this.variableNames=[\"A\"],this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${e}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},yr=\"if (isnan(x)) return x;\",bz=\"return x;\",h1=\"return abs(x);\";var wz=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",Iz=yr+`\n return (x < 0.0) ? 0.0 : x;\n`,Cz=yr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Ia=\"return x;\",vz=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var Nz=\"return x;\",kz=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,Tz=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,_z=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Ez=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Rn=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${e}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var eI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let e=t.length,n=er(\"rc\",e),o=zt(e),s=hz(e,n),i=n.slice(-2),a=e<=1?\"rc\":`vec2(${i.join(\",\")})`;this.userCode=`\n void main() {\n ${o} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${a}));\n }\n `}};var Qnt=Kr.whereImpl,tot=1e-7,eot=1e-4,rI={};function rot(r){return r in rI||(rI[r]={}),rI[r]}var not=L().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),oot=600;function sot(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*oot/1024/1024}var ju=class extends Wo{nextDataId(){return ju.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!L().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let e;if(t!=null){if(t instanceof hp)e=t;else{let n=jn(L().getNumber(\"WEBGL_VERSION\"),t);e=new hp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=jn(L().getNumber(\"WEBGL_VERSION\"));e=new hp(n),this.binaryCache=rot(L().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new tI(this.gpgpu),this.numMBBeforeWarning=sot(),this.texData=new Ta(this,Vn())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=kd(e),c=new cg(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((L().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||L().getBool(\"DEBUG\"))&&this.checkNumericalProblems(t),n===\"complex64\"&&t!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Zr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(L().getBool(\"DEBUG\")&&this.checkNumericalProblems(e),o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Zr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new Rn(a,Ia):m=new zr(a,Ia);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o===\"string\")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o===\"complex64\"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new Rn(o,Ia):d=new zr(o,Ia);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(L().getBool(\"DEBUG\")&&!L().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&L().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(i!==\"complex64\"&&L().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...ig(o))}this.pendingRead.set(t,[]),i!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let p;if(i===\"complex64\"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Vn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(i!=null){let f;u?f=new Rn(s,Ia):f=new zr(s,Ia);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(t,e.customTexShape),p=Vn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype===\"string\")try{let n=e.map(o=>y.decodeString(o));return bt(t.shape,t.dtype,n)}catch(n){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return bt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(\", \")}else a.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=not){return L().getBool(\"WEBGL_CPU_FORWARD\")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Vn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new eI(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new Qw(t.shape),n=!0;return this.runWebGLProgram(e,[t],t.dtype,null,n)}packedReshape(t,e){let n=[zl(t.shape),...Bl(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[zl(e),...Bl(e)],i=new Od(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let a=kd(s),u;o?u=new Vw(a):u=new Bw(a);let l=!0,c=[e!=null?e:ig(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===qu.DENSE){let x=i!=null?i:ig(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=L().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Ku(b.shape,x.shape)){let w=x,I=x.shape;x.shape=b.shape,x=this.packedReshape(x,I),l.push(x),b=this.texData.get(x.dataId),w.shape=I}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=SL(t,c,p),f=this.getAndSaveBinary(m,()=>CL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get(\"ENGINE_COMPILE_ONLY\")||vL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=L().get(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!L().getBool(\"WEBGL_LAZILY_UNPACK\")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(L().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=B(()=>{if(!L().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let t=L().getBool(\"DEBUG\");L().set(\"DEBUG\",!1);let e=this.abs(ft(1e-8)).dataSync()[0];if(L().set(\"DEBUG\",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?tot:eot}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=WT(n,u),e.texShape=p),s!=null){let m=kd(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=wa(p[0],p[1])),u?f=new Uw(m,g):f=new cg(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Zr.PIXELS:w.usage=Zr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let I=[[h,d]],N=!0,E=this.runWebGLProgram(f,[b],o,I,N),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,L().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=iot(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!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(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await kh(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Fw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=YT(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=a,t.outTexShapeLocation=u}}createTensorFromGPUData(t,e,n){t.channels=t.channels||\"RGBA\";let{texture:o,height:s,width:i,channels:a}=t,u=Vn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let l=u.writeTexture(o,e,n,s,i,a);return Vn().makeTensorFromDataId(l,e,n,u)}};ju.nextDataId=0;function iot(r,t){if(t===\"float32\"||t===\"complex64\")return r;if(t===\"int32\"||t===\"bool\"){let e=t===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew ju,2);var aDe={forceHalfFloat:Dz};var Pd=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var mo=class{constructor(t,e,n){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${t}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Yn=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var zo=class{constructor(t,e,n,o=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=de(s);let i=\"\";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(i=`\n ${zt(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?i+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:i+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let u=er(\"coords\",s);this.enableShapeUniforms?i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${t}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${i}\n\n setOutput(result);\n }\n `}};function rr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var $z={kernelName:xo,backendName:\"webgl\",kernelFunc:rr};function Fn(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,\"complex64\"),i=e.texData.get(s.dataId),a=rr({inputs:{x:n},backend:e}),u=rr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var Rz={kernelName:Op,backendName:\"webgl\",kernelFunc:Fn};var g1=\"return (a < 0.) ? b * a : a;\",x1=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function aot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),a=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new zo(x1,o.shape,i.shape):new mo(g1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],\"float32\");return e.disposeIntermediateTensorInfo(i),u}var Fz={kernelName:Is,backendName:\"webgl\",kernelFunc:aot};var y1=\"return (a < 0.) ? b * a : a;\",b1=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function lot(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new zo(b1,n.shape,o.shape):new mo(y1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],\"float32\")}var Oz={kernelName:Ps,backendName:\"webgl\",kernelFunc:lot};var Bo=\"if (isnan(x)) return x;\";function wt({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=L().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new Rn(i.shape,t):c=new zr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function ue({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype===\"complex64\"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[I,N]=w,E={dataId:I.dataId,dtype:I.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},D=new mo(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],ur(I.dtype,N.dtype))}),b=Fn({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||ur(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?S.fromUint8ToStringArray(d):d,x=u.dtype===\"string\"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),I=c.makeTensorInfo(w,p),N=c.texData.get(I.dataId);return N.values=b,I}let m=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,f;return m?f=new zo(t,u.shape,l.shape,e):f=new mo(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Vl(r,t=!1){if(r===\"linear\")return t?Nz:bz;if(r===\"relu\")return t?Tz:Iz;if(r===\"elu\")return t?kz:wz;if(r===\"relu6\")return t?_z:Cz;if(r===\"prelu\")return t?b1:y1;if(r===\"leakyrelu\")return t?x1:g1;if(r===\"sigmoid\")return t?Ez:vz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Md=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=de(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?\"i * 2, rc.y\":\"rc.y, i * 2\",f=s?\"rc.z, i * 2\":\"i * 2, rc.z\",d=o?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";a&&(u?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${a}\n }`:l?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${a}\n }`:g=`vec4 activation(vec4 x) {\n ${a}\n }`,x=\"result = activation(result);\");let b=i?\"result += getBiasAtOutCoords();\":\"\";i&&this.variableNames.push(\"bias\"),u&&this.variableNames.push(\"preluActivationWeights\"),l&&this.variableNames.push(\"leakyreluAlpha\");let w=\"rc.x\",I=\"rc.x\";t[0]`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!Ku(o.shape,u)&&!(c.texture!==null&&Ku(c.shape,u))?Lz(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var zz={kernelName:Bi,backendName:\"webgl\",kernelFunc:rt};var dg=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l=\"sumValue += dot(values, ones);\";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c=\"\";s%n>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${a}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${l}\n }\n\n int inIdx = inOffset + ${a};\n if (${u===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${l}\n } else if (${u===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${l}\n } else if (${u===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${l}\n }\n setOutput(sumValue);\n }\n `}};var nI=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=\"0.0\",u=\"\";e===\"prod\"?a=\"1.0\":e===\"min\"?(a=\"1.0 / 1e-20\",u=\"min\"):e===\"max\"&&(a=\"-1.0 / 1e-20\",u=\"max\");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), 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);\n\n ${m}\n } else if (${p===2}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${p===3}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function cot(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function Zn(r,t,e,n){let o=cot(r.shape),s=r;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],n=new Array(t);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=zt(this.rank),s=d1(\"rc\",this.rank),i=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes 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Md(N,E,[P,f,d],e,n,G,H,W,q),st=[A,D];if(s!=null&&st.push(s),W&&st.push(i),q){let at=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));st.push(at),F.push(at)}X=o.runWebGLProgram(nt,st,et)}let Z=rt({inputs:{x:X},backend:o,attrs:{shape:I}});F.push(X);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function mot(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return yp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var Wz={kernelName:Ki,backendName:\"webgl\",kernelFunc:mot};var Uz=\"return abs(x);\";function fot(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!==\"complex64\"){let s=e.texData.get(n.dataId),i=Zw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?o=new Rn(n.shape,Uz):o=new 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aI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${n.join(`\n `)}\n\n vec4 result = ${o};\n setOutput(result);\n }\n `}};function lI(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return rr({inputs:{x:n[0]},backend:e});if(n.length>L().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let u=Math.floor(n.length/2),l=lI({inputs:n.slice(0,u),backend:e}),c=lI({inputs:n.slice(u),backend:e});return lI({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>ur(u,l)),s=n.map(u=>u.shape),a=L().getBool(\"WEBGL_PACK\")?new aI(n[0].shape,s):new iI(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var Yz={kernelName:Ko,backendName:\"webgl\",kernelFunc:lI};function bot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"all\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Zn(h,h.dtype,\"all\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var Zz={kernelName:Ea,backendName:\"webgl\",kernelFunc:bot};function wot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"any\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Zn(h,h.dtype,\"any\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var Jz={kernelName:Aa,backendName:\"webgl\",kernelFunc:wot};var uI=class{constructor(t,e,n){this.variableNames=[\"A\"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,i];let a=e===\"max\"?\">\":\"<\",u=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${o}; i++) {\n int inIdx = ${u};\n float candidate = getA(batch, inIdx);\n if (candidate ${a} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var cI=class{constructor(t,e,n,o){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push(\"bestIndicesA\");let a=this.outputShape,u=a.length,l=zt(u),c=er(\"coords\",u),p,m;if(i===1){m=u+1;let D=zt(m);p=`\n ${D} sourceLocR = ${D}(${c.join()}, 0);\n ++${c[u-1]};\n ${D} sourceLocG = ${D}(${c.join()}, 0);\n ++${c[u-2]};\n ${D} sourceLocA = ${D}(${c.join()}, 0);\n --${c[u-1]};\n ${D} sourceLocB = ${D}(${c.join()}, 0);\n --${c[u-2]};`}else m=u,p=`\n ${l} sourceLocR = coords;\n ++${c[u-1]};\n ${l} sourceLocG = coords;\n ++${c[u-2]};\n ${l} sourceLocA = coords;\n --${c[u-1]};\n ${l} sourceLocB = coords;\n --${c[u-2]};`;let f=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),d=\".\"+f[m-1],h=f.map(D=>\"int \"+D),g=er(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=er(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=er(\"sourceLocB\",m-1).concat(\"inIdx.b\"),w=er(\"sourceLocA\",m-1).concat(\"inIdx.a\"),I=n===\"max\"?\"greaterThan\":\"lessThan\",N=o?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${w.join()})));`,E=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? getAChannel(${b.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,A=o?\"\":`\n float getBestIndicesAChannel(${h.join()}) {\n return getChannel(getBestIndicesA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${h.join()}) {\n return getChannel(getA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }\n ${A}\n void main() {\n ${l} coords = getOutputCoords();\n bool hasNextCol = ${c[u-1]} < ${a[u-1]-1};\n bool hasNextRow = ${c[u-2]} < ${a[u-2]-1};\n ${p}\n ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},\n sourceLocB${d}, sourceLocA${d}) * ${e};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${E};\n\n for (int i = 0; i < ${e}; i++) {\n inIdx = srcIdx;\n ${N}\n vec4 candidate = ${E};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${I}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}};function Qz(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new uI(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=Qz(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function t3(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new cI(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,\"int32\");if(l.shape.length===t.shape.length){let c=t3(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function pI(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!L().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=rt({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=Qz(r,m,n);s.push(f);let d=rt({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return t3(r,t,n)}function Iot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=pI(e,u,i[0],\"max\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var e3={kernelName:Ei,backendName:\"webgl\",kernelFunc:Iot};function Cot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=pI(e,u,i[0],\"min\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var r3={kernelName:Ai,backendName:\"webgl\",kernelFunc:Cot};var vot=yr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,Sot=wt({opSnippet:vot}),n3={kernelName:jo,backendName:\"webgl\",kernelFunc:Sot};var Not=yr+\"return log(x + sqrt(x * x + 1.0));\",kot=wt({opSnippet:Not}),o3={kernelName:Xo,backendName:\"webgl\",kernelFunc:kot};var Tot=yr+`\n return atan(x);\n`,_ot=wt({opSnippet:Tot}),s3={kernelName:Yo,backendName:\"webgl\",kernelFunc:_ot};var Eot=Pd+`\n return atan(a, b);\n`,Aot=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+Yn+`\n return result;\n`,Dot=ue({opSnippet:Eot,packedOpSnippet:Aot}),i3={kernelName:Jo,backendName:\"webgl\",kernelFunc:Dot};var $ot=yr+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Rot=wt({opSnippet:$ot}),a3={kernelName:Zo,backendName:\"webgl\",kernelFunc:Rot};var vi=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e===\"avg\",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),n){let D=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${D} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let w=\"max\",I=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(I=\"avgValue / max(count, 1.0)\");let N=Math.floor(i/4)*4,E=i%4,A=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${w}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${N}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${A}\n }\n\n int xC = xCCorner + ${N};\n if (${E===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${E===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${E===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${A}\n }\n }\n setOutput(${I});\n }\n `}},Yu=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e===\"avg\",I=\"0.0\";if(w||(I=\"-1.0 / 1e-20\"),n){let P=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${P} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${d} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let N=\"max\",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(E=\"avgValue / max(count, 1.0)\");let A=Math.floor(i/4)*4,D=i%4,F=`\n if (${w}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${N}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${I};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${I});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${A}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${F}\n }\n\n int xC = xCCorner + ${A};\n if (${D===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${D===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${D===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${F}\n }\n }\n }\n setOutput(${E});\n }\n `}};function Fot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;Ii(o,\"avgPool\");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new vi(c,\"avg\",!1);return e.runWebGLProgram(p,[o],\"float32\")}var l3={kernelName:Qo,backendName:\"webgl\",kernelFunc:Fot};function Oot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new Yu(p,\"avg\",!1);return e.runWebGLProgram(m,[o],\"float32\")}var u3={kernelName:Di,backendName:\"webgl\",kernelFunc:Oot};var mI=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${p});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${u};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${l};\n wC+= ${a}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},fI=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=`\n const ivec3 pads = ivec3(${d}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${u}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${l}) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${f};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function Pot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new fI(m);return e.runWebGLProgram(f,[o],i.dtype)}var c3={kernelName:Xl,backendName:\"webgl\",kernelFunc:Pot};function Mot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;Ii([o,s],\"avgPoolGrad\");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new mI(c);return e.runWebGLProgram(p,[o],i.dtype)}var p3={kernelName:Fp,backendName:\"webgl\",kernelFunc:Mot};function Lot(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return yp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var m3={kernelName:ts,backendName:\"webgl\",kernelFunc:Lot};var dI=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"0.0\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"1.0\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${a};\n float scale = ${u};\n float inv = scale * inversesqrt(variance + float(${i}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var hI=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"vec4(0.0)\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"vec4(1.0)\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n vec4 offset = ${a};\n vec4 scale = ${u};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${i}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var zot=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(i==null||o.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(a==null||o.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=L().getBool(\"WEBGL_PACK_NORMALIZATION\")?new hI(n.shape,o.shape,s.shape,c,p,u):new dI(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},f3={kernelName:gs,backendName:\"webgl\",kernelFunc:zot};var gI=class{constructor(t){this.variableNames=[\"source\"],this.outputShape=t,this.rank=t.length;let e=zt(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let n=Bot(this.rank),o,s=t.map((i,a)=>`sourceLoc.${C1[a]} = start[${a}] + coords.${C1[a]};`);o=`\n ${e} sourceLoc;\n ${e} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${o}\n setOutput(getSource(${n}));\n }\n `}},C1=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function Bot(r){if(r===1)return\"sourceLoc\";if(r<=6)return C1.slice(0,r).map(t=>\"sourceLoc.\"+t).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var xI=class{constructor(t){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let e=zt(this.rank),n=er(\"coords\",this.rank),o=er(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=`\n result.x = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.y = ${i};\n --${o[this.rank-1]};\n }\n `,u=this.rank===1?\"\":`\n --${n[this.rank-1]};\n if (++${n[this.rank-2]} < ${t[this.rank-2]}) {\n ++${o[this.rank-2]};\n result.z = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.w = ${i};\n }\n }\n `,l=this.rank<=4?`sourceLoc = coords +\n ${e}(${t.map((c,p)=>`start[${p}]`).join()});`:t.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`\n`);this.userCode=`\n void main() {\n ${e} coords = getOutputCoords();\n ${e} sourceLoc;\n ${l}\n vec4 result = vec4(0.);\n ${a}\n ${u}\n setOutput(result);\n }\n `}};function Vot(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=ze.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function Si(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=ze.parseSliceParams(o,s,i);if(ze.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype===\"string\"){let p=e.texData.get(o.dataId),m=rz(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=ze.isSliceContinous(o.shape,a,u);if(l||!c){let p=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new xI(u):new gI(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),Vot(o,a,u,e)}var d3={kernelName:Gi,backendName:\"webgl\",kernelFunc:Si};var Got=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=rt({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Si({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},h3={kernelName:$i,backendName:\"webgl\",kernelFunc:Got};function Wot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=Yw(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var g3={kernelName:Da,backendName:\"webgl\",kernelFunc:Wot};function Uot(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],\"int32\",Int32Array.from(a))}var x3={kernelName:Yl,backendName:\"webgl\",kernelFunc:Uot};var Hot=\"return float(a != b);\",v1=ue({opSnippet:Hot,cpuKernelImpl:KL,dtype:\"bool\"}),y3={kernelName:Ja,backendName:\"webgl\",kernelFunc:v1};function Gl(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.real},backend:e})}var b3={kernelName:jp,backendName:\"webgl\",kernelFunc:Gl};var qot=\"return float(int(x));\";function w3(r,t){let e=new zr(r.shape,qot),n=t.runWebGLProgram(e,[r],\"int32\");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function S1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s===\"complex64\"){if(o.dtype===\"complex64\")return rr({inputs:{x:o},backend:e});let i=Te(o.shape),a=S1({inputs:{x:o},backend:e,attrs:{dtype:\"float32\"}}),u=Fn({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype===\"complex64\"){let i=Gl({inputs:{input:o},backend:e}),a=S1({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!y.hasEncodingLoss(o.dtype,s)){let i=rr({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(e.shouldExecuteOnCPU([o])){let i=e.texData.get(o.dataId).values,[a,u,l]=TL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s===\"int32\")return w3(o,e);if(s===\"bool\"){let i=e.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),u=v1({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var I3={kernelName:ho,backendName:\"webgl\",kernelFunc:S1};var C3=\"return ceil(x);\",Kot=wt({opSnippet:C3,packedOpSnippet:C3,cpuKernelImpl:_L}),v3={kernelName:es,backendName:\"webgl\",kernelFunc:Kot};var yI=class{constructor(t){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var bI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;L().getBool(\"WEBGL_PACK_CLIP\")?a=new bI(o.shape):a=new yI(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var S3={kernelName:go,backendName:\"webgl\",kernelFunc:jot};var wI=class{constructor(t){this.variableNames=[\"real\",\"imag\"],this.outputShape=t,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `}};function N3(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function Xot(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new wI(n.shape),i=[N3(n,o.complexTensorInfos.real),N3(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var k3={kernelName:Zl,backendName:\"webgl\",kernelFunc:Xot};var II=class{constructor(t){this.outputShape=[],this.outputShape=S.computeOutShape(t,1),this.variableNames=t.map((i,a)=>`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h= ${u[h-1]}) {\n return getChannel(\n getT${h}(${CI(a,l,g)}),\n vec2(${CI(c,l,g)}));\n }`}let f=u.length,d=u[u.length-1];m+=`\n return getChannel(\n getT${f}(${CI(a,l,d)}),\n vec2(${CI(c,l,d)}));`,this.userCode=`\n float getValue(${a.map(h=>\"int \"+h)}) {\n ${m}\n }\n\n void main() {\n ${s} coords = getOutputCoords();\n vec4 result = vec4(getValue(${i}), 0., 0., 0.);\n\n ${i[o-1]} = ${i[o-1]} + 1;\n if (${i[o-1]} < ${n[o-1]}) {\n result.g = getValue(${i});\n }\n\n ${i[o-2]} = ${i[o-2]} + 1;\n if (${i[o-2]} < ${n[o-2]}) {\n result.a = getValue(${i});\n }\n\n ${i[o-1]} = ${i[o-1]} - 1;\n if (${i[o-2]} < ${n[o-2]} &&\n ${i[o-1]} < ${n[o-1]}) {\n result.b = getValue(${i});\n }\n setOutput(result);\n }\n `}};function CI(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function bp(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var T3={kernelName:Gp,backendName:\"webgl\",kernelFunc:bp};function Ld(r,t,e){let n=r[0].dtype;if(n===\"complex64\"){let f=r.map(b=>Gl({inputs:{input:b},backend:e})),d=r.map(b=>bp({inputs:{input:b},backend:e})),h=Ld(f,t,e),g=Ld(d,t,e),x=Fn({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n===\"string\"&&(o=!0),o){let f=r.map(I=>{let E=[-1,y.sizeFromShape(I.shape.slice(t))];return rt({inputs:{x:I},backend:e,attrs:{shape:E}})}),d=f.map(I=>({vals:e.readSync(I.dataId),shape:I.shape})),h=S.computeOutShape(f.map(I=>I.shape),1),g=f[0].shape[0]===1,x=EL(d,h,n,g),b=S.computeOutShape(r.map(I=>I.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let f=i?new zr(r[0].shape,Ia):new Rn(r[0].shape,Ia);return e.runWebGLProgram(f,r,n)}let a=L().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>a){let f=[];for(let h=0;hd.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=Yot(s,t,e),c=new II(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=rt({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function Yot(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>rt({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function N1(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?rr({inputs:{x:u[0]},backend:e}):Ld(u,s,e)}var _3={kernelName:Ri,backendName:\"webgl\",kernelFunc:N1};var zd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,w=g?3:1,I=\"\",N=\"\";n&&(o?I=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?I=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:I=`\n float activation(float x) {\n ${n}\n }\n `,N=\"result = activation(result);\");let E=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${I}\n\n const ivec2 strides = ivec2(${u}, ${l});\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${w}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${p};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${d}) *\n getW(wR, wC, ${d}, d2);\n } else {\n dotProd +=\n getX(batch, ${d}, xR, xC) *\n getW(wR, wC, ${d}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2),\n getW(wR, wC, ${d} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1),\n getX(batch, xR, xC, ${d} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC),\n getX(batch, ${d} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${E}\n ${N}\n setOutput(result);\n }\n `}},SI=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${i}, ${a});\n const ivec3 pads = ivec3(${e}, ${n}, ${o});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${p}; wF++) {\n int xF = xFCorner + wF * ${u};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${l};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${d}) *\n getW(wF, wR, wC, ${d}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1),\n getX(batch, xF, xR, xC, ${d} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2),\n getW(wF, wR, wC, ${d} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var Bd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(p+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*u};\n `,a===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,u===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,u>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${a}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+p}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+p}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${l}\n\n ${o.output} = result;\n }\n `}};function kI(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function TI({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat===\"channelsLast\",d=!1,h=!1,g,x=[];if(s!=null){let I=kI(s.shape,f);I!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:I}}),x.push(s))}if(o!=null){let I=kI(o.shape,f);I!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:I}}),x.push(o))}if(!((p===1||m===1)&&c>I1)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let I=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,I,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(Ku(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let D=yp({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get(D.dataId);y.assert(F.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=E,F.shape=e.outShape,g=rr({inputs:{x:D},backend:n}),g.shape=e.outShape,x.push(D)}else{let I=e.outHeight*e.outWidth,N=rt({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,I,e.inChannels]:[e.batchSize,e.inChannels,I]}}),E=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=yp({a:f?N:E,b:f?E:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=rt({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(E),x.push(A)}for(let I of x)n.disposeIntermediateTensorInfo(I);return g}function _I({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f===\"channelsLast\",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,I=[];if(s!=null){let Z=kI(s.shape,d);Z!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:Z}}),I.push(s))}if(o!=null){let Z=kI(o.shape,d);Z!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:Z}}),I.push(o))}let N=rt({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});I.push(N);let E=new NI(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],D=n.runWebGLProgram(E,[r],\"float32\",A),F=rt({inputs:{x:D},backend:n,attrs:{shape:x}});I.push(D),I.push(F);let P=o!=null,V=s!=null,G=a===\"leakyrelu\",W=a?Vl(a,!0):null,q=new Md(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,P,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));H.push(Z),I.push(Z)}let K=n.runWebGLProgram(q,H,\"float32\"),X=rt({inputs:{x:K},backend:n,attrs:{shape:e.outShape}});I.push(K);for(let Z of I)n.disposeIntermediateTensorInfo(Z);return X}function Zot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,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=TI({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p===\"channelsLast\"&&L().getBool(\"WEBGL_EXP_CONV\")){let h=new Bd(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],\"float32\",g)}else if(L().getBool(\"WEBGL_CONV_IM2COL\"))f=_I({x:o,filter:s,convInfo:m,backend:e});else{let h=new zd(m);f=e.runWebGLProgram(h,[o,s],\"float32\")}let d=rt({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var E3={kernelName:rs,backendName:\"webgl\",kernelFunc:Zot};var EI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n ${i?`float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);`}\n }\n }\n }\n setOutput(dotProd);\n }\n `}},AI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat===\"channelsLast\",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${p}];\n\n ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n\n if (${i}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},DI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yF = 0; yF < ${t.outDepth}; yF++) {\n int xF = wF + yF * ${e} - ${s};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${n} - ${i};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${a};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},$I=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${u}, ${l}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${e}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${e} - 1 - wF;\n\n for (int wR = 0; wR < ${n}; wR++) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${n} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function Jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new EI(m);return e.runWebGLProgram(f,[o,s],\"float32\")}var A3={kernelName:Pp,backendName:\"webgl\",kernelFunc:Jot};var RI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"strides\",type:\"vec2\"}],this.outputShape=t.inShape,this.enableShapeUniforms=de(this.outputShape.length);let e=t.filterHeight,n=t.filterWidth,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left;this.userCode=`\n const ivec2 pads = ivec2(${o}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n vec4 result = vec4(0.);\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / strides[0];\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n int wCPerm = ${n} - 1 - wC;\n\n float dyC = float(dyCCorner + wC) / strides[1];\n bool idyCVal = (dyC >= 0.0) && (dyC < ${t.outWidth}.0)\n && (fract(dyC) == 0.0);\n int idyC = int(dyC);\n\n float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${t.outWidth}.0)\n && (fract(dyC2) == 0.0);\n int idyC2 = int(dyC2);\n\n if (idyCVal && idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n dySample : getDy(batch, idyR, idyC2, d2);\n\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n\n dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample2.xy : dySample2.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC2, d2);\n vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n }\n }\n }\n setOutput(result);\n }\n `}};function Qot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p);if(L().getBool(\"WEBGL_PACK\")&&p===\"channelsLast\"){let f=[[m.strideHeight,m.strideWidth]],d=new RI(m);return e.runWebGLProgram(d,[o,s],\"float32\",f)}else{let f=new AI(m);return e.runWebGLProgram(f,[o,s],\"float32\")}}var D3={kernelName:ns,backendName:\"webgl\",kernelFunc:Qot};function tst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new SI(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var $3={kernelName:os,backendName:\"webgl\",kernelFunc:tst};function est(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new DI(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var R3={kernelName:$a,backendName:\"webgl\",kernelFunc:est};function rst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new $I(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var F3={kernelName:Ra,backendName:\"webgl\",kernelFunc:rst};var nst=Bo+`\n return cos(x);\n`,ost=`\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${Yn}\n return result;\n`,sst=wt({opSnippet:nst,packedOpSnippet:ost}),O3={kernelName:ss,backendName:\"webgl\",kernelFunc:sst};var ist=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,ast=wt({opSnippet:ist}),P3={kernelName:is,backendName:\"webgl\",kernelFunc:ast};var FI=class{constructor(t,e,n,o,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o===\"bilinear\"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,\"(y2-y1) * height_ratio\",`y1*${d} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${d}`],[w,I,N]=m>1?[`${(u-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${w});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${i}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${I};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${d} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${N};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${f} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var lst=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new FI(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],\"float32\")},M3={kernelName:Oa,backendName:\"webgl\",kernelFunc:lst};var wp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(wp||(wp={}));var hg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,i=this.op===wp.Prod?\"1.0\":\"0.0\",a=n?i:`getX(${L3(s,\"coords\",this.op)})`,u=this.outputShape[this.outputShape.length-1],l=\"\",c=\"\";n?(l=o?`end != ${u-1}`:\"end != 0\",c=o?\"end + 1\":\"end - 1\"):(l=o?`end + pow2 < ${u}`:\"end >= pow2\",c=o?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${zt(s)} coords = getOutputCoords();\n int end = ${z3(s,\"coords\",this.op)};\n float val = ${a};\n int pow2 = int(pow(2.0, index));\n if (${l}) {\n int idx = ${c};\n ${z3(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${L3(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function L3(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function z3(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function OI(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=rr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new hg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new hg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function ust(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return OI(wp.Prod,o,e,s,i,a)}var B3={kernelName:Fa,backendName:\"webgl\",kernelFunc:ust};function cst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return OI(wp.Sum,o,e,s,i,a)}var V3={kernelName:as,backendName:\"webgl\",kernelFunc:cst};function pst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Yw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=kL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var G3={kernelName:Jl,backendName:\"webgl\",kernelFunc:pst};var PI=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${e};\n int offset_h = imod(h, ${e});\n int in_w = w / ${e};\n int offset_w = imod(w, ${e});\n int offset_d = (offset_h * ${e} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}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 mst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i===\"NHWC\"?o.shape[1]:o.shape[2],l=i===\"NHWC\"?o.shape[2]:o.shape[3],c=i===\"NHWC\"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i===\"NHWC\"?[a,p,m,f]:[a,f,p,m],h=new PI(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var W3={kernelName:Pa,backendName:\"webgl\",kernelFunc:mst};var Vd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l=\"\",c=\"\";n&&(o?l=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?l=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:l=`\n float activation(float x) {\n ${n}\n }\n `,c=\"result = activation(result);\");let p=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${l}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${u};\n int q = d2 - d1 * ${u};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${i}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${a}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${p}\n ${c}\n setOutput(result);\n }\n `}};var Gd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(f+=`\n xC = xCCorner + ${b*l};\n `,u===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,l===1&&b>0?f+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:f+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):f+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,l>1?f+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:f+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):w===1?f+=`\n xC${b+1} = xTexelC${b};\n `:f+=`\n xCOffset = xC + ${w};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(f+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;L().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new Gd(p):m=new Vd(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],\"float32\",f)}var U3={kernelName:ls,backendName:\"webgl\",kernelFunc:fst};var MI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${i} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},LI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${u}; dm++) {\n int d2 = d1 * ${u} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function dst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new MI(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var H3={kernelName:Mp,backendName:\"webgl\",kernelFunc:dst};function hst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new LI(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var q3={kernelName:Lp,backendName:\"webgl\",kernelFunc:hst};var zI=class{constructor(t){this.variableNames=[\"X\"],this.outputShape=[t,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function gst(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=rt({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new zI(s),u=e.runWebGLProgram(a,[i],i.dtype),l=rt({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var K3={kernelName:Ql,backendName:\"webgl\",kernelFunc:gst};var BI=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${i});\n const ivec2 pads = ivec2(${p}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${a}; h++) {\n int hIn = hBeg + h * ${l};\n\n if (hIn >= 0 && hIn < ${e}) {\n for (int w = 0; w < ${u}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${n}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function xst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,\"NHWC\",u),c,p=new BI(l);c=e.runWebGLProgram(p,[o,s],\"float32\");let m=rt({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var j3={kernelName:us,backendName:\"webgl\",kernelFunc:xst};function yst(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h=0&&(m=xp({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var X3={kernelName:zp,backendName:\"webgl\",kernelFunc:yst};var bst=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",wst=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,Ist=wt({opSnippet:bst,packedOpSnippet:wst}),Y3={kernelName:ps,backendName:\"webgl\",kernelFunc:Ist};var Cst=\"return (b >= 0.0) ? a : a * (b + 1.0);\",vst=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,Sst=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new zo(vst,n.shape,o.shape):new mo(Cst,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},Z3={kernelName:Ma,backendName:\"webgl\",kernelFunc:Sst};var Nst=`\n return vec4(equal(a, b));\n`,kst=\"return float(a == b);\",Tst=ue({opSnippet:kst,packedOpSnippet:Nst,dtype:\"bool\",cpuKernelImpl:AL}),J3={kernelName:za,backendName:\"webgl\",kernelFunc:Tst};var _st=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${S.ERF_P};\n float a1 = ${S.ERF_A1};\n float a2 = ${S.ERF_A2};\n float a3 = ${S.ERF_A3};\n float a4 = ${S.ERF_A4};\n float a5 = ${S.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,Est=wt({opSnippet:_st}),Q3={kernelName:La,backendName:\"webgl\",kernelFunc:Est};var Ast=Bo+`\n return exp(x);\n`,Dst=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,k1=wt({opSnippet:Ast,packedOpSnippet:Dst,cpuKernelImpl:DL,dtype:\"float32\"}),tB={kernelName:ms,backendName:\"webgl\",kernelFunc:k1};function VI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var eB={kernelName:Fi,backendName:\"webgl\",kernelFunc:VI};var rB=\"return exp(x) - 1.0;\",$st=wt({opSnippet:rB,packedOpSnippet:rB,cpuKernelImpl:$L}),nB={kernelName:fs,backendName:\"webgl\",kernelFunc:$st};var gg=class{constructor(t,e,n){this.variableNames=[\"real\",\"imag\"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:\"1.0\",a;if(t===\"real\")a=\"return real * expR - imag * expI;\";else if(t===\"imag\")a=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${t}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${a}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${o});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${o}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${i};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function GI(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=rt({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new gg(\"real\",u,t),c=new gg(\"imag\",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,\"float32\"),f=e.runWebGLProgram(c,p,\"float32\"),d=Fn({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=rt({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function Rst(r){let{inputs:t,backend:e}=r,{input:n}=t;return GI(n,!1,e)}var oB={kernelName:Bp,backendName:\"webgl\",kernelFunc:Rst};var WI=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=t,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Wl(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s===\"string\"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new WI(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var sB={kernelName:ru,backendName:\"webgl\",kernelFunc:Wl};var UI=class{constructor(t){this.variableNames=[\"Image\"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${e} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${e}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var iB={kernelName:Ba,backendName:\"webgl\",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new UI(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var aB=\"return floor(x);\",Fst=wt({opSnippet:aB,packedOpSnippet:aB,cpuKernelImpl:RL}),lB={kernelName:ds,backendName:\"webgl\",kernelFunc:Fst};var Ost=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,Pst=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,Mst=ue({opSnippet:Ost,packedOpSnippet:Pst,dtype:\"int32\"}),uB={kernelName:hs,backendName:\"webgl\",kernelFunc:Mst};var HI=class{constructor(t){this.variableNames=[\"A\"];let e=We(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);\n\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var qI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let e=We(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${o}.0, ${n}.0);\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${e.output} = result;\n }\n `}};var cB={kernelName:oh,backendName:\"webgl\",kernelFunc:Lst},Wd,T1=L().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function Lst(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!=\"undefined\"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!=\"undefined\"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];if(a||i){let h=L().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Wd==null||h!==T1)&&(T1=h,Wd=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:T1})),Wd.canvas.width=u,Wd.canvas.height=l,Wd.drawImage(o,0,0,u,l),o=Wd.canvas}let m=e.makeTensorInfo(c,\"int32\");e.texData.get(m.dataId).usage=Zr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=L().getBool(\"WEBGL_PACK\")?new qI(p):new HI(p),d=e.runWebGLProgram(f,[m],\"int32\");return e.disposeData(m.dataId),d}function zst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),x,b=[],w=i!=null,I=a!=null,N=f===\"leakyrelu\",E=()=>{let D=[o,s],F=(P,V)=>{if(V===\"NCHW\"&&P.shape.length===1&&P.shape[0]!==1){let G=rt({inputs:{x:P},backend:e,attrs:{shape:[P.shape[0],1,1]}});return b.push(G),G}return P};if(w&&D.push(F(i,c)),I&&D.push(F(a,c)),N){let P=e.makeTensorInfo([],\"float32\",y.createScalarValue(d,\"float32\"));D.push(P),b.push(P)}return D};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=TI({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(g.strideWidth<=2&&h===\"channelsLast\"&&L().getBool(\"WEBGL_EXP_CONV\")){let D=f?Vl(f,!0):null,F=new Bd(g,w,D,I,N),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],V=E();x=e.runWebGLProgram(F,V,\"float32\",P)}else if(L().getBool(\"WEBGL_CONV_IM2COL\"))x=_I({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let D=f?Vl(f,!1):null,F=new zd(g,w,D,I,N),P=E();x=e.runWebGLProgram(F,P,\"float32\")}let A=rt({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach(D=>e.disposeIntermediateTensorInfo(D)),A}var pB={kernelName:ji,backendName:\"webgl\",kernelFunc:zst};function Bst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),x=L().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Vl(m,x):null,w=[o,s],I=i!=null,N=a!=null,E=m===\"leakyrelu\";if(I&&w.push(i),N&&w.push(a),E){let P=e.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));w.push(P),d.push(P)}let A;x?A=new Gd(g,I,b,N,E):A=new Vd(g,I,b,N,E);let D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=e.runWebGLProgram(A,w,\"float32\",D);return d.forEach(P=>e.disposeIntermediateTensorInfo(P)),F}var mB={kernelName:Xi,backendName:\"webgl\",kernelFunc:Bst};var KI=class{constructor(t,e,n,o){this.sliceDim=t,this.strides=e,this.paramsShape=o,this.variableNames=[\"x\",\"indices\"],this.outputShape=n;let s=zt(n.length),i=`\n int index;`;for(let a=0;a= ${this.paramsShape[a]};\n flattenIndex += index * ${this.strides[a]};`;this.userCode=`\n void main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${i}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function Vst(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=o.shape,i=s[s.length-1],a=y.sizeFromShape(n.shape),[u,l,c,p]=S.prepareAndValidate(n,o),m=rt({inputs:{x:o},backend:e,attrs:{shape:[l,i]}}),f=rt({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/c,c]}});if(e.shouldExecuteOnCPU([n,o])||n.dtype===\"string\"){let x=e.readSync(o.dataId),b=e.bufferSync(n),w=FL(x,b,n.dtype,l,i,c,p,n.shape,a);return e.makeTensorInfo(u,n.dtype,w.values)}let d=new KI(i,p,[l,c],n.shape),h=e.runWebGLProgram(d,[f,m],f.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:u}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var fB={kernelName:Va,backendName:\"webgl\",kernelFunc:Vst};var jI=class{constructor(t,e){this.variableNames=[\"A\",\"indices\"],this.outputShape=e,this.rank=e.length;let n=zt(this.rank),o=Gst(t,2);this.userCode=`\n void main() {\n ${n} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${o}));\n }\n `}};function Gst(r,t){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],n=[];for(let o=0;o=0,()=>`GatherV2: the index value ${N} is not in [0, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=rt({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=rt({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype===\"string\"){let b=e.bufferSync(f),w=e.bufferSync(m),I=OL(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,I.dtype,I.values)}let h=new jI(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=rt({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var dB={kernelName:Oi,backendName:\"webgl\",kernelFunc:_1};var Wst=\"return float(a > b);\",Ust=`\n return vec4(greaterThan(a, b));\n`,Hst=ue({opSnippet:Wst,packedOpSnippet:Ust,cpuKernelImpl:PL,dtype:\"bool\"}),hB={kernelName:Ga,backendName:\"webgl\",kernelFunc:Hst};var qst=\"return float(a >= b);\",Kst=`\n return vec4(greaterThanEqual(a, b));\n`,jst=ue({opSnippet:qst,packedOpSnippet:Kst,dtype:\"bool\",cpuKernelImpl:ML}),gB={kernelName:xs,backendName:\"webgl\",kernelFunc:jst};function Xst(r){let{inputs:t,backend:e}=r,{input:n}=t;return GI(n,!0,e)}var xB={kernelName:Vp,backendName:\"webgl\",kernelFunc:Xst};var Yst=\"return float(!isnan(x) && !isinf(x));\",Zst=wt({opSnippet:Yst,dtype:\"bool\"}),yB={kernelName:ys,backendName:\"webgl\",kernelFunc:Zst};var Jst=\"return float(isinf(x));\",Qst=wt({opSnippet:Jst,dtype:\"bool\"}),bB={kernelName:bs,backendName:\"webgl\",kernelFunc:Qst};var tit=\"return float(isnan(x));\",eit=wt({opSnippet:tit,dtype:\"bool\"}),wB={kernelName:ws,backendName:\"webgl\",kernelFunc:eit};var rit=\"return float(a < b);\",nit=`\n return vec4(lessThan(a, b));\n`,oit=ue({opSnippet:rit,packedOpSnippet:nit,cpuKernelImpl:LL,dtype:\"bool\"}),IB={kernelName:Wa,backendName:\"webgl\",kernelFunc:oit};var sit=\"return float(a <= b);\",iit=`\n return vec4(lessThanEqual(a, b));\n`,ait=ue({opSnippet:sit,packedOpSnippet:iit,cpuKernelImpl:zL,dtype:\"bool\"}),CB={kernelName:Ua,backendName:\"webgl\",kernelFunc:ait};function lit(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=BL(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var vB={kernelName:Ha,backendName:\"webgl\",kernelFunc:lit};var uit=Bo+`\n return x < 0.0 ? 0./0. : log(x);\n`,cit=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,pit=wt({opSnippet:uit,packedOpSnippet:cit,cpuKernelImpl:VL}),SB={kernelName:Cs,backendName:\"webgl\",kernelFunc:pit};var mit=Bo+`\n return log(1.0 + x);\n`,fit=wt({opSnippet:mit}),NB={kernelName:vs,backendName:\"webgl\",kernelFunc:fit};var dit=\"return float(a >= 1.0 && b >= 1.0);\",hit=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,git=ue({opSnippet:dit,packedOpSnippet:hit,dtype:\"bool\"}),kB={kernelName:qa,backendName:\"webgl\",kernelFunc:git};var xit=\"return float(!(x >= 1.0));\",yit=wt({opSnippet:xit}),TB={kernelName:Ka,backendName:\"webgl\",kernelFunc:yit};var bit=\"return float(a >= 1.0 || b >= 1.0);\",wit=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,Iit=ue({opSnippet:bit,packedOpSnippet:wit,dtype:\"bool\"}),_B={kernelName:ja,backendName:\"webgl\",kernelFunc:Iit};var XI=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${i}; j <= ${i}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${a}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${u};\n setOutput(val);\n }\n `}};var YI=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${i};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${i}; j <= ${i}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${u};\n setOutput(result);\n }\n `}};var Cit=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=L().getBool(\"WEBGL_PACK_NORMALIZATION\")?new YI(o.shape,s,i,a,u):new XI(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},EB={kernelName:Ss,backendName:\"webgl\",kernelFunc:Cit};var ZI=class{constructor(t,e,n,o,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${e})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${e} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${o}) * norm + float(${n});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${o})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var vit=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new ZI(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},AB={kernelName:Xa,backendName:\"webgl\",kernelFunc:vit};function DB(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Zn(a,r.dtype,\"max\",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function E1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,I=new Array(a);for(let A=0;A`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new vi(c,\"max\",!1);return e.runWebGLProgram(p,[o],o.dtype)}var FB={kernelName:Ts,backendName:\"webgl\",kernelFunc:Tit};function _it(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new Yu(p,\"max\",!1);return e.runWebGLProgram(m,[o],o.dtype)}var OB={kernelName:Pi,backendName:\"webgl\",kernelFunc:_it};var JI=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${o}) {\n float dyR = float(dyRCorner + wR) / ${e}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${i}; wC++) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${i} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},QI=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${m}, ${f});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${u};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${e}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${l};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${a}) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${d} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${l} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function Eit(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new Yu(m,\"max\",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new QI(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var PB={kernelName:nu,backendName:\"webgl\",kernelFunc:Eit};function Ait(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;Ii([s,i],\"maxPoolGrad\");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new vi(m,\"max\",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new JI(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var MB={kernelName:Wp,backendName:\"webgl\",kernelFunc:Ait};function LB(r,t,e,n){let o=new vi(e,\"max\",!1),s=n.runWebGLProgram(o,[r],\"float32\");o=new vi(e,\"max\",!0,!0,t);let i=n.runWebGLProgram(o,[r],\"float32\");return[s,i]}var zB={kernelName:Up,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. 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sC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=zt(o),i=e.map(h=>h[0]).join(\",\"),a=e.map((h,g)=>h[0]+t[g]).join(\",\"),u=er(\"rc\",o),l=er(\"source\",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?\"source\":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1;\n if(${c}) {\n `,o===1?\"\":`}\n rc = outputLoc;\n ${u[o-2]} += 1;\n if(${u[o-2]} < ${this.outputShape[o-2]}) {`,o===1?\"\":` ${u[o-1]} += 1;\n if(${c}) {`],f=o===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",d=\"\";for(let h=0,g=o===1?2:4;h{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Wl({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new sC(o.shape,s,i):new oC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},iV={kernelName:Fs,backendName:\"webgl\",kernelFunc:R1};var Qit=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,tat=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = 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d=S.expandShapeToKeepDim(f.shape,l);f=rt({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var lV={kernelName:Ms,backendName:\"webgl\",kernelFunc:rat};function nat(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=XL(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],\"int32\",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var uV={kernelName:Hp,backendName:\"webgl\",kernelFunc:nat};function oat(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=YL(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],\"int32\",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var cV={kernelName:qp,backendName:\"webgl\",kernelFunc:oat};function sat(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=ZL(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var pV={kernelName:Kp,backendName:\"webgl\",kernelFunc:sat};var F1=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=JL(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},mV={kernelName:ou,backendName:\"webgl\",kernelFunc:F1};var iat=\"return 1.0 / x;\",aat=wt({opSnippet:iat}),fV={kernelName:Ls,backendName:\"webgl\",kernelFunc:aat};var lat=yr+`\n return (x < 0.0) ? 0.0 : x;\n`,uat=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,cat=wt({opSnippet:lat,packedOpSnippet:uat}),dV={kernelName:zs,backendName:\"webgl\",kernelFunc:cat};var pat=yr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,mat=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,fat=wt({opSnippet:pat,packedOpSnippet:mat}),hV={kernelName:Gs,backendName:\"webgl\",kernelFunc:fat};var iC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var aC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function dat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new aC(o.shape,u,l,s,i):new iC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],\"float32\")}var gV={kernelName:Vs,backendName:\"webgl\",kernelFunc:dat};var lC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function hat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new lC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var xV={kernelName:nl,backendName:\"webgl\",kernelFunc:hat};var uC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":f=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var cC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":f=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function gat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new cC(o.shape,u,l,s,i):new uC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var yV={kernelName:Bs,backendName:\"webgl\",kernelFunc:gat};var pC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float sourceFracRow =\n float(${u[0]}) *\n (float(dyR) / float(${l[0]}));\n\n float sourceFracCol =\n float(${u[1]}) *\n (float(dyC) / float(${l[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${o}) - 1),\n ${n} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${n} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function xat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new pC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var bV={kernelName:rl,backendName:\"webgl\",kernelFunc:xat};var mC=class{constructor(t,e){this.variableNames=[\"x\"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${t[0]} - coord - 1));\n }\n `;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(\",\"),i=zt(n);this.userCode=`\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var fC=class{constructor(t,e){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=er(\"rc\",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=zt(n);n===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${t[0]} - rc - 1),\n ${t[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${t[0]} - (rc + 1) - 1),\n ${t[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${a} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${u(o.slice())};\n if(${s}){\n result.g = ${l(o.slice())};\n }\n if(${i}) {\n result.b = ${c(o.slice())};\n if(${s}) {\n result.a = ${p(o.slice())};\n }\n }\n setOutput(result);\n }\n `;function u(d){return m(d)}function l(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",m(d)}function c(d){return d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function p(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function yat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return rr({inputs:{x:o},backend:e});let u=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new fC(o.shape,a):new mC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var wV={kernelName:Ws,backendName:\"webgl\",kernelFunc:yat};var dC=class{constructor(t,e){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let n=t[1],o=t[2];this.outputShape=t;let s=\"\";typeof e==\"number\"?s=`float outputValue = ${e.toFixed(2)};`:s=`\n vec3 fill = vec3(${e.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var IV={kernelName:fl,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new dC(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var bat=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n 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setOutput(mix(${g}, sum, float(found)));\n }\n `}};var hC=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=i;let l=zt(s.length),c=zt(i.length),p=\"\";n===1?p=\"i\":n===2&&(p=\"i, j\");let m=`getIndices(${p})`,f=\"\";o===1?f=\"i\":o===2&&(f=\"i, coords[1]\");let d=`getUpdates(${f})`,h=\"\";u&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=e>1?\"strides[j]\":\"strides\",b=e>1?\"strides[j + 1]\":\"strides\";this.userCode=`\n ${l} strides = ${l}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n vec4 sum = vec4(0.);\n vec4 found = vec4(0.);\n for (int i = 0; i < ${t}; i+=2) {\n ivec2 flattenedIndex = ivec2(0);\n for (int j = 0; j < ${e}; j+=2) {\n ivec4 index = round(${m});\n flattenedIndex += index.xz * ${x};\n if (j + 1 < ${e}) {\n flattenedIndex += index.yw * ${b};\n }\n }\n if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n vec4 updVals = ${d};\n if (flattenedIndex[0] == coords[0]) {\n sum.xy += updVals.xy;\n found.xy = vec2(1.);\n } else if (flattenedIndex[0] == coords[0] + 1) {\n sum.zw += updVals.xy;\n found.zw = vec2(1.);\n }\n if (flattenedIndex[1] == coords[0]) {\n sum.xy += updVals.zw;\n found.xy = vec2(1.);\n } else if (flattenedIndex[1] == coords[0] + 1) {\n sum.zw += updVals.zw;\n found.zw = vec2(1.);\n }\n }\n }\n setOutput(mix(${g}, sum, found));\n }\n `}};function vat(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=rt({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],\"float32\",new Float32Array([0])),g;L().getBool(\"WEBGL_PACK\")?g=new hC(u,a,f.shape.length,d.shape.length,c,m):g=new Zu(u,a,f.shape.length,d.shape.length,c,m);let x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var SV={kernelName:ol,backendName:\"webgl\",kernelFunc:vat};var gC=class{constructor(t,e,n,o){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[t,n];let s=\"while (left < right) {\",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,a=L().getNumber(\"WEBGL_VERSION\")===2?s:i,u=o===\"left\"?\"<\":\"<=\";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${a}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${u} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return 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e.runWebGLProgram(i,[n,o,s],ur(o.dtype,s.dtype))}var kV={kernelName:Vi,backendName:\"webgl\",kernelFunc:Nat};var kat=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${S.SELU_SCALEALPHA};\n float scale = ${S.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,Tat=wt({opSnippet:kat}),TV={kernelName:qs,backendName:\"webgl\",kernelFunc:Tat};var _at=Bo+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,Eat=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Aat=wt({opSnippet:_at,packedOpSnippet:Eat,cpuKernelImpl:ez}),_V={kernelName:Xs,backendName:\"webgl\",kernelFunc:Aat};var Dat=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,$at=wt({opSnippet:Dat}),EV={kernelName:js,backendName:\"webgl\",kernelFunc:$at};var Rat=Bo+`\n return sin(x);\n`,Fat=`\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${Yn}\n return result;\n`,Oat=wt({opSnippet:Rat,packedOpSnippet:Fat}),AV={kernelName:Ks,backendName:\"webgl\",kernelFunc:Oat};var Pat=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Mat=wt({opSnippet:Pat}),DV={kernelName:al,backendName:\"webgl\",kernelFunc:Mat};var Lat=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`,zat=wt({opSnippet:Lat}),$V={kernelName:Ys,backendName:\"webgl\",kernelFunc:zat};var Bat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;y.assert(o.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let a=s.reduce((x,b)=>x*b),u=[[0,0]];u.push(...i);for(let x=1+s.length;xe.disposeIntermediateTensorInfo(x)),g},RV={kernelName:Wi,backendName:\"webgl\",kernelFunc:Bat};function Vat(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n ${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:\n ${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n ${i.shape}`);let a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=nz(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],\"bool\",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var FV={kernelName:su,backendName:\"webgl\",kernelFunc:Vat};function Gat(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=oz(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var OV={kernelName:ll,backendName:\"webgl\",kernelFunc:Gat};function Wat(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=Jw(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var PV={kernelName:iu,backendName:\"webgl\",kernelFunc:Wat};function Uat(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at 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s=e.readSync(o.dataId),i=S.fromUint8ToStringArray(s),a=iz(i,\"string\",n);return e.makeTensorInfo(o.shape,\"string\",a)}var HV={kernelName:sc,backendName:\"webgl\",kernelFunc:Zat};function Jat({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=yr+`\n return x > 0.0 ? 1.0 : float(${t.alpha});\n `,s=new zr(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var qV={kernelName:yo,backendName:\"webgl\",kernelFunc:Jat};var yC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=n;let o=n.length,s=zt(n.length),i=zt(n.length),a=\"\";if(o===1)a=\"coords * strides + begin\";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${t});\n ${s} strides = ${s}(${e});\n\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${a}));\n }\n `}};function 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f=rt({inputs:{x:s},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:i},backend:e,attrs:{shape:[u,l]}}),h=rt({inputs:{x:o},backend:e,attrs:{shape:m}}),g=new Zu(u,a,f.shape.length,d.shape.length,c,m,!1,!0),x=e.runWebGLProgram(g,[d,f,h],h.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:o.shape}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(x),b}var QV={kernelName:sl,backendName:\"webgl\",kernelFunc:alt};var bC=class{constructor(t,e){this.variableNames=[\"A\"];let n=new Array(t.length);for(let i=0;i5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],n=[];for(let o=0;o5){let u=e.readSync(o.dataId),l=o.dtype===\"string\"?u.map(m=>y.decodeString(m)):u,c=bt(o.shape,o.dtype,l),p=mz(c,s);return e.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new bC(o.shape,s);return e.runWebGLProgram(i,[o],o.dtype)}var tG={kernelName:so,backendName:\"webgl\",kernelFunc:O1};var wC=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n `}},IC=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function Ip(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function eG(r){let t=1;for(;tu){let P=e.readSync(o.dataId),[V,G]=fz(P,l,o.dtype,s,i);return[e.makeTensorInfo(V.shape,V.dtype,V.values),e.makeTensorInfo(G.shape,G.dtype,G.values)]}if(s===0)return l[l.length-1]=0,[e.makeTensorInfo(l,o.dtype,[]),e.makeTensorInfo(l,\"int32\",[])];if(c===1)return[o,Wl({attrs:{shape:l,dtype:\"int32\",value:0},backend:e})];let p=e.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?e.unpackTensor(o):o,h=y.sizeFromShape(l)/c,g=rt({inputs:{x:f},attrs:{shape:[h,c]},backend:e});m&&Ip(e,f);let x=eG(s),b=eG(c),w=null,I=()=>w===null?[g,g]:[g,w],N=(P,V,G)=>{let W=I(),q=new wC(G),K=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[P],[V]],X=w;w=e.runWebGLProgram(q,W,\"int32\",K),Ip(e,X)};for(let P=1;P=1;G/=2)N(V,G,[h,b])}for(let P=b;P>x;P/=2){let V=I(),G=new IC([h,P/2]),q=[[c],[w===null?1:0],[x]],H=w;w=e.runWebGLProgram(G,V,\"int32\",q),Ip(e,H);let K=x/2,X=K*2;for(let Z=K;Z>=1;Z/=2)N(X,Z,w.shape)}let E=w;w=Si({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),Ip(e,E);let A=_1({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});Ip(e,g);let D=l.slice(0,-1);D.push(s),E=w,w=rt({inputs:{x:w},attrs:{shape:D},backend:e}),Ip(e,E);let F=A;return A=rt({inputs:{x:A},attrs:{shape:D},backend:e}),Ip(e,F),[A,w]}var rG={kernelName:pl,backendName:\"webgl\",kernelFunc:ult};var CC=class{constructor(t,e,n,o,s,i){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=i;let a=n===\"nearest\"?1:2,u;switch(o){case\"constant\":u=1;break;case\"reflect\":u=2;break;case\"wrap\":u=3;break;case\"nearest\":u=4;break;default:u=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${u} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${t} && 0 <= coordX && coordX < ${e}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = getTransforms(batch, 2);\n float b1 = getTransforms(batch, 3);\n float b2 = getTransforms(batch, 4);\n float b3 = getTransforms(batch, 5);\n float c1 = getTransforms(batch, 6);\n float c2 = getTransforms(batch, 7);\n float projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = float(${s});\n } else {\n float inX = (a1 * xf + a2 * yf + a3) / projection;\n float inY = (b1 * xf + b2 * yf + b3) / projection;\n float mapX = mapCoord(inX, float(${e}));\n float mapY = mapCoord(inY, float(${t}));\n\n if (${a} == 1) {\n int coordY = int(round(mapY));\n int coordX = int(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n float yFloor = floor(mapY);\n float xFloor = floor(mapX);\n float yCeil = yFloor + 1.0;\n float xCeil = xFloor + 1.0;\n float valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n float valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function clt(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new CC(p,m,i,a,u,g);return e.runWebGLProgram(x,[o,s],\"float32\")}var nG={kernelName:ml,backendName:\"webgl\",kernelFunc:clt};function plt(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;Ii(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let i=n.readSync(s.dataId),{outputValues:a,outputShape:u,indices:l}=dz(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],\"int32\",l)]}var oG={kernelName:mu,backendName:\"webgl\",kernelFunc:plt};function mlt(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o,a=i.shape.length,u=o.shape[s],l=new Array(a-1),c=0;for(let h=0;he.disposeIntermediateTensorInfo(h)),d}var sG={kernelName:Hi,backendName:\"webgl\",kernelFunc:mlt};var vC=class{constructor(t,e){this.variableNames=[\"x\",\"segmentIds\"];let n=t.windowSize,o=t.batchSize,s=t.inSize,i=t.numSegments,a=i*Math.ceil(s/n);this.outputShape=[o,a];let u=\"0.0\",l=\"sumValue\",c=Math.floor(n/4)*4,p=n%4,m=`\n sumValue += dot(values, segFilter);\n `,f=\"\";s%n>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let d=\"\";s%n>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${u};\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${d}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${i})) * float(${n}));\n int currentSeg = int(mod(float(outIdx), float(${i})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${p===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function flt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n,a=o.shape.length,u=[],l=0,c=S.getAxesPermutation([l],a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,a)[0]);let m=S.segment_util.computeOutShape(p.shape,l,i),f=y.sizeFromShape([p.shape[l]]),d=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,f]}});u.push(d);let h=mc(o.dtype),g=(I,N,E,A,D)=>{let F=I.shape[0],P=I.shape[1],V=S.segment_util.segOpComputeOptimalWindowSize(P,D),G={windowSize:V,inSize:P,batchSize:F,numSegments:D},W=new vC(G,N),q=e.compileAndRun(W,[I,E],A);if(u.push(q),q.shape[1]===D)return q;let H=F1({backend:e,attrs:{start:0,stop:D,step:1,dtype:\"float32\"}}),K=O1({inputs:{x:H},backend:e,attrs:{reps:[P/V]}});return u.push(H),u.push(K),g(q,N,K,A,D)},x=g(d,\"unsortedSegmentSum\",s,h,i),b=rt({inputs:{x},backend:e,attrs:{shape:m}}),w=b;if(c!=null){u.push(b);let I=S.getUndoAxesPermutation(c);w=Pe({inputs:{x:w},backend:e,attrs:{perm:I}})}return u.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}var iG={kernelName:fu,backendName:\"webgl\",kernelFunc:flt};var dlt=[Wz,Hz,qz,Kz,Xz,Yz,Zz,Jz,e3,r3,n3,o3,s3,i3,a3,l3,u3,c3,p3,m3,f3,h3,g3,x3,I3,v3,S3,Rz,k3,_3,E3,A3,D3,$3,R3,F3,O3,P3,M3,B3,V3,G3,W3,U3,H3,q3,K3,j3,X3,Y3,Z3,J3,Q3,tB,eB,nB,oB,sB,iB,lB,uB,cB,pB,mB,fB,dB,hB,gB,$z,xB,T3,yB,bB,wB,Fz,IB,CB,vB,SB,NB,kB,TB,_B,EB,AB,$B,RB,FB,OB,PB,MB,zB,VB,GB,WB,UB,HB,YB,Mz,ZB,JB,QB,tV,y3,eV,oV,sV,iV,aV,Oz,lV,uV,cV,pV,mV,b3,qB,fV,dV,hV,zz,gV,xV,yV,bV,wV,IV,CV,vV,SV,NV,kV,TV,_V,EV,AV,DV,d3,XB,$V,RV,FV,OV,PV,MV,LV,zV,VV,GV,UV,HV,qV,KV,jV,XV,YV,jB,Vz,ZV,JV,QV,tG,rG,nG,Gz,oG,sG,iG,rV];for(let r of dlt)ic(r);var kt;(function(r){r[r.float32=0]=\"float32\",r[r.int32=1]=\"int32\",r[r.bool=2]=\"bool\",r[r.string=3]=\"string\",r[r.complex64=4]=\"complex64\"})(kt||(kt={}));var Ju;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(Ju||(Ju={}));var aG;function hlt(r){aG=r.wasm.cwrap(Ki,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function glt(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t;if(o.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=0;if(i!=null){let D=e.dataIdMap.get(i.dataId);if(D.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${D.shape.length}.`);d=D.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=Ju[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=u?o.shape[2]:o.shape[1],b=l?s.shape[1]:s.shape[2],w=Ur.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)),I=e.makeOutput([...w,x,b],o.dtype),N=e.dataIdMap.get(I.dataId).id,E=new Uint8Array(new Int32Array(o.shape).buffer),A=new Uint8Array(new Int32Array(s.shape).buffer);return aG(m,E,o.shape.length,f,A,s.shape.length,u,l,g,d,h,p||0,N),I}var lG={kernelName:Ki,backendName:\"wasm\",setupFunc:hlt,kernelFunc:glt};function Ct(r,t){let e;function n(s){e=s.wasm.cwrap(r,null,[\"number\",\"number\",\"number\"])}function o(s){let{backend:i,inputs:{x:a}}=s,u=i.dataIdMap.get(a.dataId).id,l=i.makeOutput(a.shape,t||a.dtype),c=i.dataIdMap.get(l.dataId).id;return y.sizeFromShape(l.shape)===0||e(u,kt[a.dtype],c),l}return{kernelName:r,backendName:\"wasm\",setupFunc:n,kernelFunc:o}}var uG=Ct(_i);var cG=Ct(Ho);var pG=Ct(qo);function ae(r,t,e){let n;function o(i){n=i.wasm.cwrap(r,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function s(i){let{backend:a,inputs:u}=i,{a:l,b:c}=u,p=a.dataIdMap.get(l.dataId).id,m=a.dataIdMap.get(c.dataId).id,f=e!=null?e:l.dtype,d=S.assertAndGetBroadcastShape(l.shape,c.shape),h=a.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(l.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=a.dataIdMap.get(h.dataId).id;return(()=>n(p,g,l.shape.length,m,x,c.shape.length,kt[l.dtype],b))(),h}return{kernelName:r,backendName:\"wasm\",setupFunc:o,kernelFunc:s}}var xlt=!0,mG=ae(oo,xlt);var fG;function ylt(r){fG=r.wasm.cwrap(Ko,null,[\"array\",\"number\",\"number\",\"number\"])}function blt(r){let{inputs:t,backend:e}=r,n=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(n.shape)===0)return n;let o=t.map(a=>e.dataIdMap.get(a.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),i=e.dataIdMap.get(n.dataId).id;return fG(s,o.length,kt[n.dtype],i),n}var dG={kernelName:Ko,backendName:\"wasm\",setupFunc:ylt,kernelFunc:blt};function Cp(r){let{inputs:{x:t},backend:e}=r;if(t.dtype===\"string\")return sr(e.readSync(t.dataId),t.shape,t.dtype);let n=e.makeOutput(t.shape,t.dtype),o=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(n).set(o),n}var hG={kernelName:xo,backendName:\"wasm\",kernelFunc:Cp};var gG;function wlt(r){gG=r.wasm.cwrap(io,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function fo(r){let{inputs:t,backend:e,attrs:n}=r,[o,s]=Clt(t.x.shape,n.perm),i=!0;for(let d=0;d=o&&(s===-1||n[s]>n[i])&&(s=i);n[s]=o}return[e,n]}var xG={kernelName:io,backendName:\"wasm\",kernelFunc:fo,setupFunc:wlt};function vn(r,t,e){let n=r.shape,o=r.shape.length,s=y.parseAxisParam(t,n),i=s,a=S.getAxesPermutation(i,o),u=null,l=!1;if(a!=null){let c=new Array(o);for(let f=0;f`new shape: ${i}, old shape: ${n.shape}. 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t.dtype===\"string\"?p.stringBytes=u.slice(d,d+y.sizeFromShape(i)):o.typedArrayFromHeap(l).set(u.subarray(d,d+y.sizeFromShape(i))),l}if(t.dtype===\"string\"){let d=sp(u,s,i,t.shape,t.dtype);return p.stringBytes=d,l}let m=o.typedArrayFromHeap(l),f=t.shape.length;if(f===2)Olt(u,c[0],m,s,i);else if(f===3)Plt(u,c[0],c[1],m,s,i);else if(f===4)Mlt(u,c[0],c[1],c[2],m,s,i);else{let d=sp(u,s,i,t.shape,t.dtype);m.set(d)}return l}function Olt(r,t,e,n,o){let s=0,i=n[0],a=n[1],u=i+o[0];for(let l=i;lx*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=mr({inputs:{x:o},backend:e,attrs:{shape:u}}),d=fo({inputs:{x:f},backend:e,attrs:{perm:l}}),h=mr({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Vo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeData(f.dataId),e.disposeData(d.dataId),e.disposeData(f.dataId),g}var zG={kernelName:$i,backendName:\"wasm\",kernelFunc:Llt};var BG;function zlt(r){BG=r.wasm.cwrap(Da,null,[\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\"])}function Blt(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i}=n,a=s.shape.reduce((p,m)=>p*m,1)!==0,u=o.shape.length===1?[i]:[o.shape[0],i],l=t.makeOutput(u,s.dtype);function c(p){return t.dataIdMap.get(p.dataId).id}return BG(c(o),i,a,c(s),kt[s.dtype],c(l)),l}var VG={kernelName:Da,backendName:\"wasm\",setupFunc:zlt,kernelFunc:Blt};function Vlt(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.typedArrayFromHeap(n),i=e.typedArrayFromHeap(o),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeOutput([a.length],\"int32\",void 0,new Int32Array(a))}var GG={kernelName:Yl,backendName:\"wasm\",kernelFunc:Vlt};function On(r){let{inputs:{x:t},attrs:{dtype:e},backend:n}=r,o=n.makeOutput(t.shape,e),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(o).set(s),o}var WG={kernelName:ho,backendName:\"wasm\",kernelFunc:On};var UG=Ct(es);var HG;function Glt(r){HG=r.wasm.cwrap(go,null,[\"number\",\"number\",\"number\",\"number\"])}function Wlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a=e.dataIdMap.get(o.dataId).id,u=e.makeOutput(o.shape,o.dtype),l=e.dataIdMap.get(u.dataId).id;return HG(a,s,i,l),u}var qG={kernelName:go,backendName:\"wasm\",setupFunc:Glt,kernelFunc:Wlt};function P1(r){let{inputs:t,backend:e}=r,n=y.parseAxisParam(r.attrs.axis,t[0].shape)[0],o=t.map(f=>f.shape);S.assertParamsConsistent(o,n);let s=S.computeOutShape(t.map(f=>f.shape),n),i=t.filter(f=>y.sizeFromShape(f.shape)>0);if(i.length===1)return Cp({inputs:{x:i[0]},backend:e});let a=e.makeOutput(s,t[0].dtype);if(y.sizeFromShape(s)===0)return a;if(i[0].dtype===\"string\"){let f=i.map(w=>{let N=[-1,y.sizeFromShape(w.shape.slice(n))];return mr({inputs:{x:w},backend:e,attrs:{shape:N}})}),d=f.map(w=>({vals:e.readSync(w.dataId),shape:w.shape}));s=S.computeOutShape(f.map(w=>w.shape),1);let h=f[0].shape[0]===1,g=rp(d,s,t[0].dtype,h),x=S.computeOutShape(i.map(w=>w.shape),n);a.shape=x;let b=e.dataIdMap.get(a.dataId);return b.stringBytes=S.fromStringArrayToUint8(g),f.forEach(w=>e.disposeData(w.dataId)),a}let u=y.sizeFromShape(i[0].shape.slice(0,n)),l=0,c=i.map(f=>{let d=y.sizeFromShape(f.shape.slice(n));return l+=d,d}),p=i.map(f=>e.typedArrayFromHeap(f)),m=e.typedArrayFromHeap(a);for(let f=0;f`cumprod does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=fo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims(\"cumprod\",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;lW(d,i?1:0,a?1:0,f,h,kt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=fo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var uW={kernelName:Fa,backendName:\"wasm\",setupFunc:rut,kernelFunc:nut};var cW;function out(r){cW=r.wasm.cwrap(as,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function sut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype===\"float32\"||o.dtype===\"int32\",()=>`cumsum does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=fo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims(\"cumsum\",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;cW(d,i?1:0,a?1:0,f,h,kt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=fo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var pW={kernelName:as,backendName:\"wasm\",setupFunc:out,kernelFunc:sut};var mW;function iut(r){mW=r.wasm.cwrap(\"DenseBincount\",null,[\"number\",\"array\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"boolean\",\"number\"])}function aut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i,binaryOutput:a}=n,u=s.shape.reduce((m,f)=>m*f,1)!==0,l=o.shape.length===1?[i]:[o.shape[0],i],c=t.makeOutput(l,s.dtype);function p(m){return t.dataIdMap.get(m.dataId).id}return mW(p(o),new Uint8Array(new Int32Array(o.shape).buffer),o.shape.length,i,u,p(s),kt[s.dtype],a,p(c)),c}var fW={kernelName:Jl,backendName:\"wasm\",setupFunc:iut,kernelFunc:aut};var dW;function lut(r){dW=r.wasm.cwrap(Pa,null,[\"number\",\"number\",\"number\",\"array\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function uut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i===\"NHWC\"?o.shape[1]:o.shape[2],l=i===\"NHWC\"?o.shape[2]:o.shape[3],c=i===\"NHWC\"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i===\"NHWC\"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,\"float32\"),x=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),I=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),N=t.dataIdMap.get(h.dataId).id;return dW(x,s,i===\"NHWC\"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var hW={kernelName:Pa,backendName:\"wasm\",setupFunc:lut,kernelFunc:uut};var gW;function cut(r){gW=r.wasm.cwrap(ls,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function put(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,I=f.dilationHeight,N=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,D=f.inChannels,F=f.outChannels,P=f.padInfo.type===\"SAME\"?1:0;if(f.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let V=n.makeOutput(f.outShape,\"float32\"),G=n.dataIdMap.get(V.dataId).id;return gW(i,o.shape[0],o.shape[1],o.shape[2],a,d,h,g,x,b,w,P,I,N,E,A,D,F,G),V}var xW={kernelName:ls,backendName:\"wasm\",setupFunc:cut,kernelFunc:put};var yW;function mut(r){yW=r.wasm.cwrap(\"Diag\",null,[\"number\",\"number\",\"number\",\"number\"])}function fut(r){let{inputs:t,backend:e}=r,{x:n}=t,o=y.sizeFromShape(n.shape),s=e.makeOutput([...n.shape,...n.shape],n.dtype);return yW(e.dataIdMap.get(n.dataId).id,kt[n.dtype],o,e.dataIdMap.get(s.dataId).id),s}var bW={kernelName:Ql,backendName:\"wasm\",setupFunc:mut,kernelFunc:fut};var wW;function dut(r){wW=r.wasm.cwrap(us,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function hut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;if(o.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${o.dtype} and ${s.dtype}`);let l=S.computeDilation2DInfo(o.shape,s.shape,i,a,\"NHWC\",u),c=e.makeOutput(l.outShape,o.dtype);return wW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,kt[o.dtype],l.batchSize,l.inChannels,l.inHeight,l.inWidth,l.outHeight,l.outWidth,l.strideHeight,l.strideWidth,l.dilationHeight,l.dilationWidth,l.filterHeight,l.filterWidth,l.padInfo.top,l.padInfo.left),c}var IW={kernelName:us,backendName:\"wasm\",setupFunc:dut,kernelFunc:hut};var CW;function gut(r){CW=r.wasm.cwrap(eu,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function xut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,\"NHWC\",l),p=e.makeOutput(s.shape,s.dtype);return CW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,kt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var vW={kernelName:eu,backendName:\"wasm\",setupFunc:gut,kernelFunc:xut};var SW;function yut(r){SW=r.wasm.cwrap(tu,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function but(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,\"NHWC\",l),p=e.makeOutput(o.shape,o.dtype);return SW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,kt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var NW={kernelName:tu,backendName:\"wasm\",setupFunc:yut,kernelFunc:but};var kW=Ct(ps);var TW;function wut(r){TW=r.wasm.cwrap(Ma,null,[\"number\",\"number\",\"number\"])}function Iut(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=e.makeOutput(o.shape,\"float32\"),i=a=>e.dataIdMap.get(a.dataId).id;return TW(i(o),i(n),i(s)),s}var _W={kernelName:Ma,backendName:\"wasm\",setupFunc:wut,kernelFunc:Iut};var Cut=!1,EW=ae(za,Cut,\"bool\");var AW=Ct(ms,\"float32\");function NC(r){let{inputs:t,attrs:e,backend:n}=r,{input:o}=t,{dim:s}=e,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),mr({inputs:{x:o},backend:n,attrs:{shape:a}})}var DW={kernelName:Fi,backendName:\"wasm\",kernelFunc:NC};var $W=Ct(fs,\"float32\");function L1(r){let{attrs:{shape:t,value:e,dtype:n},backend:o}=r,s=o.makeOutput(t,n);return o.typedArrayFromHeap(s).fill(e),s}var RW={kernelName:ru,backendName:\"wasm\",kernelFunc:L1};var FW;function vut(r){FW=r.wasm.cwrap(Ba,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Sut(r){let{inputs:t,backend:e}=r,{image:n}=t,o=e.makeOutput(n.shape,n.dtype),s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,[a,u,l,c]=n.shape;return FW(s,a,u,l,c,i),o}var OW={kernelName:Ba,backendName:\"wasm\",kernelFunc:Sut,setupFunc:vut};var PW=Ct(ds);var Nut=!1,MW=ae(hs,Nut);var LW;function kut(r){LW=r.wasm.cwrap(gs,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Tut(r){let{backend:t,inputs:e,attrs:n}=r,{varianceEpsilon:o}=n,{x:s,mean:i,variance:a,offset:u,scale:l}=e,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,m=t.dataIdMap.get(a.dataId).id,f=u!=null?t.dataIdMap.get(u.dataId).id:0,d=l!=null?t.dataIdMap.get(l.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return LW(c,p,m,f,d,o,g),h}var zW={kernelName:gs,backendName:\"wasm\",setupFunc:kut,kernelFunc:Tut};var BW;function _ut(r){BW=r.wasm.cwrap(ji,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Eut(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m),g=Ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,P=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,K=h.padInfo.type===\"SAME\"?1:0,X=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,\"float32\"),st=n.dataIdMap.get(nt.dataId).id,at=a==null?0:n.dataIdMap.get(a.dataId).id;return BW(x,X,Z,et,b,N,E,I,A,D,F,P,K,V,G,W,q,H,w,g,at,d||0,st),nt}var VW={kernelName:ji,backendName:\"wasm\",setupFunc:_ut,kernelFunc:Eut};var GW;function Aut(r){GW=r.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 Dut(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m,!0),g=Ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,P=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,K=h.padInfo.type===\"SAME\"?1:0,X=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,\"float32\"),st=n.dataIdMap.get(nt.dataId).id,at=a==null?0:n.dataIdMap.get(a.dataId).id;return GW(x,X,Z,et,b,N,E,I,A,D,F,P,K,V,G,W,q,H,w,g,at,d||0,st),nt}var WW={kernelName:Xi,backendName:\"wasm\",setupFunc:Aut,kernelFunc:Dut};var UW;function $ut(r){UW=r.wasm.cwrap(Va,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Rut(r){let{backend:t,inputs:e}=r,{params:n,indices:o}=e,[s,i,a,u]=Dy.prepareAndValidate(n,o),l=t.makeOutput(s,n.dtype);if(i===0)return l;let c=o.shape,p=c[c.length-1],f=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(o.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),x=t.dataIdMap.get(l.dataId).id;return UW(f,kt[n.dtype],h,i,p,a,g,x),l}var HW={kernelName:Va,backendName:\"wasm\",setupFunc:$ut,kernelFunc:Rut};var qW;function Fut(r){qW=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Out(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,indices:s}=e,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0],l=t.readSync(s.dataId),c=o.shape[u];for(let F=0;F=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),m=mr({inputs:{x:o},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),f=y.sizeFromShape(s.shape),d=mr({inputs:{x:s},attrs:{shape:[p.batchSize,f/p.batchSize]},backend:t}),h=[p.batchSize,p.outerSize,f/p.batchSize,p.sliceSize],g=t.makeOutput(h,o.dtype);if(y.sizeFromShape(o.shape)===0)return g;let x=m.shape.length-1,w=t.dataIdMap.get(m.dataId).id,N=t.dataIdMap.get(d.dataId).id,E=t.dataIdMap.get(g.dataId).id,A=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),D=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return qW(w,kt[o.dtype],A,x,N,p.batchSize,D,E),t.disposeData(m.dataId),t.disposeData(d.dataId),g.shape=p.outputShape,g}var KW={kernelName:Oi,backendName:\"wasm\",setupFunc:Fut,kernelFunc:Out};var Put=!1,jW=ae(Ga,Put,\"bool\");var Mut=!1,XW=ae(xs,Mut,\"bool\");var YW=Ct(ys,\"bool\");var ZW=Ct(bs,\"bool\");var JW=Ct(ws,\"bool\");var QW;function Lut(r){QW=r.wasm.cwrap(Is,null,[\"number\",\"number\",\"number\",\"number\"])}function zut(r){let{inputs:{x:t},attrs:{alpha:e},backend:n}=r,o=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,\"float32\");if(y.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;QW(o,kt[t.dtype],e,i)}return s}var tU={kernelName:Is,backendName:\"wasm\",setupFunc:Lut,kernelFunc:zut};var But=!1,eU=ae(Wa,But,\"bool\");var Vut=!1,rU=ae(Ua,Vut,\"bool\");var nU;function Gut(r){nU=r.wasm.cwrap(Ha,null,[\"number\",\"number\",\"number\",\"number\"])}function Wut(r){let{attrs:t,backend:e}=r,{start:n,stop:o,num:s}=t,i=Math.floor(s),a=e.makeOutput([i],\"float32\");return nU(e.dataIdMap.get(a.dataId).id,n,o,i),a}var oU={kernelName:Ha,backendName:\"wasm\",setupFunc:Gut,kernelFunc:Wut};var sU=Ct(Cs);var iU=Ct(vs);var Uut=!1,aU=ae(qa,Uut,\"bool\");var lU=Ct(Ka);var Hut=!1,uU=ae(ja,Hut,\"bool\");var qut=!1,cU=ae(g_,qut,\"bool\");var pU;function Kut(r){pU=r.wasm.cwrap(Ss,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function jut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;if(o.dtype!==\"float32\")throw new Error(\"LRN error: x must have dtype float32\");let l=e.makeOutput(o.shape,o.dtype);return pU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(l.dataId).id,o.shape[3],s,i,a,u),l}var mU={kernelName:Ss,backendName:\"wasm\",setupFunc:Kut,kernelFunc:jut};var fU;function Xut(r){fU=r.wasm.cwrap(Xa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Yut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n;if(o.dtype!==\"float32\"||s.dtype!==\"float32\"||i.dtype!==\"float32\")throw new Error(\"LRNGrad error: x, y, and dy must have dtype float32\");let p=e.makeOutput(o.shape,o.dtype);return fU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,i.shape[3],a,u,l,c),p}var dU={kernelName:Xa,backendName:\"wasm\",setupFunc:Xut,kernelFunc:Yut};var hU;function Zut(r){hU=r.wasm.cwrap(Ns,null,[\"number\",\"number\",\"number\",\"number\"])}function Jut(r){let{backend:t,inputs:e,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=vn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims(\"max\",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;hU(u,kt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var gU={kernelName:Ns,backendName:\"wasm\",setupFunc:Zut,kernelFunc:Jut};var Qut=!1,xU=ae(ks,Qut);var yU;function tct(r){yU=r.wasm.cwrap(Ts,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ect(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id;y.assert(o.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${o.dtype}.`);let{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=e,c=S.computePool2DInfo(o.shape,i,a,1,u,l),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,w=c.strideHeight,I=c.strideWidth,N=c.inChannels,E=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let A=n.makeOutput(c.outShape,\"float32\"),D=n.dataIdMap.get(A.dataId).id;return yU(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,b,w,I,N,E,D),A}var bU={kernelName:Ts,backendName:\"wasm\",setupFunc:tct,kernelFunc:ect};var wU;function rct(r){wU=r.wasm.cwrap(\"MaxPool3D\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function nct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.makeOutput(c.outShape,o.dtype);return wU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var IU={kernelName:Pi,backendName:\"wasm\",setupFunc:rct,kernelFunc:nct};var CU;function oct(r){CU=r.wasm.cwrap(\"MaxPool3DGrad\",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 sct(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return CU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var vU={kernelName:nu,backendName:\"wasm\",setupFunc:oct,kernelFunc:sct};var SU;function ict(r){SU=r.wasm.cwrap(_s,null,[\"number, number, number\"])}function act(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=vn(i,o,t),d=p;if(f){let I=t.dataIdMap.get(c.dataId).id;I!==a&&(l=c,u=I,d=S.getInnerMostAxes(d.length,l.shape.length))}S.assertAxesAreInnerMostDims(\"mean\",d,l.shape.length);let[h,g]=S.computeOutAndReduceShapes(l.shape,d),x=y.sizeFromShape(g),b=l;l.dtype!==\"float32\"&&(b=On({backend:t,inputs:{x:l},attrs:{dtype:\"float32\"}}),u=t.dataIdMap.get(b.dataId).id);let w=t.makeOutput(h,\"float32\");if(y.sizeFromShape(l.shape)!==0){let I=t.dataIdMap.get(w.dataId).id;SU(u,x,I)}if(f&&t.disposeData(c.dataId),s){let I=S.expandShapeToKeepDim(w.shape,m);w.shape=I}return l.dtype!==\"float32\"&&t.disposeData(b.dataId),w}var NU={kernelName:_s,backendName:\"wasm\",setupFunc:ict,kernelFunc:act};var kU;function lct(r){kU=r.wasm.cwrap(Es,null,[\"number\",\"number\",\"number\",\"number\"])}function uct(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=vn(i,o,t);if(f){let 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GraphModel,Ga as Greater,xs as GreaterEqual,Zy as History,Vp as IFFT,xo as Identity,Gp as Imag,Ie as InputSpec,ys as IsFinite,bs as IsInf,ws as IsNan,Wo as KernelBackend,Ss as LRN,Xa as LRNGrad,Dh as LayerVariable,qn as LayersModel,Is as LeakyRelu,Wa as Less,Ua as LessEqual,Ha as LinSpace,Cs as Log,vs as Log1p,x_ as LogSoftmax,qa as LogicalAnd,Ka as LogicalNot,ja as LogicalOr,g_ as LogicalXor,rmt as LowerBound,Uu as MathBackendCPU,ju as MathBackendWebGL,nmt as MatrixBandPart,Ns as Max,Ts as MaxPool,Pi as MaxPool3D,nu as MaxPool3DGrad,Wp as MaxPoolGrad,Up as MaxPoolWithArgmax,ks as Maximum,_s as Mean,Es as Min,As as Minimum,Ds as MirrorPad,Ya as Mod,Dc as MomentumOptimizer,Za as Multinomial,$s as Multiply,Mi as Neg,Qa as NonMaxSuppressionV3,tl as NonMaxSuppressionV4,el as NonMaxSuppressionV5,Ja as NotEqual,A0 as OP_SCOPE_SUFFIX,Rs as OneHot,Li as OnesLike,qr as Optimizer,Nh as OptimizerConstructors,zi as Pack,Fs as PadV2,omt as Pool,Os as Pow,Ps as Prelu,Ms as Prod,$c as RMSPropOptimizer,An as RNN,Hp as RaggedGather,qp as RaggedRange,Kp as RaggedTensorToTensor,ou as Range,w0 as Rank,jp as Real,cs as RealDiv,Ls as Reciprocal,Ze as Reduction,zs as Relu,Gs as Relu6,Bi as Reshape,Vs as ResizeBilinear,nl as ResizeBilinearGrad,Bs as ResizeNearestNeighbor,rl as ResizeNearestNeighborGrad,Ws as Reverse,fl as RotateWithOffset,Us as Round,Hs as Rsqrt,Cl as SGDOptimizer,ol as ScatterNd,il as SearchSorted,Vi as Select,qs as Selu,xa as Sequential,Xs as Sigmoid,js as Sign,Ks as Sin,al as Sinh,Gi as Slice,Qs as Softmax,Ys as Softplus,Wi as SpaceToBatchND,su as SparseFillEmptyRows,ll as SparseReshape,iu as SparseSegmentMean,au as SparseSegmentSum,ul as SparseToDense,Ui as SplitV,Zs as Sqrt,lu as Square,ti as SquaredDifference,sc as StaticRegexReplace,yo as Step,cl as StridedSlice,uu as StringNGrams,cu as StringSplit,pu as StringToHashBucketFast,ei as Sub,Js as Sum,rn as SymbolicTensor,ri as Tan,ni as Tanh,Pt as Tensor,le as TensorBuffer,sl as TensorScatterUpdate,so as Tile,pl as TopK,ml as Transform,io as Transpose,mu as Unique,Hi as Unpack,fu as UnsortedSegmentSum,smt as UpperBound,dl as Variable,qi as ZerosLike,Ki as _FusedMatMul,Ee as abs,dx as acos,hx as acosh,Y as add,fE as addN,am as all,dc as any,ta as argMax,gx as argMin,xx as asin,yx as asinh,bx as atan,wx as atan2,Ix as atanh,bu as avgPool,Cx as avgPool3d,mE as backend,S as backend_util,gE as basicLSTMCell,na as batchNorm,vx as batchNorm2d,Sx as batchNorm3d,Nx as batchNorm4d,wu as batchToSpaceND,kx as bincount,yE as bitwiseAnd,c5 as booleanMaskAsync,bE as broadcastArgs,oa as broadcastTo,Ur as broadcast_util,Ay as browser,bt as buffer,T9 as callbacks,Q as cast,Tx as ceil,vr as clipByValue,un as clone,Nn as complex,se as concat,_x as concat1d,Ex as concat2d,Ax as concat3d,Dx as concat4d,nR as constraints,um as conv1d,kn as conv2d,pm as conv2dTranspose,$x as conv3d,Fx as conv3dTranspose,mmt as copyRegisteredKernels,Iu as cos,mm as cosh,Ih as cosineWindow,gc as cumprod,fm as cumsum,mn as customGrad,VF as data,gh as denseBincount,B0 as deprecationWarn,Ox as depthToSpace,sa as depthwiseConv2d,D9 as deregisterOp,xu as device_util,wE as diag,Px as dilation2d,vdt as disableDeprecationWarnings,Tt as dispose,Sdt as disposeVariables,ct as div,Mx as divNoNan,Lx as dot,nN as dropout,CE as einsum,ia as elu,Cdt as enableDebugMode,Idt as enableProdMode,oN as enclosingPowerOfTwo,Vn as engine,vE as ensureShape,L as env,Rr as equal,zx as erf,Bx as euclideanNorm,ir as exp,ar as expandDims,Vx as expm1,xc as eye,Au as fft,vo as fill,Adt as findBackend,Ddt as findBackendFactory,aa as floor,im as floorDiv,Dz as forceHalfFloat,Ru as fused,la as gather,w5 as gatherND,Dy as gather_util,_dt as getBackend,m0 as getGradient,ih as getKernel,Zg as getKernelsForBackend,Wpt as getThreadsCount,f1 as gpgpu_util,f6 as grad,d6 as grads,Fe as greater,pn as greaterEqual,Il as ifft,Cu as imag,dn as image,v5 as inTopKAsync,oR as initializers,BN as input,Mr as io,km as irfft,Gx as isFinite,Wx as isInf,Ux as isNaN,$e as keep,Kr as kernel_impls,LR as layers,vu as leakyRelu,bl as less,Gn as lessEqual,iN as linalg,kE as linspace,TQ as loadGraphModel,_Q as loadGraphModelSync,SR as loadLayersModel,Hx as localResponseNormalization,Nr as log,Su as log1p,jx as logSigmoid,dm as logSoftmax,hm as logSumExp,Or as logicalAnd,Nu as logicalNot,gm as logicalOr,Xx as logicalXor,S8 as losses,TE as lowerBound,Bt as matMul,y2 as math,Sr as max,ku as maxPool,Zx as maxPool3d,_E as maxPoolWithArgmax,Tn as maximum,ke as mean,fh as memory,EE as meshgrid,zR as metrics,xl as min,uo as minimum,Jx as mirrorPad,Qx as mod,T7 as model,BR as models,yc as moments,f5 as movingAverage,$ as mul,AE as multiRNNCell,DE as multinomial,Ut as neg,kh as nextFrame,yl as norm,li as notEqual,ua as oneHot,dr as ones,Ir as onesLike,k as op,$E as outerProduct,fn as pad,RE as pad1d,FE as pad2d,OE as pad3d,PE as pad4d,ty as pool,cn as pow,_u as prelu,fx as print,ey as prod,Ndt as profile,ME as raggedGather,LE as raggedRange,zE as raggedTensorToTensor,BE as rand,aA as randomGamma,Ic as randomNormal,lA as randomStandardNormal,Wn as randomUniform,uA as randomUniformInt,ca as range,Tdt as ready,wl as real,ay as reciprocal,sm as registerBackend,E7 as registerCallbackConstructor,b_ as registerGradient,ic as registerKernel,A9 as registerOp,VR as regularizers,Pr as relu,xm as relu6,Edt as removeBackend,R as reshape,hr as reverse,cA as reverse1d,pA as reverse2d,mA as reverse3d,fA as reverse4d,Du as rfft,ym as round,bm as rsqrt,ft as scalar,h5 as scatterND,$u as scatter_util,yh as searchSorted,wm as selu,Im as separableConv2d,_7 as sequential,J as serialization,bK as setBackend,$dt as setPlatform,Gpt as setThreadsCount,Bpt as setWasmPath,Vpt as setWasmPaths,TT as setWebGLContext,dA as setdiff1dAsync,Nw as shared,tn as sigmoid,ly as sign,v8 as signal,Cm as sin,vm as sinh,Ot as slice,Sm as slice1d,wh as slice2d,Nm as slice3d,Cc as slice4d,ze as slice_util,Eu as softmax,ai as softplus,Tu as spaceToBatchND,N8 as sparse,y5 as sparseToDense,C8 as spectral,gr as split,Ne as sqrt,Wt as square,Tm as squaredDifference,Un as squeeze,qe as stack,No as step,uy as stridedSlice,k8 as string,lt as sub,pt as sum,mc as sumOutType,cy as tan,ra as tanh,sr as tensor,Ke as tensor1d,ui as tensor2d,py as tensor3d,hA as tensor4d,gA as tensor5d,xA as tensor6d,bA as tensorScatterUpdate,Co as tensor_util,iA as test_util,B as tidy,Fr as tile,kdt as time,my as topk,Fc as train,Vt as transpose,Em as truncatedNormal,fy as unique,pmt as unregisterGradient,cmt as unregisterKernel,Am as unsortedSegmentSum,xr as unstack,ur as upcastType,wA as upperBound,y as util,h6 as valueAndGrad,g6 as valueAndGrads,dy as variable,qx as variableGrads,Ypt as version,IF as version_converter,A2 as version_core,TO as version_cpu,tf as version_layers,Upt as version_wasm,Az as version_webgl,aDe as webgl,Td as webgl_util,be as where,gy as whereAsync,Te as zeros,vt as zerosLike};\n", "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 = 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 = 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 ${JSON.stringify({ width, height })}`);\n }\n\n this._width = width;\n this._height = height;\n }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public reverse(): Dimensions {\n return new Dimensions(1 / this.width, 1 / this.height);\n }\n}\n", "export interface IPoint {\n x: number\n y: number\n}\n\nexport class Point implements IPoint {\n private _x: number;\n\n private _y: number;\n\n constructor(x: number, y: number) {\n this._x = x;\n this._y = y;\n }\n\n get x(): number { return this._x; }\n\n get y(): number { return this._y; }\n\n public add(pt: IPoint): Point {\n return new Point(this.x + pt.x, this.y + pt.y);\n }\n\n public sub(pt: IPoint): Point {\n return new Point(this.x - pt.x, this.y - pt.y);\n }\n\n public mul(pt: IPoint): Point {\n return new Point(this.x * pt.x, this.y * pt.y);\n }\n\n public div(pt: IPoint): Point {\n return new Point(this.x / pt.x, this.y / pt.y);\n }\n\n public abs(): Point {\n return new Point(Math.abs(this.x), Math.abs(this.y));\n }\n\n public magnitude(): number {\n return Math.sqrt((this.x ** 2) + (this.y ** 2));\n }\n\n public floor(): Point {\n return new Point(Math.floor(this.x), Math.floor(this.y));\n }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box implements IBoundingBox, IRect {\n public static isRect(rect: any): boolean {\n return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n }\n\n public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions = false) {\n if (!Box.isRect(box)) {\n throw new Error(`${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 = 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 {\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 {\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 {\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({ x, y, width, height });\n }\n\n public rescale(s: IDimensions | number): Box {\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 {\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({ x, y, width, height });\n }\n\n public clipAtImageBorders(imgWidth: number, imgHeight: number): Box {\n const { x, y, right, bottom } = 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({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();\n }\n\n public shift(sx: number, sy: number): Box {\n const { width, height } = this;\n const x = this.x + sx;\n const y = this.y + sy;\n\n return new Box({ x, y, width, height });\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 { dy, edy, dx, edx, y, ey, x, ex, w, h };\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 implements IBoundingBox {\n constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions = false) {\n super({ left, top, right, bottom }, 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 override 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 = 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 = 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(imgTensor: tf.Tensor4D, isCenterImage = false): tf.Tensor4D {\n return tf.tidy(() => {\n const [height, width] = imgTensor.shape.slice(1);\n if (height === width) return imgTensor;\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 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 const paddingTensorAppend = createPaddingTensor(paddingAmount);\n const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null;\n const tensorsToStack = [paddingTensorPrepend, imgTensor, paddingTensorAppend]\n .filter((t) => !!t)\n .map((t) => tf.cast(t as tf.Tensor4D, '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 implements IRect {\n constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions = false) {\n super({ x, y, width, height }, 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(width: number, height: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n { width, height },\n );\n }\n\n public shiftBy(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(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 override 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 override 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 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 = 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 {\n public static assertIsValidLabeledBox(box: any, callee: string) {\n Box.assertIsValidBox(box, callee);\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 & {\n detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection(sourceObj: TSource, detection: FaceDetection): WithFaceDetection {\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 createVideoElement: () => document.createElement('video'),\n fetch,\n readFile,\n };\n}\n", "export function isNodejs(): boolean {\n return typeof global === 'object'\n && typeof process !== 'undefined'\n && process.versions != null\n && process.versions.node != null;\n}\n", "import { FileSystem } from './types';\nimport { isNodejs } from './isNodejs';\n\nexport function createFileSystem(fs?: any): FileSystem {\n let requireFsError = '';\n if (!fs && isNodejs()) {\n try {\n // eslint-disable-next-line global-require\n fs = require('fs');\n } catch (err) {\n requireFsError = (err as any).toString();\n }\n }\n\n const readFile = fs\n ? (filePath: string) => new Promise((resolve, reject) => { fs.readFile(filePath, (err: any, buffer) => (err ? reject(err) : resolve(buffer))); })\n : () => { throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`); };\n return { readFile };\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 // eslint-disable-next-line dot-notation\n const Video = global['Video'] || global.HTMLVideoElement;\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 createVideoElement = () => {\n if (Video) return new Video();\n throw new Error('createVideoElement - missing Video 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 createVideoElement,\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) {\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 { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } 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,\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)) resolve(null);\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 {\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 {\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 = 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 if (inputSize <= 0) return createCanvas({ width: 1, height: 1 });\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 if (inputCanvas.width > 0 && inputCanvas.height > 0) 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 { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n private _imageTensors: Array = [];\n\n private _canvases: HTMLCanvasElement[] = [];\n\n private _batchSize: number;\n\n private _treatAsBatchInput = false;\n\n private _inputDimensions: number[][] = [];\n\n private _inputSize = 0;\n\n constructor(inputs: Array, treatAsBatchInput = false) {\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 // @ts-ignore\n const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n this._canvases[idx] = canvas as HTMLCanvasElement;\n this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n });\n }\n\n public get imageTensors(): Array {\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 = 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 let imgTensor = isTensor4D(input) ? input : tf.expandDims(input);\n imgTensor = padToSquare(imgTensor as tf.Tensor4D, isCenterInputs);\n\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n imgTensor = tf['image'].resizeBilinear(imgTensor as tf.Tensor4D, [inputSize, inputSize], false, false);\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) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n // const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))) as tf.Tensor4D;\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 {\n if (inputs instanceof NetInput) return inputs;\n const inputArgArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArgArray.length) throw new Error('toNetInput - empty array passed as input');\n const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n const inputArray = inputArgArray.map(resolveInput);\n inputArray.forEach((input, i) => {\n if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n if (typeof inputArgArray[i] === 'string') throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n }\n 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) throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n });\n // wait for all media elements being loaded\n await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)));\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(input: TNetInput, detections: Array): Promise {\n const { Canvas } = env.getEnv();\n let canvas = input as HTMLCanvasElement;\n if (!(input instanceof Canvas)) {\n const netInput = await toNetInput(input);\n if (netInput.batchSize > 1) throw new Error('extractFaces - batchSize > 1 not supported');\n const tensorOrCanvas = netInput.getInput(0);\n canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);\n }\n const ctx = getContext2dOrThrow(canvas);\n const boxes = detections\n .map((det) => (det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det))\n .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n return boxes.map(({ x, y, width, height }) => {\n const faceImg = createCanvas({ width, height });\n if (width > 0 && height > 0) getContext2dOrThrow(faceImg).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(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array): Promise {\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 const boxes = detections.map((det) => (det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det))\n .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n const faceTensors = boxes\n .filter((box) => box.width > 0 && box.height > 0)\n .map(({ x, y, width, height }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\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 {\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 {\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(uri: string): Promise {\n return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise {\n return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "import { env } from '../env/index';\n\nexport function bufferToVideo(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToVideo - expected buf to be of type: Blob'));\n\n const video = env.getEnv().createVideoElement();\n video.oncanplay = () => resolve(video);\n video.onerror = reject;\n video.playsInline = true;\n video.muted = true;\n video.src = URL.createObjectURL(buf);\n video.play();\n });\n}\n", "import { bufferToVideo } from './bufferToVideo';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchVideo(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('video/')) {\n throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToVideo(blob);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n const defaultManifestFilename = `${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 {\n const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n // @ts-ignore\n const manifest = await fetchJson(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 = 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 {\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 = true) {\n this.getParamList().forEach((param) => {\n if (throwOnRedispose && param.tensor.isDisposed) {\n throw new Error(`param tensor has already been disposed for path ${param.path}`);\n }\n param.tensor.dispose();\n });\n this._params = undefined;\n }\n\n public serializeParams(): Float32Array {\n return new Float32Array(\n this.getParamList()\n .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n .reduce((flat, arr) => flat.concat(arr)),\n );\n }\n\n public async load(weightsOrUrl: Float32Array | string | undefined): Promise {\n if (weightsOrUrl instanceof Float32Array) {\n this.extractWeights(weightsOrUrl);\n return;\n }\n await this.loadFromUri(weightsOrUrl);\n }\n\n public async loadFromUri(uri: string | undefined) {\n if (uri && typeof uri !== 'string') {\n throw new Error(`${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 = 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 = false,\n isScaleDown = 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 = 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: (originalPath: string, paramRank: number) => T,\n) {\n return (prefix: string): SeparableConvParams => {\n const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4);\n const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n return (originalPath: string, paramRank: number, mappedPath?: string) => {\n const tensor = weightMap[originalPath];\n\n if (!isTensor(tensor, paramRank)) {\n throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n }\n\n paramMappings.push(\n { originalPath, paramPath: mappedPath || originalPath },\n );\n\n return tensor;\n };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n let remainingWeights = weights;\n\n function extractWeights(numWeights: number): Float32Array {\n const ret = remainingWeights.slice(0, numWeights);\n remainingWeights = remainingWeights.slice(numWeights);\n return ret;\n }\n\n function getRemainingWeights(): Float32Array {\n return remainingWeights;\n }\n\n return {\n extractWeights,\n getRemainingWeights,\n };\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock4Params {\n const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${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: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: {\n dense0, dense1, dense2, dense3,\n },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: (originalPath: string, paramRank: number) => T) {\n return (prefix: string): ConvParams => {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return { filters, bias };\n };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractDenseBlock3Params(prefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(prefix: string, isFirstLayer = false): DenseBlock4Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${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 implements IFaceFeatureExtractor {\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(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 {\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: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: extractFcParams('fc'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n const featureExtractorMap: tf.NamedTensorMap = {};\n const classifierMap: tf.NamedTensorMap = {};\n\n Object.keys(weightMap).forEach((key) => {\n const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n map[key] = weightMap[key];\n });\n\n return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends NeuralNetwork {\n protected _faceFeatureExtractor: IFaceFeatureExtractor;\n\n constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor) {\n super(_name);\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): IFaceFeatureExtractor {\n return this._faceFeatureExtractor;\n }\n\n protected abstract override getDefaultModelName(): string\n\n protected abstract getClassifierChannelsIn(): number\n\n protected abstract getClassifierChannelsOut(): number\n\n public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${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 override dispose(throwOnRedispose = 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 = 0;\n public happy = 0;\n public sad = 0;\n public angry = 0;\n public fearful = 0;\n public disgusted = 0;\n public surprised = 0;\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 {\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 {\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 = t.dataSync();\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 & { expressions: FaceExpressions }\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions(sourceObj: TSource, expressions: FaceExpressions): WithFaceExpressions {\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(canvasArg: string | HTMLCanvasElement, faceExpressions: DrawFaceExpressionsInput | Array, minConfidence = 0.1, textFieldAnchor?: IPoint) {\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\n> = TSource & {\n landmarks: TFaceLandmarks;\n unshiftedLandmarks: TFaceLandmarks;\n alignedRect: FaceDetection;\n angle: {\n roll: number | undefined;\n pitch: number | undefined;\n yaw: number | undefined;\n };\n};\n\nexport function isWithFaceLandmarks(\n obj: any,\n): obj is WithFaceLandmarks, FaceLandmarks> {\n return (\n 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}\n\nfunction calculateFaceAngle(mesh) {\n // Helper to convert radians to degrees\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const degrees = (radians) => (radians * 180) / Math.PI;\n const calcLengthBetweenTwoPoints = (a, b) => Math.sqrt((a._x - b._x) ** 2 + (a._y - b._y) ** 2);\n\n const angle = {\n roll: undefined,\n pitch: undefined,\n yaw: undefined,\n };\n\n const calcYaw = (leftPoint, midPoint, rightPoint) => {\n // Calc x-distance from left side of the face (\"ear\") to facial midpoint (\"nose\")\n const leftToMidpoint = Math.floor(leftPoint._x - midPoint._x);\n // Calc x-distance from facial midpoint (\"nose\") to the right side of the face (\"ear\")\n const rightToMidpoint = Math.floor(midPoint._x - rightPoint._x);\n // Difference in distances coincidentally approximates to angles\n return leftToMidpoint - rightToMidpoint;\n };\n\n const calcRoll = (lever, pivot) => {\n // When rolling, the head seems to pivot from the nose/lips/chin area.\n // So, we'll choose any two points from the facial midline, where the first point should be the pivot, and the other \"lever\"\n // Plan/Execution: get the hypotenuse & opposite sides of a 90deg triangle ==> Calculate angle in radians\n const hypotenuse = Math.hypot(pivot._x - lever._x, pivot._y - lever._y);\n const opposite = pivot._y - lever._y;\n const angleInRadians = Math.asin(opposite / hypotenuse);\n const angleInDegrees = degrees(angleInRadians);\n const normalizeAngle = Math.floor(90 - angleInDegrees);\n // If lever more to the left of the pivot, then we're tilting left\n // \"-\" is negative direction. \"+\", or absence of a sign is positive direction\n const tiltDirection = pivot._x - lever._x < 0 ? -1 : 1;\n const result = normalizeAngle * tiltDirection;\n return result;\n };\n\n const calcPitch = (leftPoint, midPoint, rightPoint) => {\n // Theory: While pitching, the nose is the most salient point --> That's what we'll use to make a trianle.\n // The \"base\" is between point that don't move when we pitch our head (i.e. an imaginary line running ear to ear through the nose).\n // Executuin: Get the opposite & adjacent lengths of the triangle from the ear's perspective. Use it to get angle.\n\n const base = calcLengthBetweenTwoPoints(leftPoint, rightPoint);\n // adjecent is base/2 technically.\n const baseCoords = {\n _x: (leftPoint._x + rightPoint._x) / 2,\n _y: (leftPoint._y + rightPoint._y) / 2,\n };\n const midToBaseLength = calcLengthBetweenTwoPoints(midPoint, baseCoords);\n const angleInRadians = Math.atan(midToBaseLength / base);\n const angleInDegrees = Math.floor(degrees(angleInRadians));\n // Account for directionality.\n // pitch forwards (_i.e. tilting your head forwards) is positive (or no sign); backward is negative.\n const direction = baseCoords._y - midPoint._y < 0 ? -1 : 1;\n const result = angleInDegrees * direction;\n return result;\n };\n\n if (!mesh || !mesh._positions || mesh._positions.length !== 68) return angle;\n const pt = mesh._positions;\n angle.roll = calcRoll(pt[27], pt[66]);\n angle.pitch = calcPitch(pt[14], pt[30], pt[2]);\n angle.yaw = calcYaw(pt[14], pt[33], pt[2]);\n return angle;\n}\n\nexport function extendWithFaceLandmarks, TFaceLandmarks extends FaceLandmarks = FaceLandmarks68>(\n sourceObj: TSource,\n unshiftedLandmarks: TFaceLandmarks,\n): WithFaceLandmarks {\n const { box: shift } = sourceObj.detection;\n const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);\n const rect = landmarks.align();\n const { imageDims } = sourceObj.detection;\n const alignedRect = new FaceDetection(\n sourceObj.detection.score,\n rect.rescale(imageDims.reverse()),\n imageDims,\n );\n const angle = calculateFaceAngle(unshiftedLandmarks);\n const extension = { landmarks, unshiftedLandmarks, alignedRect, angle };\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>\n\nexport function drawFaceLandmarks(\n canvasArg: string | HTMLCanvasElement,\n faceLandmarks: DrawFaceLandmarksInput | Array,\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> or array thereof');\n }\n\n new DrawFaceLandmarks(landmarks).draw(canvasArg);\n });\n}\n", "{\n \"name\": \"@vladmandic/face-api\",\n \"version\": \"1.7.11\",\n \"description\": \"FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS\",\n \"sideEffects\": false,\n \"main\": \"dist/face-api.node.js\",\n \"module\": \"dist/face-api.esm.js\",\n \"browser\": \"dist/face-api.esm.js\",\n \"types\": \"types/face-api.d.ts\",\n \"author\": \"Vladimir Mandic \",\n \"bugs\": {\n \"url\": \"https://github.com/vladmandic/face-api/issues\"\n },\n \"homepage\": \"https://vladmandic.github.io/face-api/demo/webcam.html\",\n \"license\": \"MIT\",\n \"engines\": {\n \"node\": \">=14.0.0\"\n },\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/vladmandic/face-api.git\"\n },\n \"scripts\": {\n \"start\": \"node --no-warnings demo/node.js\",\n \"dev\": \"build --profile development\",\n \"build\": \"node build.js\",\n \"lint\": \"eslint src/ demo/\",\n \"test\": \"node --trace-warnings test/test-node.js\",\n \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n },\n \"keywords\": [\n \"face-api\",\n \"faceapi\",\n \"face-detection\",\n \"age-gender\",\n \"emotion-detection\",\n \"face-recognition\",\n \"face\",\n \"face-description\",\n \"tensorflow\",\n \"tensorflowjs\",\n \"tfjs\"\n ],\n \"devDependencies\": {\n \"@canvas/image\": \"^1.0.1\",\n \"@microsoft/api-extractor\": \"^7.34.8\",\n \"@tensorflow/tfjs\": \"^4.5.0\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.5.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.5.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.5.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"0.0.1-alpha.20\",\n \"@tensorflow/tfjs-converter\": \"^4.5.0\",\n \"@tensorflow/tfjs-core\": \"^4.5.0\",\n \"@tensorflow/tfjs-data\": \"^4.5.0\",\n \"@tensorflow/tfjs-layers\": \"^4.5.0\",\n \"@tensorflow/tfjs-node\": \"^4.5.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.5.0\",\n \"@types/node\": \"^20.1.0\",\n \"@types/offscreencanvas\": \"^2019.7.0\",\n \"@typescript-eslint/eslint-plugin\": \"^5.59.2\",\n \"@typescript-eslint/parser\": \"^5.59.2\",\n \"@vladmandic/build\": \"^0.9.2\",\n \"@vladmandic/pilogger\": \"^0.4.8\",\n \"esbuild\": \"^0.17.18\",\n \"eslint\": \"^8.40.0\",\n \"eslint-config-airbnb-base\": \"^15.0.0\",\n \"eslint-plugin-import\": \"^2.27.5\",\n \"eslint-plugin-json\": \"^3.1.0\",\n \"eslint-plugin-node\": \"^11.1.0\",\n \"eslint-plugin-promise\": \"^6.1.1\",\n \"rimraf\": \"^5.0.0\",\n \"seedrandom\": \"^3.0.5\",\n \"tslib\": \"^2.5.0\",\n \"typedoc\": \"^0.24.7\",\n \"typescript\": \"5.0.4\"\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: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { entry_flow, middle_flow, exit_flow },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${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 = 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 {\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(255) 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 {\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: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc: { age, gender } },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: {\n age: extractFcParams('fc/age'),\n gender: extractFcParams('fc/gender'),\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n // eslint-disable-next-line no-unused-vars\n FEMALE = 'female',\n // eslint-disable-next-line no-unused-vars\n MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n age: number\n gender: Gender\n genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n fc: {\n age: FCParams\n gender: FCParams\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork {\n private _faceFeatureExtractor: TinyXception;\n\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n super('AgeGenderNet');\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): TinyXception {\n return this._faceFeatureExtractor;\n }\n\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${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 {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictAgeAndGender(input: TNetInput): Promise {\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 = (ageTensor.dataSync())[0];\n const probMale = (genderTensor.dataSync())[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 override dispose(throwOnRedispose = 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 {\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 {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async detectLandmarks(input: TNetInput): Promise {\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(landmarkTensor.dataSync());\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 {\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 implements IFaceFeatureExtractor {\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(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 {\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 {\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 { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { isFloat } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } 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, `${mappedPrefix}/scale`);\n\n return { conv, scale };\n }\n\n function extractResidualLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n isDown = false,\n ): ResidualLayerParams {\n const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${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(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(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(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 {\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(255) 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 as tf.Tensor2D;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async computeFaceDescriptor(input: TNetInput): Promise {\n // @ts-ignore\n if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n const netInput = await toNetInput(input);\n const faceDescriptorTensors = tf.tidy(() => tf.unstack(this.forwardInput(netInput)));\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 & {\n descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n TSource\n>(\n sourceObj: TSource,\n descriptor: Float32Array,\n): WithFaceDescriptor {\n const extension = { descriptor };\n return { ...sourceObj, ...extension };\n}\n", "export type WithAge = 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 {\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 & {\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 {\n const extension = { gender, genderProbability };\n return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common/index';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } 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: ${getRemainingWeights().length}`);\n }\n\n return {\n params: {\n mobilenetv1,\n prediction_layer,\n output_layer,\n },\n paramMappings,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n const filters = extractWeightEntry(`${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(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]) {\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(x: tf.Tensor4D, params: MobileNetV1.DepthwiseConvParams, strides: [number, number]) {\n return tf.tidy(() => {\n let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n out = tf.batchNorm(\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) conv11 = out;\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) return 0.0;\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) * 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(maxOutputSize, numBoxes);\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], 2)),\n tf.add(vec[1], tf.div(sizes[1], 2)),\n ];\n return { sizes, centers };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n const { sizes, centers } = 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], 5)), sizes[0]), 2);\n const add0_out = tf.add(tf.mul(tf.div(vec[0], 10), sizes[0]), centers[0]);\n const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], 5)), sizes[1]), 2);\n const add1_out = tf.add(tf.mul(tf.div(vec[1], 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(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams) {\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(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);\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(scores, [batchSize, scores.shape[1] as number]);\n\n const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n return { boxes: boxesByBatch, scores: scoresByBatch };\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 = '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 {\n constructor() {\n super('SsdMobilenetv1');\n }\n\n public forwardInput(input: NetInput) {\n const { params } = this;\n if (!params) throw new Error('SsdMobilenetv1 - load model before inference');\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n const x = tf.sub(tf.div(batchTensor, 127.5), 1) as tf.Tensor4D; // input is normalized -1..1\n const features = mobileNetV1(x, params.mobilenetv1);\n const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);\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(input: TNetInput, options: ISsdMobilenetv1Options = {}): Promise {\n const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n const netInput = await toNetInput(input);\n const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput);\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 const scoresData = Array.from(scores.dataSync());\n const iouThreshold = 0.5;\n const indices = nonMaxSuppression(boxes, scoresData as number[], maxResults, iouThreshold, minConfidence);\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 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(left, top, right - left, bottom - top),\n { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) },\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 = '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 {\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(255) 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 {\n return this.forwardInput(await toNetInput(input), inputSize);\n }\n\n public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise {\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 = 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 {\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 override getDefaultModelName(): string {\n return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n }\n\n protected override 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 = true) {\n const net = new TinyYolov2(withSeparableConvs);\n net.extractWeights(weights);\n return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport type ITinyFaceDetectorOptions = ITinyYolov2Options\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n protected override _name = 'TinyFaceDetectorOptions';\n}\n", "export class ComposableTask {\n // eslint-disable-next-line no-unused-vars\n public async then(onfulfilled: (value: T) => T | PromiseLike): Promise {\n return onfulfilled(await this.run());\n }\n\n public async run(): Promise {\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, TResult>(\n parentResults: TSource[],\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResults: (faces: Array) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment: (parentResult: WithFaceLandmarks) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n ? getRectForAlignment(parentResult)\n : parentResult.detection));\n const faces: Array = extractedFaces || (\n input instanceof tf.Tensor\n ? await extractFaceTensors(input, faceBoxes)\n : await extractFaces(input, faceBoxes)\n );\n const results = await computeResults(faces);\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return results;\n}\n\nexport async function extractSingleFaceAndComputeResult, TResult>(\n parentResult: TSource,\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment?: (parentResultLocal: WithFaceLandmarks) => FaceDetection,\n) {\n return extractAllFacesAndComputeResults(\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 {\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 override getDefaultModelName(): string {\n return 'tiny_face_detector_model';\n }\n\n protected override 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 => 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 => 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 => 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 => 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 => 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 => 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 => 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 => 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 { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllFaceExpressionsTask> extends PredictFaceExpressionsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n\n const faceExpressionsByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(\n faces.map((face) => nets.faceExpressionNet.predictExpressions(face) as Promise),\n ),\n this.extractedFaces,\n );\n\n return parentResults.map(\n (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]),\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsTask> extends PredictFaceExpressionsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const faceExpressions = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceExpressionNet.predictExpressions(face) as Promise,\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>> extends PredictAllFaceExpressionsTask {\n override 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>> extends PredictSingleFaceExpressionsTask {\n override 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 { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllAgeAndGenderTask> extends PredictAgeAndGenderTaskBase>[], TSource[]> {\n public override async run(): Promise>[]> {\n const parentResults = await this.parentTask;\n const ageAndGenderByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise)),\n this.extractedFaces,\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> extends PredictAgeAndGenderTaskBase> | undefined, TSource | undefined> {\n public override async run(): Promise> | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise,\n this.extractedFaces,\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>> extends PredictAllAgeAndGenderTask {\n override 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>> extends PredictSingleAgeAndGenderTask {\n override 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 { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n ) {\n super();\n }\n}\n\nexport class ComputeAllFaceDescriptorsTask>> extends ComputeFaceDescriptorsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const descriptors = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise)),\n null,\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n return descriptors.map((descriptor, i) => extendWithFaceDescriptor(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>> extends ComputeFaceDescriptorsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const descriptor = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise,\n null,\n // eslint-disable-next-line no-shadow, @typescript-eslint/no-shadow\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\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 { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\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> extends DetectFaceLandmarksTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const detections = parentResults.map((res) => res.detection);\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, detections)\n : await extractFaces(this.input, detections);\n const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face))) as FaceLandmarks68[];\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n const result = parentResults\n .filter((_parentResult, i) => faceLandmarksByFace[i])\n .map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));\n return result;\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> extends DetectFaceLandmarksTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n const { detection } = parentResult;\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, [detection])\n : await extractFaces(this.input, [detection]);\n const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return extendWithFaceLandmarks(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 extends ComposableTask {\n // eslint-disable-next-line no-unused-vars\n constructor(protected input: TNetInput, protected options: FaceDetectionOptions = new SsdMobilenetv1Options()) {\n super();\n }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase {\n public override async run(): Promise {\n const { input, options } = this;\n let result;\n if (options instanceof TinyFaceDetectorOptions) result = nets.tinyFaceDetector.locateFaces(input, options);\n else if (options instanceof SsdMobilenetv1Options) result = nets.ssdMobilenetv1.locateFaces(input, options);\n else if (options instanceof TinyYolov2Options) result = nets.tinyYolov2.locateFaces(input, options);\n else throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options');\n return result;\n }\n\n private runAndExtendWithFaceDetections(): Promise[]> {\n return new Promise[]>((resolve, reject) => {\n this.run()\n .then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection))))\n .catch((err) => reject(err));\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = 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 {\n public override async run(): Promise {\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) faceDetectionWithHighestScore = faceDetection;\n });\n return faceDetectionWithHighestScore;\n }\n\n private runAndExtendWithFaceDetection(): Promise | undefined> {\n // eslint-disable-next-line no-async-promise-executor\n return new Promise | undefined>(async (resolve) => {\n const detection = await this.run();\n resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = 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(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectSingleFaceTask {\n return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): 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\nexport async function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise>>[]> {\n return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise>>[]> {\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 const desc1 = Array.from(arr1);\n const desc2 = Array.from(arr2);\n return Math.sqrt(\n desc1\n .map((val, i) => val - desc2[i])\n .reduce((res, diff) => res + (diff * diff), 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 private _distanceThreshold: number;\n\n constructor(inputs: LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>, distanceThreshold = 0.6) {\n this._distanceThreshold = distanceThreshold;\n const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArray.length) throw new Error('FaceRecognizer.constructor - expected atleast one input');\n let count = 1;\n const createUniqueLabel = () => `person ${count++}`;\n this._labeledDescriptors = inputArray.map((desc) => {\n if (desc instanceof LabeledFaceDescriptors) return desc;\n if (desc instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n if (desc.descriptor && desc.descriptor instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | 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) / (descriptors.length || 1);\n }\n\n public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n return this.labeledDescriptors\n .map(({ descriptors, label }) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors)))\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) ? bestMatch : 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.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(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).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\nexport const version = pkg.version as string;\n\n// set webgl defaults\n// if (browser) tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n"], + "mappings": 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t=[];return sN(e,t,new Set),t}function sN(e,t,n){if(e==null)return;if(e instanceof Ce){t.push(e);return}if(!lM(e))return;let a=e;for(let r in a){let s=a[r];n.has(s)||(n.add(s),sN(s,t,n))}}function lM(e){return Array.isArray(e)||typeof e=="object"}function rx(e){return e.kernelName!=null}var nI=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()}},bc=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new nI}async 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i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;return a!=null&&(p=_(a,"offset","batchNorm")),A(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),A(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),A(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${p.rank}.`),Cs(i,o,l,p,u,s)}var Wv=L({batchNorm2d_:VP});function UP(e,t,n,a,r,s){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;return a!=null&&(p=_(a,"offset","batchNorm")),A(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),A(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),A(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${p.rank}.`),Cs(i,o,l,p,u,s)}var Bv=L({batchNorm3d_:UP});function GP(e,t,n,a,r,s){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;return a!=null&&(p=_(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}.`),u!=null&&A(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${p.rank}.`),Cs(i,o,l,p,u,s)}var Vv=L({batchNorm4d_:GP});function HP(e,t,n){let a=_(e,"x","bincount"),r=_(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 ${n}.`),A(r.size===a.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${a.shape}, weights shape: ${r.shape}.`);let s={x:a,weights:r},i={size:n};return O.runKernel(cu,s,i)}var Uv=L({bincount_:HP});function qP(e,t){let n=_(e,"x","bitwiseAnd"),a=_(t,"y","bitwiseAnd");if(!Mr(n.shape,a.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${n.shape}, y: 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Has rank ${n.rank}`);if(a.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). 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setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}},Pw=class extends Or{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=O.registeredVariables[t];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:P(()=>xn(a.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;P(()=>{let i=X(s,ut(r));s.assign(i);let o=X(z(he(r,mn(X(i,O.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&_e(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)}},Ow=class extends Or{static get className(){return"Adam"}constructor(e,t,n,a=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],P(()=>{this.accBeta1=ve(t).variable(),this.accBeta2=ve(n).variable()}),a==null&&(this.epsilon=O.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);P(()=>{let n=pe(1,this.accBeta1),a=pe(1,this.accBeta2);t.forEach((r,s)=>{let i=O.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:P(()=>qe(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:P(()=>qe(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedSecondMoment[s].variable,d=X(z(u,this.beta1),z(l,1-this.beta1)),c=X(z(p,this.beta2),z(ut(l),1-this.beta2)),h=he(d,n),m=he(c,a);u.assign(d),p.assign(c);let f=X(z(he(h,X(mn(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(z(this.accBeta1,this.beta1)),this.accBeta2.assign(z(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&_e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&_e(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),P(()=>{this.accBeta1.assign(Rr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Rr(this.beta2,this.iterations_+1))});let 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)}},Lw=class extends Or{static get className(){return"Adamax"}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=[],P(()=>{this.iteration=ve(0).variable(),this.accBeta1=ve(t).variable()}),a==null&&(this.epsilon=O.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);P(()=>{let n=pe(1,this.accBeta1),a=he(-this.learningRate,X(z(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=O.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:qe(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:qe(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedWeightedInfNorm[s].variable,d=X(z(u,this.beta1),z(l,1-this.beta1)),c=z(p,this.beta2),h=zt(l),m=mr(c,h);u.assign(d),p.assign(m);let f=X(z(he(a,n),he(d,X(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(X(this.iteration,1)),this.accBeta1.assign(z(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&_e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&_e(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)}},_f=class extends Or{static get className(){return"SGD"}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=O.registeredVariables[t];P(()=>{let s=X(z(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(ve(-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)}},zw=class extends _f{static get className(){return"Momentum"}constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=ve(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=O.registeredVariables[t];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${t}/momentum`,variable:P(()=>qe(a).variable(!1))});let r=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&P(()=>{let i,o=X(z(this.m,r),s);this.useNesterov?i=X(z(this.c,X(s,z(o,this.m))),a):i=X(z(this.c,o),a),r.assign(o),a.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&_e(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}},Ww=class extends Or{static get className(){return"RMSProp"}constructor(e,t=.9,n=0,a=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=O.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=O.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:P(()=>qe(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:P(()=>qe(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:P(()=>qe(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;P(()=>{let l=X(z(i,this.decay),z(ut(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[n].variable,p=X(z(u,this.decay),z(s,1-this.decay)),d=he(z(s,this.learningRate),mn(pe(l,X(ut(p),this.epsilon)))),c=X(z(o,this.momentum),d);i.assign(l),u.assign(p),o.assign(c);let h=pe(a,c);a.assign(h)}else{let u=X(z(i,this.decay),z(ut(s),1-this.decay)),p=X(z(o,this.momentum),he(z(s,this.learningRate),mn(X(u,this.epsilon))));i.assign(u),o.assign(p);let d=pe(a,p);a.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&_e(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&_e(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&_e(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)}},NW=[Mw,Pw,Ow,Lw,zw,Ww,_f];function TW(){for(let e of NW)ET(e)}var 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browser.");e.startsWith(ql.URL_SCHEME)&&(e=e.slice(ql.URL_SCHEME.length)),(e==null||e.length===0)&&(e=CW),this.modelJsonFileName=e+_W,this.weightDataFileName=e+EW}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],a=sN(e,n),r=window.URL.createObjectURL(new Blob([JSON.stringify(a)],{type:"application/json"})),s=this.modelJsonAnchor==null?document.createElement("a"):this.modelJsonAnchor;if(s.download=this.modelJsonFileName,s.href=r,await iI(()=>s.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){let 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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}}};Xl.className="Sequential";ne.registerClass(Xl);function tH(e){return new $r(e)}function nH(e){return new Xl(e)}function D2(e){return d2(e)}function aH(e,t){_a.registerCallbackConstructor(e,t)}var Gn=class extends ne.Serializable{getConfig(){return{}}},R2=class extends Gn{apply(e,t=1){return CU(e,t)}};R2.className="elu";ne.registerClass(R2);var M2=class extends Gn{apply(e){return hf(e)}};M2.className="selu";ne.registerClass(M2);var P2=class extends Gn{apply(e){return Ke(e)}};P2.className="relu";ne.registerClass(P2);var O2=class extends Gn{apply(e){return P(()=>hs(6,Ke(e)))}};O2.className="relu6";ne.registerClass(O2);var L2=class extends Gn{apply(e){return e}};L2.className="linear";ne.registerClass(L2);var z2=class extends Gn{apply(e){return ma(e)}};z2.className="sigmoid";ne.registerClass(z2);var W2=class extends Gn{apply(e){return EU(e)}};W2.className="hardSigmoid";ne.registerClass(W2);var B2=class extends Gn{apply(e){return Lo(e)}};B2.className="softplus";ne.registerClass(B2);var V2=class extends Gn{apply(e){return _U(e)}};V2.className="softsign";ne.registerClass(V2);var U2=class extends Gn{apply(e){return ci(e)}};U2.className="tanh";ne.registerClass(U2);var h0=class extends Gn{apply(e,t=-1){return Xa(e,t)}};h0.className="softmax";ne.registerClass(h0);var G2=class extends Gn{apply(e,t=-1){return rf(e,t)}};G2.className="logSoftmax";ne.registerClass(G2);var H2=class extends Gn{apply(e,t=1){return P(()=>z(ma(z(e,t)),e))}};H2.className="swish";ne.registerClass(H2);var q2=class extends Gn{apply(e){return P(()=>z(e,ci(Lo(e))))}};q2.className="mish";ne.registerClass(q2);function fs(e){return e.getClassName()}function dx(e,t={}){return bd(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function gs(e){if(e==null){let t={};return t.className="linear",t.config={},dx(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},dx(t)}else return e instanceof Gn?e:dx(e)}function m0(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var j2=class extends ne.Serializable{},kd=class extends j2{constructor(e){super(),m0(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 P(()=>{let t=Nt([1]);return this.hasL1&&(t=X(t,fe(z(this.l1,zt(e))))),this.hasL2&&(t=X(t,fe(z(this.l2,xd(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};kd.className="L1L2";ne.registerClass(kd);function rH(e){return m0(e),new kd({l1:e!=null?e.l1:null,l2:0})}function sH(e){return m0(e),new kd({l2:e!=null?e.l2:null,l1:0})}var DI={l1l2:"L1L2"};function mt(e){return qw(e)}function RI(e,t={}){return bd(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ct(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in DI?DI[e]:e,config:{}};return RI(t)}else return e instanceof j2?e:RI(e)}var f0=class extends Ue{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ce(e);let n=Ke(e);return this.maxValue!=null&&(n=nn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};f0.className="ReLU";ne.registerClass(f0);var g0=class extends Ue{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 n=Ce(e);return od(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};g0.className="LeakyReLU";ne.registerClass(g0);var b0=class extends Ue{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Tt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ct(e.alphaRegularizer),this.alphaConstraint=Yt(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 V(`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 a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a(Pt(t),t==="channelsFirst"?De(e,[0,2,3,1]):e))}function K2(e,t){return P(()=>(Pt(t),t==="channelsFirst"?De(e,[0,2,3,4,1]):e))}function iH(e,t,n,a=1,r="valid",s,i=1){return P(()=>{if(s==null&&(s=ja()),Pt(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=De(e,[0,2,1])),r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Jm(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ya(o,n)),o})}function MI(e,t,n,a=[1,1],r="valid",s,i,o=null){return P(()=>{if(s==null&&(s=ja()),Pt(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=w0(e,s);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Hl.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=De(l,[0,3,1,2])),l})}function oH(e,t,n,a=[1,1,1],r="valid",s,i){return P(()=>{if(s==null&&(s=ja()),Pt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=K2(e,s);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Xv(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ya(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var k0=class extends Ue{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",k0.verifyArgs(t),this.rank=e,en(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Le(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Rl(t.kernelSize,e,"kernelSize"),this.strides=Rl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,va(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Pt(this.dataFormat),this.activation=gs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Tt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(t.biasConstraint),this.biasRegularizer=Ct(t.biasRegularizer),this.activityRegularizer=Ct(t.activityRegularizer),this.dilationRate=Rl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`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 V(`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 V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(rr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!jw(e.kernelSize,"number",1,3))throw new V(`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:fs(this.activation),useBias:this.useBias,biasInitializer:At(this.biasInitializer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Id=class extends k0{constructor(e,t){super(e,t),this.kernel=null,Id.verifyArgs(t),this.filters=t.filters,en(this.filters,"filters"),this.kernelInitializer=Tt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(t.kernelConstraint),this.kernelRegularizer=Ct(t.kernelRegularizer)}build(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`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 P(()=>{e=Ce(e);let n,a=this.bias==null?null:this.bias.read(),r=n2(this.activation.getClassName());if(r!=null&&this.rank===2)n=MI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=iH(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=MI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=oH(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Le("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=Qe(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},Sd=class extends Id{constructor(e){super(2,e),Sd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!jw(e.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Sd.className="Conv2D";ne.registerClass(Sd);var Nd=class extends Id{constructor(e){super(3,e),Nd.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 V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Nd.className="Conv3D";ne.registerClass(Nd);var I0=class extends Sd{constructor(e){if(super(e),this.inputSpec=[new Wt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Qe(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Wt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ce(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],u=this.kernelSize[0],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=sr(o,d,u,this.padding),m=sr(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,1]));let g=Qm(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=De(g,[0,3,1,2])),this.bias!=null&&(g=Ya(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=Qe(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]=sr(t[a],o,s,this.padding),t[r]=sr(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};I0.className="Conv2DTranspose";ne.registerClass(I0);var S0=class extends Nd{constructor(e){if(super(e),this.inputSpec=[new Wt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Qe(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Wt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ce(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],b=sr(l,m,d,this.padding),y=sr(u,f,c,this.padding),x=sr(p,g,h,this.padding),w=[r,b,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,4,1]));let I=Yv(n,this.kernel.read(),w,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=De(I,[0,4,1,2,3])),this.bias!==null&&(I=Ya(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=Qe(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=sr(t[a],u,i,this.padding),t[r]=sr(t[r],p,o,this.padding),t[s]=sr(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};S0.className="Conv3DTranspose";ne.registerClass(S0);var X2=class extends Id{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("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 V(`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=Tt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ct(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=Tt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ct(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Qe(e),e.length{e=Ce(e);let n;if(this.rank===1)throw new Le("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=De(e,[0,2,3,1])),n=As(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=De(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=At(this.depthwiseInitializer),e.pointwiseInitializer=At(this.pointwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.pointwiseRegularizer=mt(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};X2.className="SeparableConv";var N0=class extends X2{constructor(e){super(2,e)}};N0.className="SeparableConv2D";ne.registerClass(N0);var Bf=class extends Id{constructor(e){super(1,e),Bf.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"&&!jw(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Bf.className="Conv1D";ne.registerClass(Bf);var T0=class extends Ue{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 P(()=>{if(e=Ce(e),this.dataFormat==="channelsLast"){let n=Dh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Dh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Dh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Dh(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}};T0.className="Cropping2D";ne.registerClass(T0);var C0=class extends Ue{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,Pt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,vU(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 P(()=>{let n=Ce(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=De(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Fa.resizeNearestNeighbor(n,[r,s]):Fa.resizeBilinear(n,[r,s]);return De(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Fa.resizeNearestNeighbor(n,[r,s]):Fa.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};C0.className="UpSampling2D";ne.registerClass(C0);function lH(e,t,n=[1,1],a="valid",r,s){return P(()=>{r==null&&(r=ja()),Pt(r);let i=w0(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=_s(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}var _0=class extends k0{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Tt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Ct(e.depthwiseRegularizer)}build(e){if(e=Qe(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 P(()=>{e=Ce(e);let n=lH(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Qe(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=Ha(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ha(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=At(this.depthwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};_0.className="DepthwiseConv2D";ne.registerClass(_0);function Y2(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("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 Z2(e,t,n,a=!1,r,s,i=!1,o=!1){return P(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(qa(2,l));if(t=De(t,u),s!=null)throw new Le("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=ie(ie(r,"bool"),"float32"),r.rank===l-1&&(r=Qt(r,-1)),r=De(r,u)),a&&(t=ba(t,0),r!=null&&(r=ba(r,0)));let p=[],d,c=n,h=t.shape[0],m=ct(t),f;r!=null&&(f=ct(r));for(let b=0;be(y,c));if(r==null)d=x[0],c=x[1];else{let w=P(()=>{let I=f[b],T=pe(na(I),I),C=X(z(x[0],I),z(c[0],T)),E=c.map((F,D)=>X(z(x[1][D],I),z(F,T)));return{output:C,newStates:E}});d=w.output,c=w.newStates}o&&p.push(d)}let g;return o&&(g=Dt(p,1)),[d,g,c]})}var gr=class extends Ue{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Gf({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("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 Wt({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 qa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Px(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 P(()=>{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;ns.shape[s.shape.length-1]),r))throw new V(`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=r.map(s=>new Wt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new Tr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new V("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=>Nt([n,a])):this.states_=[Nt([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>Nt([n,a])):this.states_[0]=Nt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let a=0;aHt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=Y2(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 Wt({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 Va){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let p=super.apply(o,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return P(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Ce(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 V(`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=Z2((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],p=o[2];this.stateful&&this.resetStates(p,a);let d=this.returnSequences?u:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return P(()=>{let t=Nt(e.shape);return t=fe(t,[1,2]),t=yd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Rx(t,[1,n]):t):this.cell.stateSize>1?[Rx(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()===gr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),e),t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ga(a,n);return new e(Object.assign(t,{cell:r}))}};gr.className="RNN";ne.registerClass(gr);var Td=class extends Ue{},Vf=class extends Td{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,en(this.units,"units"),this.activation=gs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=jl([1,ms([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=jl([1,ms([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,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 P(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0na(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0na(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=ur(z(e,s),this.kernel.read()):r=ur(e,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,ur(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:fs(this.activation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),recurrentInitializer:At(this.recurrentInitializer),biasInitializer:At(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};Vf.className="SimpleRNNCell";ne.registerClass(Vf);var E0=class extends gr{constructor(e){e.cell=new Vf(e),super(e)}call(e,t){return P(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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)}};E0.className="SimpleRNN";ne.registerClass(E0);var Uf=class extends Td{constructor(e){if(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",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,en(this.units,"units"),this.activation=gs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=gs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=jl([1,ms([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=jl([1,ms([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,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 P(()=>{if(e=e,e.length!==2)throw new V(`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],0na(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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)}};A0.className="GRU";ne.registerClass(A0);var Cd=class extends Td{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,en(this.units,"units"),this.activation=gs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=gs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=jl([1,ms([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=jl([1,ms([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Qe(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 Pa{apply(i,o){let l=r.apply([s]),u=new Ff().apply([s]),p=r.apply([s*2]);return xI(xI(l,u),p)}},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 P(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0na(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};F0.className="LSTM";ne.registerClass(F0);var Gf=class extends Td{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return P(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{ni(`RNNCell_${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(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ga(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 Ox(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):u2(t(),n),o=()=>vd(i,t,a);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var uH=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{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("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 P(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=Nt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new Tr("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 V("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(()=>Nt(r)):this.states_=[Nt(r)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Nt(r)):this.states_[0]=Nt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;sHt(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],u=e[o?4:3],p=Ha(l,a[0],r,s[0],i[0]),d=Ha(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,p,d]:[p,d,n]]}};J2.className="ConvRNN2D";var Hf=class extends Cd{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,en(this.filters,"filters"),this.kernelSize=Rl(n,2,"kernelSize"),this.kernelSize.forEach(o=>en(o,"kernelSize")),this.strides=Rl(a||1,2,"strides"),this.strides.forEach(o=>en(o,"strides")),this.padding=r||"valid",va(this.padding),this.dataFormat=s||"channelsLast",Pt(this.dataFormat),this.dilationRate=Rl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>en(o,"dilationRate"))}build(e){var t;e=Qe(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`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,u=this.filters;o=new(t=class extends Pa{apply(p,d){let c=l.apply([u]),h=Qn([u]),m=l.apply([u*2]);return Kw([c,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 P(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0na(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0na(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,w,I,T]=zn(this.kernel.read(),i,y),[C,E,F,D]=this.useBias?zn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,w,E,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,T,D,this.padding);let[$,S,M,B]=zn(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),b=this.recurrentConv(b,B);let U=this.recurrentActivation.apply(X(u,m)),H=this.recurrentActivation.apply(X(p,f)),j=X(z(H,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,b)),this.activation.apply(j));return[K,K,j]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=uH(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=Rt(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ya(r,n,this.dataFormat):r}recurrentConv(e,t){return Rt(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Hf.className="ConvLSTM2DCell";ne.registerClass(Hf);var $0=class extends J2{constructor(e){let t=new Hf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};$0.className="ConvLSTM2D";ne.registerClass($0);var qf=class extends Ue{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.invokeCallHook(e,t);let n=Ce(e);if(0u2(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()}};qf.className="Dropout";ne.registerClass(qf);var D0=class extends qf{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};D0.className="SpatialDropout1D";ne.registerClass(D0);var R0=class extends Ue{constructor(e){if(super(e),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,en(this.units,"units"),this.activation=gs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Ct(e.kernelRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.activityRegularizer=Ct(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 P(()=>{this.invokeCallHook(e,t);let n=Ce(e),a=n2(this.activation.getClassName()),r;return a!=null?r=ur(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=ur(n,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:fs(this.activation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),biasInitializer:At(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};R0.className="Dense";ne.registerClass(R0);var M0=class extends Ue{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 V(`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],ls(e,1)]}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ce(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r{this.invokeCallHook(e,t);let n=Ce(e);return this.activation.apply(n)})}getConfig(){let e={activation:fs(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};P0.className="Activation";ne.registerClass(P0);var O0=class extends Ue{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 P(()=>(e=Ce(e),SU(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};O0.className="RepeatVector";ne.registerClass(O0);var L0=class extends Ue{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t{this.invokeCallHook(e,t);let n=Ce(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return W(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};L0.className="Reshape";ne.registerClass(L0);var z0=class extends Ue{constructor(e){if(super(e),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=qa(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Wt({ndim:this.dims.length+1})]}computeOutputShape(e){e=Qe(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return De(Ce(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};z0.className="Permute";ne.registerClass(z0);var W0=class extends Ue{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=Ce(e),a=-1;return xc(hi(n,this.maskValue),a)}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ce(e),a=-1,r=!0,s=xc(hi(n,this.maskValue),a,r);return z(n,ie(s,n.dtype))})}};W0.className="Masking";ne.registerClass(W0);var B0=class extends Ue{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(kt(e.inputLength))}this.inputDim=e.inputDim,en(this.inputDim,"inputDim"),this.outputDim=e.outputDim,en(this.outputDim,"outputDim"),this.embeddingsInitializer=Tt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ct(e.embeddingsRegularizer),this.activityRegularizer=Ct(e.activityRegularizer),this.embeddingsConstraint=Yt(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 P(()=>this.maskZero?(e=Ce(e),hi(e,qe(e))):null)}computeOutputShape(e){if(e=Qe(e),this.inputLength==null)return[...e,this.outputDim];let t=kt(this.inputLength);if(t.length!==e.length-1)throw new V(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a{this.invokeCallHook(e,t);let n=Ce(e);n.dtype!=="int32"&&(n=lr(n,"int32"));let a=l2(this.embeddings.read(),W(n,[n.size]));return W(a,Qe(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:At(this.embeddingsInitializer),embeddingsRegularizer:mt(this.embeddingsRegularizer),activityRegularizer:mt(this.activityRegularizer),embeddingsConstraint:Xt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};B0.className="Embedding";ne.registerClass(B0);var Go=class extends Ue{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Le}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length1)throw new V(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;rr.length);e.indexOf(null)===-1&&os(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return P(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=ms(a);for(let s of e){let i=s.rank;for(let o=0;o1){let u=qa(1,l).concat([0]);n.push(De(o,u)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,u=o[l-1],p=[u].concat(o.slice(0,o.length-1));s=W(De(W(s,[-1,u]),[1,0]),p)}else if(i>1){let o=[i-1].concat(qa(0,i-1));s=De(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a{if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an Array");if(!Array.isArray(e))throw new V("`inputs` should be an Array");if(t.length!==e.length)throw new V(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:Qt(a,0));let n=t[0];for(let a=1;a{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new V("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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Ue{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 P(()=>{this.invokeCallHook(e,t);let n=Ce(e);return vd(()=>X(Af(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};X0.className="GaussianNoise";ne.registerClass(X0);var Y0=class extends Ue{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 P(()=>{this.invokeCallHook(e,t);let n=Ce(e);return this.rate>0&&this.rate<1?vd(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return z(n,Af(n.shape,1,a))},()=>n,t.training||!1):n})}};Y0.className="GaussianDropout";ne.registerClass(Y0);var Z0=class extends Ue{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:At(this.betaInitializer),gammaInitializer:At(this.gammaInitializer),movingMeanInitializer:At(this.movingMeanInitializer),movingVarianceInitializer:At(this.movingVarianceInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer),betaConstraint:Xt(this.betaConstraint),gammaConstraint:Xt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};J0.className="BatchNormalization";ne.registerClass(J0);var Q0=class extends Ue{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 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n=Ce(e),a=n.shape,r=a.length;return P(()=>{let{mean:s,variance:i}=pd(n,this.axis,!0),o=fi(1,r);for(let h of this.axis)o[h]=a[h];let l=h=>h!=null&&h.shape.length!==r?W(h,o):h,u=this.scale?l(this.gamma.read()):null,p=this.center?l(this.beta.read()):null,d=[],c=[];for(let h=0;h{if(e.rank!==4)throw new V(`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 V("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ja()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. 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s==="max"?i=Mt(e,t,n,o):i=ya(e,t,n,o),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}function Q2(e,t,n,a,r,s){return P(()=>{Pt(r),r2(s),va(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=ja()),s==null&&(s="max"),e=K2(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=dw(e,t,n,o):i=Lv(e,t,n,o),r==="channelsFirst"&&(i=De(i,[0,4,1,2,3])),i})}var eC=class extends Ue{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 V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(en(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 V(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,va(this.padding),this.inputSpec=[new Wt({ndim:3})]}computeOutputShape(e){e=Qe(e);let t=Ha(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return P(()=>{this.invokeCallHook(e,t),e=yd(Ce(e),2);let n=this.poolingFunction(Ce(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Fs(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},t1=class extends eC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),jf(e,t,n,a,r,"max")}};t1.className="MaxPooling1D";ne.registerClass(t1);var n1=class extends eC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),jf(e,t,n,a,r,"avg")}};n1.className="AveragePooling1D";ne.registerClass(n1);var tC=class extends Ue{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 V(`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];en(this.poolSize,"poolSize"),en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),va(this.padding),this.inputSpec=[new Wt({ndim:4})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(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 P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ce(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}},a1=class extends tC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),jf(e,t,n,a,r,"max")}};a1.className="MaxPooling2D";ne.registerClass(a1);var r1=class extends tC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),jf(e,t,n,a,r,"avg")}};r1.className="AveragePooling2D";ne.registerClass(r1);var nC=class extends Ue{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 V(`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];en(this.poolSize,"poolSize"),en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),va(this.padding),this.inputSpec=[new Wt({ndim:5})]}computeOutputShape(e){e=Qe(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=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(n,this.poolSize[1],this.padding,this.strides[1]),a=Ha(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 P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ce(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}},s1=class extends nC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),Q2(e,t,n,a,r,"max")}};s1.className="MaxPooling3D";ne.registerClass(s1);var i1=class extends nC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),Q2(e,t,n,a,r,"avg")}};i1.className="AveragePooling3D";ne.registerClass(i1);var aC=class extends Ue{constructor(e){super(e),this.inputSpec=[new Wt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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Add some layers first.");this.model=new $r({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new Ba("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Ba("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 Ba("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 Ba("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 V("Legacy serialization format not supported yet.");r=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Yl))throw new Le(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Ga(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new V("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 V("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}}};Yl.className="Sequential";ne.registerClass(Yl);function bH(e){return new $r(e)}function yH(e){return new Yl(e)}function B2(e){return v2(e)}function xH(e,t){Ea.registerCallbackConstructor(e,t)}var Gn=class extends ne.Serializable{getConfig(){return{}}},V2=class extends Gn{apply(e,t=1){return UU(e,t)}};V2.className="elu";ne.registerClass(V2);var U2=class extends Gn{apply(e){return mf(e)}};U2.className="selu";ne.registerClass(U2);var G2=class extends Gn{apply(e){return Ke(e)}};G2.className="relu";ne.registerClass(G2);var H2=class extends Gn{apply(e){return P(()=>ms(6,Ke(e)))}};H2.className="relu6";ne.registerClass(H2);var q2=class extends Gn{apply(e){return e}};q2.className="linear";ne.registerClass(q2);var j2=class extends Gn{apply(e){return fa(e)}};j2.className="sigmoid";ne.registerClass(j2);var K2=class extends 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bd(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ct(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in MI?MI[e]:e,config:{}};return PI(t)}else return e instanceof tC?e:PI(e)}var g0=class extends Be{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Te(e);let n=Ke(e);return this.maxValue!=null&&(n=rn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};g0.className="ReLU";ne.registerClass(g0);var b0=class extends Be{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 n=Te(e);return od(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};b0.className="LeakyReLU";ne.registerClass(b0);var y0=class extends Be{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Tt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ct(e.alphaRegularizer),this.alphaConstraint=Yt(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 V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Ze(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a(Pt(t),t==="channelsFirst"?De(e,[0,2,3,1]):e))}function nC(e,t){return P(()=>(Pt(t),t==="channelsFirst"?De(e,[0,2,3,4,1]):e))}function kH(e,t,n,a=1,r="valid",s,i=1){return P(()=>{if(s==null&&(s=ja()),Pt(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=De(e,[0,2,1])),r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Qm(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ya(o,n)),o})}function OI(e,t,n,a=[1,1],r="valid",s,i,o=null){return P(()=>{if(s==null&&(s=ja()),Pt(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=k0(e,s);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=ql.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=De(l,[0,3,1,2])),l})}function IH(e,t,n,a=[1,1,1],r="valid",s,i){return P(()=>{if(s==null&&(s=ja()),Pt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=nC(e,s);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Yv(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ya(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var I0=class extends Be{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",I0.verifyArgs(t),this.rank=e,nn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Le(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Ml(t.kernelSize,e,"kernelSize"),this.strides=Ml(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,wa(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Pt(this.dataFormat),this.activation=bs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Tt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(t.biasConstraint),this.biasRegularizer=Ct(t.biasRegularizer),this.activityRegularizer=Ct(t.activityRegularizer),this.dilationRate=Ml(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`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 V(`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 V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(rr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Kw(e.kernelSize,"number",1,3))throw new V(`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:gs(this.activation),useBias:this.useBias,biasInitializer:At(this.biasInitializer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Id=class extends I0{constructor(e,t){super(e,t),this.kernel=null,Id.verifyArgs(t),this.filters=t.filters,nn(this.filters,"filters"),this.kernelInitializer=Tt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(t.kernelConstraint),this.kernelRegularizer=Ct(t.kernelRegularizer)}build(e){e=Ze(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`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 P(()=>{e=Te(e);let n,a=this.bias==null?null:this.bias.read(),r=p2(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=kH(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=IH(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Le("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=Ze(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},Sd=class extends Id{constructor(e){super(2,e),Sd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Kw(e.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Sd.className="Conv2D";ne.registerClass(Sd);var Nd=class extends Id{constructor(e){super(3,e),Nd.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 V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Nd.className="Conv3D";ne.registerClass(Nd);var S0=class extends Sd{constructor(e){if(super(e),this.inputSpec=[new Bt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Ze(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Te(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],u=this.kernelSize[0],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=sr(o,d,u,this.padding),m=sr(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,1]));let g=ef(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=De(g,[0,3,1,2])),this.bias!=null&&(g=Ya(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=Ze(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]=sr(t[a],o,s,this.padding),t[r]=sr(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};S0.className="Conv2DTranspose";ne.registerClass(S0);var N0=class extends Nd{constructor(e){if(super(e),this.inputSpec=[new Bt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Ze(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Te(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],b=sr(l,m,d,this.padding),y=sr(u,f,c,this.padding),x=sr(p,g,h,this.padding),w=[r,b,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,4,1]));let I=Zv(n,this.kernel.read(),w,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=De(I,[0,4,1,2,3])),this.bias!==null&&(I=Ya(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=Ze(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=sr(t[a],u,i,this.padding),t[r]=sr(t[r],p,o,this.padding),t[s]=sr(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};N0.className="Conv3DTranspose";ne.registerClass(N0);var aC=class extends Id{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("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 V(`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=Tt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ct(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=Tt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ct(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Ze(e),e.length{e=Te(e);let n;if(this.rank===1)throw new Le("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=De(e,[0,2,3,1])),n=Fs(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=De(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=At(this.depthwiseInitializer),e.pointwiseInitializer=At(this.pointwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.pointwiseRegularizer=mt(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};aC.className="SeparableConv";var T0=class extends aC{constructor(e){super(2,e)}};T0.className="SeparableConv2D";ne.registerClass(T0);var Vf=class extends Id{constructor(e){super(1,e),Vf.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"&&!Kw(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Vf.className="Conv1D";ne.registerClass(Vf);var C0=class extends Be{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 P(()=>{if(e=Te(e),this.dataFormat==="channelsLast"){let n=Dh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Dh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Dh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Dh(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}};C0.className="Cropping2D";ne.registerClass(C0);var _0=class extends Be{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,Pt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,PU(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 P(()=>{let n=Te(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=De(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?ea.resizeNearestNeighbor(n,[r,s]):ea.resizeBilinear(n,[r,s]);return De(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?ea.resizeNearestNeighbor(n,[r,s]):ea.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};_0.className="UpSampling2D";ne.registerClass(_0);function SH(e,t,n=[1,1],a="valid",r,s){return P(()=>{r==null&&(r=ja()),Pt(r);let i=k0(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=_s(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}var E0=class extends I0{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Tt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Ct(e.depthwiseRegularizer)}build(e){if(e=Ze(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 P(()=>{e=Te(e);let n=SH(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Ze(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=Ha(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ha(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=At(this.depthwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};E0.className="DepthwiseConv2D";ne.registerClass(E0);function rC(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("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 sC(e,t,n,a=!1,r,s,i=!1,o=!1){return P(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(qa(2,l));if(t=De(t,u),s!=null)throw new Le("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=ie(ie(r,"bool"),"float32"),r.rank===l-1&&(r=tn(r,-1)),r=De(r,u)),a&&(t=ya(t,0),r!=null&&(r=ya(r,0)));let p=[],d,c=n,h=t.shape[0],m=ct(t),f;r!=null&&(f=ct(r));for(let b=0;be(y,c));if(r==null)d=x[0],c=x[1];else{let w=P(()=>{let I=f[b],T=pe(aa(I),I),C=X(z(x[0],I),z(c[0],T)),E=c.map((F,D)=>X(z(x[1][D],I),z(F,T)));return{output:C,newStates:E}});d=w.output,c=w.newStates}o&&p.push(d)}let g;return o&&(g=Dt(p,1)),[d,g,c]})}var gr=class extends Be{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Hf({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("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 Bt({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 qa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Ox(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 P(()=>{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;ns.shape[s.shape.length-1]),r))throw new V(`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=r.map(s=>new Bt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new Tr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new V("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=>Nt([n,a])):this.states_=[Nt([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>Nt([n,a])):this.states_[0]=Nt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let a=0;aHt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=rC(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 Bt({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 Va){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let p=super.apply(o,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return P(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Te(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 V(`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=sC((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],p=o[2];this.stateful&&this.resetStates(p,a);let d=this.returnSequences?u:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return P(()=>{let t=Nt(e.shape);return t=fe(t,[1,2]),t=yd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Mx(t,[1,n]):t):this.cell.stateSize>1?[Mx(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()===gr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),e),t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ga(a,n);return new e(Object.assign(t,{cell:r}))}};gr.className="RNN";ne.registerClass(gr);var Td=class extends Be{},Uf=class extends Td{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,nn(this.units,"units"),this.activation=bs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Kl([1,fs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Kl([1,fs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Ze(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 P(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0aa(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0aa(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=ur(z(e,s),this.kernel.read()):r=ur(e,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,ur(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:gs(this.activation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),recurrentInitializer:At(this.recurrentInitializer),biasInitializer:At(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};Uf.className="SimpleRNNCell";ne.registerClass(Uf);var A0=class extends gr{constructor(e){e.cell=new Uf(e),super(e)}call(e,t){return P(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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)}};A0.className="SimpleRNN";ne.registerClass(A0);var Gf=class extends Td{constructor(e){if(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",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,nn(this.units,"units"),this.activation=bs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=bs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Kl([1,fs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Kl([1,fs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Ze(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 P(()=>{if(e=e,e.length!==2)throw new V(`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],0aa(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0aa(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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)}};F0.className="GRU";ne.registerClass(F0);var Cd=class extends Td{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,nn(this.units,"units"),this.activation=bs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=bs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Kl([1,fs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Kl([1,fs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Ze(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 Pa{apply(i,o){let l=r.apply([s]),u=new $f().apply([s]),p=r.apply([s*2]);return wI(wI(l,u),p)}},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 P(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0aa(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0aa(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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)}};$0.className="LSTM";ne.registerClass($0);var Hf=class extends Td{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return P(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{ri(`RNNCell_${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(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ga(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 Lx(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):b2(t(),n),o=()=>vd(i,t,a);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var NH=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{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("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 P(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=Nt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new Tr("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 V("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(()=>Nt(r)):this.states_=[Nt(r)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Nt(r)):this.states_[0]=Nt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;sHt(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],u=e[o?4:3],p=Ha(l,a[0],r,s[0],i[0]),d=Ha(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,p,d]:[p,d,n]]}};iC.className="ConvRNN2D";var qf=class extends Cd{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,nn(this.filters,"filters"),this.kernelSize=Ml(n,2,"kernelSize"),this.kernelSize.forEach(o=>nn(o,"kernelSize")),this.strides=Ml(a||1,2,"strides"),this.strides.forEach(o=>nn(o,"strides")),this.padding=r||"valid",wa(this.padding),this.dataFormat=s||"channelsLast",Pt(this.dataFormat),this.dilationRate=Ml(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>nn(o,"dilationRate"))}build(e){var t;e=Ze(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`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,u=this.filters;o=new(t=class extends Pa{apply(p,d){let c=l.apply([u]),h=Qn([u]),m=l.apply([u*2]);return Xw([c,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 P(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0aa(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0aa(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,w,I,T]=zn(this.kernel.read(),i,y),[C,E,F,D]=this.useBias?zn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,w,E,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,T,D,this.padding);let[$,S,M,B]=zn(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),b=this.recurrentConv(b,B);let U=this.recurrentActivation.apply(X(u,m)),H=this.recurrentActivation.apply(X(p,f)),j=X(z(H,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,b)),this.activation.apply(j));return[K,K,j]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=NH(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=Rt(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ya(r,n,this.dataFormat):r}recurrentConv(e,t){return Rt(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};qf.className="ConvLSTM2DCell";ne.registerClass(qf);var D0=class extends iC{constructor(e){let t=new qf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};D0.className="ConvLSTM2D";ne.registerClass(D0);var jf=class extends Be{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.invokeCallHook(e,t);let n=Te(e);if(0b2(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()}};jf.className="Dropout";ne.registerClass(jf);var R0=class extends jf{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};R0.className="SpatialDropout1D";ne.registerClass(R0);var M0=class extends Be{constructor(e){if(super(e),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,nn(this.units,"units"),this.activation=bs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Ct(e.kernelRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.activityRegularizer=Ct(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Ze(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=Ze(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Te(e),a=p2(this.activation.getClassName()),r;return a!=null?r=ur(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=ur(n,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:gs(this.activation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),biasInitializer:At(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};M0.className="Dense";ne.registerClass(M0);var P0=class extends Be{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Ze(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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Be{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 P(()=>{this.invokeCallHook(e,t);let n=Te(e);return vd(()=>X(Ff(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Y0.className="GaussianNoise";ne.registerClass(Y0);var Z0=class extends Be{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 P(()=>{this.invokeCallHook(e,t);let n=Te(e);return this.rate>0&&this.rate<1?vd(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return z(n,Ff(n.shape,1,a))},()=>n,t.training||!1):n})}};Z0.className="GaussianDropout";ne.registerClass(Z0);var J0=class extends Be{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Bt({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 P(()=>{let n=t.training==null?!1:t.training,a=Te(e),r=a.shape,s=r.length,i=qa(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 u=i.slice();u.sort();let p=!v.arraysEqual(u,qa(0,s).slice(0,s-1)),d=()=>{if(p){let 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n=Te(e),a=n.shape,r=a.length;return P(()=>{let{mean:s,variance:i}=pd(n,this.axis,!0),o=bi(1,r);for(let h of this.axis)o[h]=a[h];let l=h=>h!=null&&h.shape.length!==r?W(h,o):h,u=this.scale?l(this.gamma.read()):null,p=this.center?l(this.beta.read()):null,d=[],c=[];for(let h=0;h{if(e.rank!==4)throw new V(`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 V("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ja()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. 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s==="max"?i=Mt(e,t,n,o):i=xa(e,t,n,o),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}function oC(e,t,n,a,r,s){return P(()=>{Pt(r),d2(s),wa(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=ja()),s==null&&(s="max"),e=nC(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=hw(e,t,n,o):i=zv(e,t,n,o),r==="channelsFirst"&&(i=De(i,[0,4,1,2,3])),i})}var lC=class extends Be{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 V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(nn(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 V(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);nn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,wa(this.padding),this.inputSpec=[new Bt({ndim:3})]}computeOutputShape(e){e=Ze(e);let t=Ha(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return P(()=>{this.invokeCallHook(e,t),e=yd(Te(e),2);let n=this.poolingFunction(Te(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return $s(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},n1=class extends lC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),wa(a),Kf(e,t,n,a,r,"max")}};n1.className="MaxPooling1D";ne.registerClass(n1);var a1=class extends lC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),wa(a),Kf(e,t,n,a,r,"avg")}};a1.className="AveragePooling1D";ne.registerClass(a1);var uC=class extends Be{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 V(`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];nn(this.poolSize,"poolSize"),nn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),wa(this.padding),this.inputSpec=[new Bt({ndim:4})]}computeOutputShape(e){e=Ze(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(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 P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(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}},r1=class extends uC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),wa(a),Kf(e,t,n,a,r,"max")}};r1.className="MaxPooling2D";ne.registerClass(r1);var s1=class extends uC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),wa(a),Kf(e,t,n,a,r,"avg")}};s1.className="AveragePooling2D";ne.registerClass(s1);var pC=class extends Be{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 V(`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];nn(this.poolSize,"poolSize"),nn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),wa(this.padding),this.inputSpec=[new Bt({ndim:5})]}computeOutputShape(e){e=Ze(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=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(n,this.poolSize[1],this.padding,this.strides[1]),a=Ha(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 P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(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}},i1=class extends pC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),wa(a),oC(e,t,n,a,r,"max")}};i1.className="MaxPooling3D";ne.registerClass(i1);var o1=class extends pC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),wa(a),oC(e,t,n,a,r,"avg")}};o1.className="AveragePooling3D";ne.registerClass(o1);var cC=class extends Be{constructor(e){super(e),this.inputSpec=[new Bt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Le}},l1=class extends cC{constructor(e){super(e||{})}call(e,t){return P(()=>{let n=Te(e);return Et(n,1)})}};l1.className="GlobalAveragePooling1D";ne.registerClass(l1);var u1=class extends cC{constructor(e){super(e||{})}call(e,t){return P(()=>{let n=Te(e);return ga(n,1)})}};u1.className="GlobalMaxPooling1D";ne.registerClass(u1);var dC=class extends Be{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),this.inputSpec=[new Bt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Le}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},p1=class extends dC{call(e,t){return P(()=>{let n=Te(e);return this.dataFormat==="channelsLast"?Et(n,[1,2]):Et(n,[2,3])})}};p1.className="GlobalAveragePooling2D";ne.registerClass(p1);var c1=class extends dC{call(e,t){return P(()=>{let n=Te(e);return 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e(s)}},d1=class extends hC{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=Ze(e),e.length<3)throw new V(`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=Ze(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 P(()=>(e=Te(e),sC((n,a)=>[Te(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};d1.className="TimeDistributed";ne.registerClass(d1);function FH(e){Go(MU,"BidirectionalMergeMode",e)}var $H="concat",h1=class extends hC{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ga(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Ga(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?$H:e.mergeMode,FH(this.mergeMode),e.weights)throw new Le("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()):On(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=rC(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 V("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(p=>new 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i;return this.mergeMode==="concat"?i=Xw([a,r]):this.mergeMode==="sum"?i=X(a,r):this.mergeMode==="ave"?i=z(.5,X(a,r)):this.mergeMode==="mul"?i=z(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){ri(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),ri(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 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 n=Ga(t.layer);if(delete t.layer,t.numConstants!=null)throw new Le("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};h1.className="Bidirectional";ne.registerClass(h1);var m1=class extends Be{constructor(e){super(e),this.scale=e.scale,e.offset?this.offset=e.offset:this.offset=0}getConfig(){let e={scale:this.scale,offset:this.offset},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return P(()=>(e=Te(e),e.dtype!=="float32"&&(e=lr(e,"float32")),X(z(e,this.scale),this.offset)))}};m1.className="Rescaling";ne.registerClass(m1);var{resizeBilinear:DH,cropAndResize:RH}=ea,f1=class extends Be{constructor(e){super(e),this.height=e.height,this.width=e.width}centerCrop(e,t,n,a,r,s,i,o){return P(()=>{let l,u=!1,p=t/s,d=n/i,c=(a+t)/s,h=(r+n)/i,m=[p,d,c,h],f=[];e.rank===3?(u=!0,l=Dt([e])):l=e;for(let x=0;x{let r=DH(e,[t,n]);return lr(r,a)})}call(e,t){return P(()=>{let n=Te(e),a=n.dtype,r=n.shape,s=r[r.length-3],i=r[r.length-2],o=0;s!==this.height&&(o=Math.floor((s-this.height)/2));let l=0;return i!==this.width&&(l=Math.floor((i-this.width)/2),l===0&&(l=1)),o>=0&&l>=0?this.centerCrop(n,o,l,this.height,this.width,s,i,a):this.upsize(e,this.height,this.width,a)})}getConfig(){let e={height:this.height,width:this.width},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){e=Ze(e);let t=e.length-3,n=e.length-2;return e[t]=this.height,e[n]=this.width,e}};f1.className="CenterCrop";ne.registerClass(f1);function MH(e,t,n,a){let r=Te(e);if(r.dtype!=="int32"&&(r=lr(r,"int32")),t==="int")return r;let s=r.shape;if(r.rank===0&&(r=tn(r,-1)),t==="oneHot"&&r.shape[r.shape.length-1]!==1&&(r=tn(r,-1)),r.rank>2)throw new V(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${r.rank}.`);let i=["multiHot","oneHot"].includes(t),o=r,l;if(typeof a!="undefined"&&t==="count"?l=om(o,a,n,i):l=om(o,[],n,i),t!=="tfIdf")return l;if(a)return z(l,a);throw new V("When outputMode is 'tfIdf', weights must be provided.")}var g1=class extends Be{constructor(e){super(e),this.numTokens=e.numTokens,e.outputMode?this.outputMode=e.outputMode:this.outputMode="multiHot"}getConfig(){let e={numTokens:this.numTokens,outputMode:this.outputMode},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){return e=Ze(e),e==null?[this.numTokens]:this.outputMode==="oneHot"&&e[e.length-1]!==1?(e.push(this.numTokens),e):(e[e.length-1]=this.numTokens,e)}call(e,t){return P(()=>{e=Te(e),e.dtype!=="int32"&&(e=lr(e,"int32"));let n;if(typeof t.countWeights!="undefined"){if(this.outputMode!=="count")throw new V(`countWeights is not used when outputMode !== count. + Received countWeights=${t.countWeights}`);n=Te(t.countWeights)}let a=ga(e),r=Wl(e),s=_n(this.numTokens,a).bufferSync().get(0),i=Pr(r,0).bufferSync().get(0);if(!(s&&i))throw new V(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return MH(e,this.outputMode,this.numTokens,n)})}};g1.className="CategoryEncoding";ne.registerClass(g1);var PH=["bilinear","nearest"],LI=new Set(PH),b1=class extends Be{constructor(e){if(super(e),this.height=e.height,this.width=e.width,e.interpolation)if(LI.has(e.interpolation))this.interpolation=e.interpolation;else throw new V(`Invalid interpolation parameter: ${e.interpolation} is not implemented`);else this.interpolation="bilinear";this.cropToAspectRatio=!!e.cropToAspectRatio}computeOutputShape(e){e=Ze(e);let t=e[2];return[this.height,this.width,t]}getConfig(){let e={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return P(()=>{let n=[this.height,this.width];if(this.interpolation==="bilinear")return ea.resizeBilinear(e,n,!this.cropToAspectRatio);if(this.interpolation==="nearest")return ea.resizeNearestNeighbor(e,n,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...LI]} are supported`)})}};b1.className="Resizing";ne.registerClass(b1);var mC=class{constructor(e){this.seed=e}next(){if(this.seed!==void 0)return this.seed++}};mC.className="RandomSeed";var fC=class extends Be{constructor(e){super(e),this.randomGenerator=new mC(e.seed)}getConfig(){let 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implemented`)}},jq=(e,t,n,a=rn)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=a.sparse.sparseFillEmptyRows(k("indices",e,t,n),k("values",e,t,n),k("denseShape",e,t,n),k("defaultValue",e,t,n));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=a.sparse.sparseReshape(k("inputIndices",e,t,n),k("inputShape",e,t,n),k("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[a.sparse.sparseSegmentMean(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];case"SparseSegmentSum":return[a.sparse.sparseSegmentSum(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Kq=(e,t,n,a=rn)=>{switch(e.op){case"FFT":return[a.fft(k("x",e,t,n))];case"IFFT":return[a.ifft(k("x",e,t,n))];case"RFFT":return[a.rfft(k("x",e,t,n))];case"IRFFT":return[a.irfft(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Xq=(e,t,n,a=rn)=>{switch(e.op){case"StaticRegexReplace":return[a.string.staticRegexReplace(k("input",e,t,n),k("pattern",e,t,n),k("rewrite",e,t,n),k("replaceGlobal",e,t,n))];case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=a.string.stringNGrams(k("data",e,t,n),k("dataSplits",e,t,n),k("separator",e,t,n),k("nGramWidths",e,t,n),k("leftPad",e,t,n),k("rightPad",e,t,n),k("padWidth",e,t,n),k("preserveShortSequences",e,t,n));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=a.string.stringSplit(k("input",e,t,n),k("delimiter",e,t,n),k("skipEmpty",e,t,n));return[r,s,i]}case"StringToHashBucketFast":return[a.string.stringToHashBucketFast(k("input",e,t,n),k("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Yq=(e,t,n,a=rn)=>{switch(e.op){case"Cast":return[a.cast(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[a.expandDims(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[a.squeeze(k("x",e,t,n),r)]}case"Reshape":return[a.reshape(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[a.mirrorPad(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[a.pad(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),s=k("paddings",e,t,n);return[a.spaceToBatchND(k("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),s=k("crops",e,t,n);return[a.batchToSpaceND(k("x",e,t,n),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),s=k("dataFormat",e,t,n).toUpperCase();return[a.depthToSpace(k("x",e,t,n),r,s)]}case"BroadcastTo":return[a.broadcastTo(k("x",e,t,n),k("shape",e,t,n))];case"BroadcastArgs":return[a.broadcastArgs(k("s0",e,t,n),k("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function GI(e,t,n,a,r=P){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>Nq(i,o,l));case"basic_math":return r(()=>Tq(i,o,l));case"control":return $q(i,o,l);case"convolution":return r(()=>Dq(i,o,l));case"creation":return r(()=>Rq(i,o,l));case"dynamic":return Mq(i,o,l);case"evaluation":return r(()=>Pq(i,o,l));case"image":return r(()=>Wq(i,o,l));case"graph":return r(()=>Oq(i,o,l));case"logical":return r(()=>Bq(i,o,l));case"matrices":return r(()=>Vq(i,o,l));case"normalization":return r(()=>Uq(i,o,l));case"ragged":return r(()=>Gq(i,o,l));case"reduction":return r(()=>Hq(i,o,l));case"slice_join":return r(()=>qq(i,o,l));case"sparse":return r(()=>jq(i,o,l));case"spectral":return r(()=>Kq(i,o,l));case"string":return r(()=>Xq(i,o,l));case"transformation":return r(()=>Yq(i,o,l));case"hash_table":return zq(i,o,l,a);case"custom":let u=fC(i.op);if(u&&u.customExecutor)return u.customExecutor(new Sq(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var HI=class{constructor(e={},t={},n={},a={},r){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.parseNodeNameCache=r,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;tt.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 qI(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(c=>Zn(c)[0]));a=a||[];let p=new Set(a.map(c=>Zn(c.name)[0])),d=[...t];for(;d.length>0;){let c=d.pop();if((Al(c)||rj(c)||sj(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&!u.has(c.name)&&!p.has(c.name)){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function Zq(e,t){let{usedNodes:n,inputs:a}=t,r=Object.keys(a).map(g=>Zn(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>n.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(b=>[b.name,b])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),d={};for(let g of u){d[g.name]=d[g.name]||0;for(let b of g.children)i(b)||(d[b.name]=Number.POSITIVE_INFINITY),d[b.name]=(d[b.name]||0)+1}let c=Object.entries(d).filter(([,g])=>g===0).map(([g])=>g),h=[...c];for(;c.length>0;){let g=c.pop(),b=p.get(g);for(let y of b.children.filter(i))--d[y.name]===0&&(h.push(y.name),c.push(y.name))}let m=h.map(g=>p.get(g)),f=Jq(m,l);return Qq(f,l),f}function Jq(e,t){let n=new Map(e.map(s=>[s.name,s])),a=t.map(s=>s.name),r=new Set(a);for(;a.length>0;){let s=a.pop(),i=n.get(s);for(let o of i.children)!n.has(o.name)||r.has(o.name)||(r.add(o.name),a.push(o.name))}return e.filter(s=>r.has(s.name))}var Ph=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function Qq(e,t){let n=new Map(e.map((o,l)=>[o.name,l])),a=new Set(t.map(o=>o.name)),r=o=>a.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!n.has(l.name))throw new Ph(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new Ph(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!n.has(l.name))throw new Ph(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new Ph(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function ej(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>Al(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;oe[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Qx(e.functions[n],this)})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPARATOR)+"--"+a.join(this.SEPARATOR)}compile(e,t){let n=qI(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${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 l=t.map(p=>p.name),u=Object.keys(e);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${u}]. Missing the following inputs: [${a}]`)}let i=Zq(this.graph,n),o=ej(i);return{orderedNodes:i,nodeLiveUntilMap:o}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return Ht(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,n])=>[t,this.cloneTensorList(n)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(d=>this.graph.nodes[Zn(d)[0]]),r=t.map(d=>Zn(d)[0]),s=new Set(r),i=r.map(d=>this.graph.nodes[d]);i.length===0&&(i=this._outputs);let o=this.getCompilationKey(a,i),l=this.compiledMap.get(o);l==null&&(l=this.compile(e,i),this.compiledMap.set(o,l));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let u={},p={};return P(()=>{let d=new HI(this.weightMap,u,p,this.functionExecutorMap,this.parseNodeNameCache),c=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(g=>{let[b,y]=Zn(g,d),x=[];x[y]=e[g],c[b]=x,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(x))});let h=this.getFrozenTensorIds(c),{orderedNodes:m,nodeLiveUntilMap:f}=l;for(let g of m){if(c[g.name])continue;let b=GI(g,c,d,this._resourceManager);if(v.isPromise(b))throw new Error(`The execution of the op '${g.op}' returned a promise. Please use model.executeAsync() instead.`);c[g.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(g,c,d,h,s,f.get(g.name))}return this.parent==null&&d.dispose(h),t.map(g=>dn(g,c,d))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,n,a,r,s,i){if(!(Al(t)||s.has(e))){for(let o of n[e])o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length);for(let o of t.inputs){if(Al(o))continue;let l=zI(o.name,n,a);if(l!=null)for(let u of l){if(!u||u.kept||r.has(u.id))continue;let p=i[u.id];p===1?(u.dispose(),delete i[u.id]):p!=null&&i[u.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,n,a,r,s){if(!(Al(e)||r.has(e.name))&&s!=null)for(let i of s){let o=zI(i,t,n);for(let l of o)!l||l.kept||a.has(l.id)||l.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,n=!1,a={},r={}){this.disposeIntermediateTensors(),n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let s=new HI(this.weightMap,a,r,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,s,t,n),o=t.map(d=>dn(d,i,s)),l=o.map(d=>d.id),u=Object.keys(e).map(d=>e[d].id),p=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach(d=>{d.forEach(c=>{c&&!c.isDisposed&&!p.has(c.id)&&c.dispose()})}),this.parent==null&&s.dispose(p),o}async executeFunctionAsync(e,t,n){let a=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(a,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,a){let r=Object.keys(e),s=r.map(x=>this.graph.nodes[Zn(x)[0]]),i=n.map(x=>Zn(x)[0]),o=new Set(i),l=i.map(x=>this.graph.nodes[x]);l.length===0&&(l=this._outputs);let{usedNodes:u,missingInputs:p,dynamicNode:d,syncInputs:c}=qI(e,l,this.weightMap,this._initNodes),h=[...s,...this.graph.weights,...this._initNodes||[]].map(x=>({node:x,contexts:t.currentContext})),m=Object.assign({},this.weightMap);Object.keys(e).forEach(x=>{let[w,I]=Zn(x),T=[];T[I]=e[x],m[w]=T});let f={},g=this.getFrozenTensorIds(m),b={};for(;h.length>0;){let x=this.processStack(s,h,t,m,b,g,o,f,u);await Promise.all(x)}d==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=l.filter(x=>!Al(x)&&!dn(x.name,m,t)).map(x=>x.name);if(y.length>0){let x="";throw d!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${c}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${p}]. ${x}`)}return m}processStack(e,t,n,a,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();n.currentContext=p.contexts;let d="";if(p.node.op==="Enter"&&k("isConstant",p.node,a,n)&&([d]=_r(p.node.name,n)),a[p.node.name]==null){let c=GI(p.node,a,n,this._resourceManager);d||([d]=_r(p.node.name,n));let h=n.currentContext;v.isPromise(c)?u.push(c.then(m=>(a[d]=m,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(m)),n.currentContext=h,this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l),m))):(a[d]=c,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(c)),this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l))}else this.processChildNodes(p.node,t,n,a,r,l)}return u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=_r(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!dn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!dn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Zn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.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 ${n.dtype}`)})}mapInputs(e){var t,n;let a={};for(let r in e){let s=(n=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||n===void 0?void 0:n[r];s!=null?a[s.name]=e[r]:a[r]=e[r]}return a}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Zn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var n,a;let r=(a=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||a===void 0?void 0:a[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[n]=Zn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},ij=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(n=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Qx(WI.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=WI.Instance.transformGraph(e.modelInitializer);this.initializer=new Qx(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof Te?[e]:e,n={};return t.forEach((a,r)=>n[this.structuredOutputKeys[r]]=a),n}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof Te)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let a=0;a1?n:n[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&_e(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function uj(e,t={},n=qt){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Please provide model artifacts or an IOHandler that loads the model");let t;if(e instanceof Array){let[a,r]=e;if(!a)throw new Error("modelJSON must be the first element of the array");if(!r||!(r instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in a))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in a))throw new Error("Model JSON is missing 'weightsManifest'");let s=qt.getWeightSpecs(a.weightsManifest),i=qt.getModelArtifactsForJSONSync(a,s,r);t=qt.fromMemorySync(i)}else if("load"in e)t=e;else if("modelTopology"in e&&"weightSpecs"in e&&"weightData"in e)t=qt.fromMemorySync(e);else throw new Error("Unknown model format");let n=new k1(t);return n.load(),n}function cj(e){return e.endsWith("/")||(e=e+"/"),`${e}${lj}${oj}`}var dj="4.3.0",OC={};Ee(OC,{CSVDataset:()=>qC,Dataset:()=>gp,FileDataSource:()=>QC,TextLineDataset:()=>HC,URLDataSource:()=>e_,array:()=>Mj,csv:()=>qj,func:()=>jj,generator:()=>Kj,microphone:()=>Yj,version_data:()=>Zj,webcam:()=>Xj,zip:()=>Pj});var hj=vs(_m()),mj=vs(_m());function fj(e,t){return ym(e,t)}function ym(e,t,n=new Map,a=new Set){if(e==null)return null;if(typeof Blob=="function"&&e instanceof Blob)return e.slice();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(Zl(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=ym(o,t,n,a);s[i]=l}return a.delete(e),e.__proto__&&(s.__proto__=e.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function gj(e,t=zC){return LC(e,t)}function LC(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(Zl(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=LC(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 zC(e){return e===null?null:Zl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function WC(e,t){let n=new Map;ym(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(v.isPromise(r)){let s=await r;n.set(a,s)}}return ym(e,t,n)}function Zl(e){let t=!1;if(G().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=FS();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Te)&&!(e instanceof Promise)&&!t)}function bj(e){return e==null||yj(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Te||v.isTypedArray(e)}function yj(e){return e===null||typeof e!="object"&&typeof e!="function"}function xj(e){return fj(e,vj)}function vj(e){return e instanceof Te?{value:e.clone(),recurse:!1}:Zl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var BC=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}},I1=class extends BC{constructor(){super(I1.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;at===!0)}rowMajorBatch(e,t=!0){return new _j(this,e,t)}columnMajorBatch(e,t=!0,n=zC){return this.rowMajorBatch(e,t).map(a=>gj(a,n))}concatenate(e,t){return new UC(VC([this,e]),t)}take(e){return e<0||e==null?this:new Cj(this,e)}skip(e){return e<0||e==null?this:new Tj(this,e)}prefetch(e){return new GC(this,e)}shuffle(e,t){return new Rj(this,e,t)}serial(){return new Nj(this)}},Ij=class extends an{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:xj(e),done:!1}}},Sj=class extends an{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: ${e.message}`,e}}},Nj=class extends an{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Tj=class extends an{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},_j=class extends an{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},Ej=class extends an{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;_e(e.value)}}},Aj=class extends an{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},Fj=class extends an{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},jI=class extends an{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},N1=class extends an{constructor(){super(),this.outputQueue=new I1,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}}},$j=class extends N1{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return!0}},UC=class extends an{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 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WC(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case rs.FAIL:throw new Error(`Zipped streams should have the same length. 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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 a=this,r=hj.alea(t||v.now().toString());return Yn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Yn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};gp.MAX_BUFFER_SIZE=1e4;function Yn(e,t=null){return new class extends gp{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function Mj(e){return Yn(async()=>VC(e),e.length)}function Pj(e){if(!Zl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await WC(e,a=>{if(a instanceof gp)return{value:a.iterator(),recurse:!1};if(Zl(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return kj(n,rs.SHORTEST)},t)}function Oj(e){if(e===null)return null;let t=e[0];return bj(t)?{value:Lj(e),recurse:!1}:{value:null,recurse:!0}}function Lj(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Te?Dt(e):bn(e)}var HC=class extends gp{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` -`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Oh='"',Qp=Symbol("out"),KI=Symbol("field"),Lh=Symbol("quote"),mx=Symbol("quoteafterquote"),XI=Symbol("quoteinquote"),qC=class extends gp{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&&v.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(v.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}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 HC(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.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 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;r14||!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_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new jC(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]===-1/0&&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(v.sizeFromShape(t));return n.set(e,n.length-e.length),bn(n,t)}},KC=class extends an{constructor(e,t){if(super(),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=je([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=$a([s,r,o,i],[1,4])}else this.cropBox=$a([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!G().get("IS_BROWSER"))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 KC(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. 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${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return P(()=>{let t=Qt(ie(e,"float32"),0),n;n=Fa.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return W(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},XC=class{},YC=class extends an{split(e){return new zj(this,e)}},zj=class extends YC{constructor(e,t){super(),this.upstream=e,this.impl=new Wj(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Wj=class extends N1{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Bj=class extends an{decodeUTF8(){return new Vj(this)}},Vj=class extends YC{constructor(e){super(),this.upstream=e,this.impl=new Uj(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Uj=class extends N1{constructor(e){if(super(),this.upstream=e,G().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=FS();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return G().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},ZC=class extends Bj{constructor(e,t={}){super(),this.file=e,this.options=t,v.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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof 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XC{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return JC(this.url)?new QC(this.url,this.fileOptions).iterator():Gj(this.url,this.fileOptions)}};function qj(e,t={}){return new qC(new e_(e),t)}function jj(e){let t=S1(e);return Yn(async()=>t)}function Kj(e){return Yn(async()=>{let t=await e();return S1(()=>t.next())})}async function Xj(e,t){return KC.create(e,t)}async function Yj(e){return jC.create(e)}var Zj="4.3.0";function ge(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var Jj=fr.whereImpl,Kf=class extends $c{nextDataId(){return Kf.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Em(this,Ea())}write(e,t,n){this.firstUse&&(this.firstUse=!1,G().get("IS_NODE")&&N.warn(` + ${a}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=Qx(s,n),o=a===0?0:e.size/a,l=P(()=>{let p=[];e=W(e,[1,a,o]);for(let d=0;d{switch(e.op){case"If":case"StatelessIf":{let a=k("thenBranch",e,t,n),r=k("elseBranch",e,t,n),s=k("cond",e,t,n),i=k("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=k("body",e,t,n),r=k("cond",e,t,n),s=k("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(p=>p.id),l=await i[0].data();i.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&p.dispose()});let u=s;for(;l[0];){let p=u;u=await n.functionMap[a].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);let d=u.map(h=>h.id);p.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&d.indexOf(h.id)===-1&&h.dispose()});let c=await n.functionMap[r].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);l=await c[0].data(),c.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&d.indexOf(h.id)===-1&&h.dispose()})}return 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a=k("tensorListId",e,t,n),r=n.getTensorList(a.id);return[ve(r.size(),"int32")]}case"TensorListResize":{let a=k("tensorListId",e,t,n),r=k("size",e,t,n),s=n.getTensorList(a.id).resize(r);return n.addTensorList(s),[s.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function qI(e,t,n){let[a,r]=k("fusedOps",e,t,n),s=a==="biasadd",i=!s,o=r==="prelu",l=a==="fusedbatchnorm",u=k("numArgs",e,t,n);if(s){if(o&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let p=k("strides",e,t,n),d=Hh(e,t,n),c=k("dataFormat",e,t,n).toUpperCase(),h=k("dilations",e,t,n),[m,f]=k("args",e,t,n);i&&(f=m,m=void 0);let g=k("leakyreluAlpha",e,t,n);return{stride:p,pad:d,dataFormat:c,dilations:h,biasArg:m,preluArg:f,activationFunc:r,leakyreluAlpha:g}}var Zq=(e,t,n,a=on)=>{switch(e.op){case"Conv1D":{let r=k("stride",e,t,n),s=k("pad",e,t,n),i=k("dataFormat",e,t,n).toUpperCase(),o=k("dilation",e,t,n);return[a.conv1d(k("x",e,t,n),k("filter",e,t,n),r,s,i,o)]}case"Conv2D":{let 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r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("kernelSize",e,t,n),o=k("includeBatchInIndex",e,t,n),{result:l,indexes:u}=a.maxPoolWithArgmax(k("x",e,t,n),[i[1],i[2]],[r[1],r[2]],s,o);return[l,u]}case"AvgPool3D":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("kernelSize",e,t,n);return[a.avgPool3d(k("x",e,t,n),[i[1],i[2],i[3]],[r[1],r[2],r[3]],s)]}case"MaxPool3D":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("kernelSize",e,t,n);return[a.maxPool3d(k("x",e,t,n),[i[1],i[2],i[3]],[r[1],r[2],r[3]],s)]}case"Dilation2D":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("dilations",e,t,n),o=r[1],l=r[2],u=i[1],p=i[2];return[a.dilation2d(k("x",e,t,n),k("filter",e,t,n),[o,l],s,[u,p],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Jq=(e,t,n,a=on)=>{switch(e.op){case"Fill":{let r=k("shape",e,t,n),s=k("dtype",e,t,n),i=k("value",e,t,n);return[a.fill(r,i,s)]}case"LinSpace":{let r=k("start",e,t,n),s=k("stop",e,t,n),i=k("num",e,t,n);return[a.linspace(r,s,i)]}case"Multinomial":{let r=k("logits",e,t,n),s=k("numSamples",e,t,n),i=k("seed",e,t,n);return[a.multinomial(r,s,i)]}case"OneHot":{let r=k("indices",e,t,n),s=k("depth",e,t,n),i=k("onValue",e,t,n),o=k("offValue",e,t,n),l=k("dtype",e,t,n);return[a.oneHot(r,s,i,o,l)]}case"Ones":return[a.ones(k("shape",e,t,n),k("dtype",e,t,n))];case"OnesLike":return[a.onesLike(k("x",e,t,n))];case"RandomStandardNormal":return[a.randomStandardNormal(k("shape",e,t,n),k("dtype",e,t,n),k("seed",e,t,n))];case"RandomUniform":return[a.randomUniform(k("shape",e,t,n),k("minval",e,t,n),k("maxval",e,t,n),k("dtype",e,t,n))];case"RandomUniformInt":return[a.randomUniformInt(k("shape",e,t,n),k("minval",e,t,n),k("maxval",e,t,n),k("seed",e,t,n))];case"Range":{let r=k("start",e,t,n),s=k("stop",e,t,n),i=k("step",e,t,n);return[a.range(r,s,i,k("dtype",e,t,n))]}case"TruncatedNormal":{let 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performance."),console.log(l);for(let p=0;pe.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ve(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(a=>a.dispose()),this.tensorMap.clear(),P(()=>{let a=ct(t),r=n.length,s=a.length;v.assert(r===s,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${s} elements.`);for(let i=0;i{let a=[];for(let r=0;r{switch(e.op){case"HashTable":case"HashTableV2":{let r=a.getHashTableHandleByName(e.name);if(r!=null)return[r];{let s=k("keyDType",e,t,n),i=k("valueDType",e,t,n),o=new nj(s,i);return a.addHashTable(e.name,o),[o.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let r=k("tableHandle",e,t,n,a),s=k("keys",e,t,n),i=k("values",e,t,n);return[await a.getHashTableById(r.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let r=k("tableHandle",e,t,n,a),s=k("keys",e,t,n),i=k("defaultValue",e,t,n);return[await a.getHashTableById(r.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let r=k("tableHandle",e,t,n,a);return[a.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},rj=(e,t,n,a=on)=>{switch(e.op){case"ResizeBilinear":{let r=k("images",e,t,n),s=k("size",e,t,n),i=k("alignCorners",e,t,n),o=k("halfPixelCenters",e,t,n);return[a.image.resizeBilinear(r,[s[0],s[1]],i,o)]}case"ResizeNearestNeighbor":{let r=k("images",e,t,n),s=k("size",e,t,n),i=k("alignCorners",e,t,n),o=k("halfPixelCenters",e,t,n);return[a.image.resizeNearestNeighbor(r,[s[0],s[1]],i,o)]}case"CropAndResize":{let r=k("image",e,t,n),s=k("boxes",e,t,n),i=k("boxInd",e,t,n),o=k("cropSize",e,t,n),l=k("method",e,t,n),u=k("extrapolationValue",e,t,n);return[a.image.cropAndResize(r,s,i,o,l,u)]}case"ImageProjectiveTransformV3":{let r=k("images",e,t,n),s=k("transforms",e,t,n),i=k("outputShape",e,t,n),o=k("fillValue",e,t,n),l=k("interpolation",e,t,n),u=k("fillMode",e,t,n);return[a.image.transform(r,s,l.toLowerCase(),u.toLowerCase(),o,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},sj=(e,t,n,a=on)=>{switch(e.op){case"Equal":return[a.equal(k("a",e,t,n),k("b",e,t,n))];case"NotEqual":return[a.notEqual(k("a",e,t,n),k("b",e,t,n))];case"Greater":return[a.greater(k("a",e,t,n),k("b",e,t,n))];case"GreaterEqual":return[a.greaterEqual(k("a",e,t,n),k("b",e,t,n))];case"Less":return[a.less(k("a",e,t,n),k("b",e,t,n))];case"LessEqual":return[a.lessEqual(k("a",e,t,n),k("b",e,t,n))];case"LogicalAnd":return[a.logicalAnd(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[a.logicalNot(k("a",e,t,n))];case"LogicalOr":return[a.logicalOr(k("a",e,t,n),k("b",e,t,n))];case"Select":case"SelectV2":return[a.where(k("condition",e,t,n),k("a",e,t,n),k("b",e,t,n))];case"BitwiseAnd":return[a.bitwiseAnd(k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},ij=(e,t,n,a=on)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[a.matMul(k("a",e,t,n),k("b",e,t,n),k("transposeA",e,t,n),k("transposeB",e,t,n))];case"Einsum":return[a.einsum(k("equation",e,t,n),...k("tensors",e,t,n))];case"Transpose":return[a.transpose(k("x",e,t,n),k("perm",e,t,n))];case"_FusedMatMul":let[r,s]=k("fusedOps",e,t,n),i=r==="biasadd",o=s==="prelu",l=k("numArgs",e,t,n),u=k("leakyreluAlpha",e,t,n);if(i){if(o&&l!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&l!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[p,d]=k("args",e,t,n);return[a.fused.matMul({a:k("a",e,t,n),b:k("b",e,t,n),transposeA:k("transposeA",e,t,n),transposeB:k("transposeB",e,t,n),bias:p,activation:s,preluActivationWeights:d,leakyreluAlpha:u})];case"MatrixBandPart":return[a.linalg.bandPart(k("a",e,t,n),k("numLower",e,t,n),k("numUpper",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},oj=(e,t,n,a=on)=>{switch(e.op){case"EuclideanNorm":return[a.euclideanNorm(k("x",e,t,n),k("axis",e,t,n),k("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[a.localResponseNormalization(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[a.softmax(k("x",e,t,n))];case"LogSoftmax":return[a.logSoftmax(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},lj=(e,t,n,a=on)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:r,outputDenseValues:s}=a.raggedGather(k("paramsNestedSplits",e,t,n),k("paramsDenseValues",e,t,n),k("indices",e,t,n),k("outputRaggedRank",e,t,n));return r.concat(s)}case"RaggedRange":{let{rtNestedSplits:r,rtDenseValues:s}=a.raggedRange(k("starts",e,t,n),k("limits",e,t,n),k("splits",e,t,n));return[r,s]}case"RaggedTensorToTensor":return[a.raggedTensorToTensor(k("shape",e,t,n),k("values",e,t,n),k("defaultValue",e,t,n),k("rowPartitionTensors",e,t,n),k("rowPartitionTypes",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},uj=(e,t,n,a=on)=>{switch(e.op){case"Max":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.max(k("x",e,t,n),o,l)]}case"Mean":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.mean(k("x",e,t,n),o,l)]}case"Min":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.min(k("x",e,t,n),o,l)]}case"Sum":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.sum(k("x",e,t,n),o,l)]}case"All":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.all(k("x",e,t,n),o,l)]}case"Any":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.any(k("x",e,t,n),o,l)]}case"ArgMax":{let o=k("axis",e,t,n);return[a.argMax(k("x",e,t,n),o)]}case"ArgMin":{let o=k("axis",e,t,n);return[a.argMin(k("x",e,t,n),o)]}case"Prod":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.prod(k("x",e,t,n),o,l)]}case"Cumprod":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumprod(k("x",e,t,n),o,l,u)]}case"Cumsum":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumsum(k("x",e,t,n),o,l,u)]}case"Bincount":let r=k("x",e,t,n),s=k("weights",e,t,n),i=k("size",e,t,n);return[a.bincount(r,s,i)];case"DenseBincount":{let o=k("x",e,t,n),l=k("weights",e,t,n),u=k("size",e,t,n),p=k("binaryOutput",e,t,n);return[a.denseBincount(o,l,u,p)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},pj=(e,t,n,a=on)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),s=k("axis",e,t,n),i=k("tensors",e,t,n);return i=i.slice(0,r),[a.concat(i,s)]}case"Gather":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gather(r,a.cast(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),s=k("batchDims",e,t,n),i=k("x",e,t,n),o=k("indices",e,t,n);return[a.gather(i,a.cast(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,n),s=[];for(let o=0;o{let r=k("axis",e,t,n),s=k("tensors",e,t,n),i=s[0].shape,o=a.squeeze(s[0]).shape,l=s.map(u=>{let p=v.arraysEqual(u.shape,i);if(!p&&!v.arraysEqual(a.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:a.reshape(u,i)});return[a.stack(l,r)]});case"Unpack":{let r=k("axis",e,t,n),s=k("tensor",e,t,n);return a.unstack(s,r)}case"Tile":{let r=k("reps",e,t,n);return[a.tile(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),s=k("numOrSizeSplits",e,t,n),i=k("x",e,t,n);return a.split(i,s,r)}case"ScatterNd":{let r=k("indices",e,t,n),s=k("values",e,t,n),i=k("shape",e,t,n);return[a.scatterND(r,s,i)]}case"GatherNd":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gatherND(r,s)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),s=k("outputShape",e,t,n),i=k("sparseValues",e,t,n),o=k("defaultValue",e,t,n);return[a.sparseToDense(r,i,s,i.dtype===o.dtype?o:a.cast(o,i.dtype))]}case"TensorScatterUpdate":{let r=k("indices",e,t,n),s=k("values",e,t,n),i=k("tensor",e,t,n);return[a.tensorScatterUpdate(i,r,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},cj=(e,t,n,a=on)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=a.sparse.sparseFillEmptyRows(k("indices",e,t,n),k("values",e,t,n),k("denseShape",e,t,n),k("defaultValue",e,t,n));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=a.sparse.sparseReshape(k("inputIndices",e,t,n),k("inputShape",e,t,n),k("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[a.sparse.sparseSegmentMean(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];case"SparseSegmentSum":return[a.sparse.sparseSegmentSum(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},dj=(e,t,n,a=on)=>{switch(e.op){case"FFT":return[a.fft(k("x",e,t,n))];case"IFFT":return[a.ifft(k("x",e,t,n))];case"RFFT":return[a.rfft(k("x",e,t,n))];case"IRFFT":return[a.irfft(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},hj=(e,t,n,a=on)=>{switch(e.op){case"StaticRegexReplace":return[a.string.staticRegexReplace(k("input",e,t,n),k("pattern",e,t,n),k("rewrite",e,t,n),k("replaceGlobal",e,t,n))];case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=a.string.stringNGrams(k("data",e,t,n),k("dataSplits",e,t,n),k("separator",e,t,n),k("nGramWidths",e,t,n),k("leftPad",e,t,n),k("rightPad",e,t,n),k("padWidth",e,t,n),k("preserveShortSequences",e,t,n));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=a.string.stringSplit(k("input",e,t,n),k("delimiter",e,t,n),k("skipEmpty",e,t,n));return[r,s,i]}case"StringToHashBucketFast":return[a.string.stringToHashBucketFast(k("input",e,t,n),k("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},mj=(e,t,n,a=on)=>{switch(e.op){case"Cast":return[a.cast(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[a.expandDims(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[a.squeeze(k("x",e,t,n),r)]}case"Reshape":return[a.reshape(k("x",e,t,n),k("shape",e,t,n))];case"EnsureShape":return[a.ensureShape(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[a.mirrorPad(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[a.pad(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),s=k("paddings",e,t,n);return[a.spaceToBatchND(k("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),s=k("crops",e,t,n);return[a.batchToSpaceND(k("x",e,t,n),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),s=k("dataFormat",e,t,n).toUpperCase();return[a.depthToSpace(k("x",e,t,n),r,s)]}case"BroadcastTo":return[a.broadcastTo(k("x",e,t,n),k("shape",e,t,n))];case"BroadcastArgs":return[a.broadcastArgs(k("s0",e,t,n),k("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function jI(e,t,n,a,r=P){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>Uq(i,o,l));case"basic_math":return r(()=>Gq(i,o,l));case"control":return Yq(i,o,l);case"convolution":return r(()=>Zq(i,o,l));case"creation":return r(()=>Jq(i,o,l));case"dynamic":return Qq(i,o,l);case"evaluation":return r(()=>ej(i,o,l));case"image":return r(()=>rj(i,o,l));case"graph":return r(()=>tj(i,o,l));case"logical":return r(()=>sj(i,o,l));case"matrices":return r(()=>ij(i,o,l));case"normalization":return r(()=>oj(i,o,l));case"ragged":return r(()=>lj(i,o,l));case"reduction":return r(()=>uj(i,o,l));case"slice_join":return r(()=>pj(i,o,l));case"sparse":return r(()=>cj(i,o,l));case"spectral":return r(()=>dj(i,o,l));case"string":return r(()=>hj(i,o,l));case"transformation":return r(()=>mj(i,o,l));case"hash_table":return aj(i,o,l,a);case"custom":let u=NC(i.op);if(u&&u.customExecutor)return u.customExecutor(new Vq(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var KI=class{constructor(e={},t={},n={},a={},r){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.parseNodeNameCache=r,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;tt.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 XI(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(c=>Zn(c)[0]));a=a||[];let p=new Set(a.map(c=>Zn(c.name)[0])),d=[...t];for(;d.length>0;){let c=d.pop();if((Js(c)||kj(c)||Ij(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&!u.has(c.name)&&!p.has(c.name)){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function fj(e,t){let{usedNodes:n,inputs:a}=t,r=Object.keys(a).map(g=>Zn(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>n.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(b=>[b.name,b])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),d={};for(let g of u){d[g.name]=d[g.name]||0;for(let b of g.children)i(b)||(d[b.name]=Number.POSITIVE_INFINITY),d[b.name]=(d[b.name]||0)+1}let c=Object.entries(d).filter(([,g])=>g===0).map(([g])=>g),h=[...c];for(;c.length>0;){let g=c.pop(),b=p.get(g);for(let y of b.children.filter(i))--d[y.name]===0&&(h.push(y.name),c.push(y.name))}let m=h.map(g=>p.get(g)),f=gj(m,l);return bj(f,l),f}function gj(e,t){let n=new Map(e.map(s=>[s.name,s])),a=t.map(s=>s.name),r=new Set(a);for(;a.length>0;){let s=a.pop(),i=n.get(s);for(let o of i.children)!n.has(o.name)||r.has(o.name)||(r.add(o.name),a.push(o.name))}return e.filter(s=>r.has(s.name))}var Ph=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function bj(e,t){let n=new Map(e.map((o,l)=>[o.name,l])),a=new Set(t.map(o=>o.name)),r=o=>a.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!n.has(l.name))throw new Ph(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new Ph(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!n.has(l.name))throw new Ph(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new Ph(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function yj(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>Js(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;oe[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new ev(e.functions[n],this)})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPARATOR)+"--"+a.join(this.SEPARATOR)}compile(e,t){let n=XI(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${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 l=t.map(p=>p.name),u=Object.keys(e);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${u}]. Missing the following inputs: [${a}]`)}let i=fj(this.graph,n),o=yj(i);return{orderedNodes:i,nodeLiveUntilMap:o}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return Ht(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,n])=>[t,this.cloneTensorList(n)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(d=>this.graph.nodes[Zn(d)[0]]),r=t.map(d=>Zn(d)[0]),s=new Set(r),i=r.map(d=>this.graph.nodes[d]);i.length===0&&(i=this._outputs);let o=this.getCompilationKey(a,i),l=this.compiledMap.get(o);l==null&&(l=this.compile(e,i),this.compiledMap.set(o,l));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let u={},p={};return P(()=>{let d=new KI(this.weightMap,u,p,this.functionExecutorMap,this.parseNodeNameCache),c=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(g=>{let[b,y]=Zn(g,d),x=[];x[y]=e[g],c[b]=x,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(x))});let h=this.getFrozenTensorIds(c),{orderedNodes:m,nodeLiveUntilMap:f}=l;for(let g of m){if(c[g.name])continue;let b=jI(g,c,d,this._resourceManager);if(v.isPromise(b))throw new Error(`The execution of the op '${g.op}' returned a promise. 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You can use model.execute() instead.");let y=l.filter(x=>!Js(x)&&!dn(x.name,m,t)).map(x=>x.name);if(y.length>0){let x="";throw d!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${c}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${p}]. ${x}`)}return m}processStack(e,t,n,a,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();n.currentContext=p.contexts;let d="";if(p.node.op==="Enter"&&k("isConstant",p.node,a,n)&&([d]=_r(p.node.name,n)),a[p.node.name]==null){let c=jI(p.node,a,n,this._resourceManager);d||([d]=_r(p.node.name,n));let h=n.currentContext;v.isPromise(c)?u.push(c.then(m=>(a[d]=m,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(m)),n.currentContext=h,this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l),m))):(a[d]=c,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(c)),this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l))}else this.processChildNodes(p.node,t,n,a,r,l)}return u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=_r(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!dn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!dn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Zn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.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 ${n.dtype}`)})}mapInputs(e){var t,n;let a={};for(let r in e){let s=(n=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||n===void 0?void 0:n[r];s!=null?a[s.name]=e[r]:a[r]=e[r]}return a}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Zn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var n,a;let r=(a=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||a===void 0?void 0:a[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[n]=Zn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Sj=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Nj="?tfjs-format=file",Tj="model.json",S1=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},n=qt){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new Sj}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(n=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new ev(UI.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=UI.Instance.transformGraph(e.modelInitializer);this.initializer=new ev(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof Ce?[e]:e,n={};return t.forEach((a,r)=>n[this.structuredOutputKeys[r]]=a),n}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof Ce)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let a=0;a1?n:n[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await 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Aj="4.5.0",jC={};Ee(jC,{CSVDataset:()=>n_,Dataset:()=>gp,FileDataSource:()=>u_,TextLineDataset:()=>t_,URLDataSource:()=>p_,array:()=>Qj,csv:()=>p5,func:()=>c5,generator:()=>d5,microphone:()=>m5,version_data:()=>f5,webcam:()=>h5,zip:()=>e5});var Fj=ws(_m()),$j=ws(_m());function Dj(e,t){return ym(e,t)}function ym(e,t,n=new Map,a=new Set){if(e==null)return null;if(typeof Blob=="function"&&e instanceof Blob)return e.slice();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(Jl(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=ym(o,t,n,a);s[i]=l}return a.delete(e),e.__proto__&&(s.__proto__=e.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function Rj(e,t=XC){return KC(e,t)}function KC(e,t,n=new Set){let 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this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},Uj=class extends sn{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Gj=class extends sn{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},qj=class extends sn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},jj=class extends sn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;_e(e.value)}}},Kj=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},Xj=class extends sn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},YI=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},C1=class extends sn{constructor(){super(),this.outputQueue=new N1,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}}},Yj=class extends C1{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=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return!0}},QC=class extends sn{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}},ss;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ss||(ss={}));var Zj=class extends sn{constructor(e,t=ss.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 sn?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await YC(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ss.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ss.SHORTEST:return{value:null,done:!0};case ss.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},e_=class extends sn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new ZC(t)}summary(){return`${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()}},Jj=class extends e_{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=$j.alea(n||v.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}}},gp=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is + ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Yn(async()=>(await n.iterator()).columnMajorBatch(e,t,t5),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Yn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Yn(async()=>(await t.iterator()).filter(a=>P(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Yn(async()=>(await t.iterator()).map(n=>P(()=>e(n))),this.size)}mapAsync(e){let t=this;return Yn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Yn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Yn(async()=>{let a=T1(async()=>({value:await t.iterator(),done:!1}));return zj(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=Fj.alea(t||v.now().toString());return Yn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Yn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};gp.MAX_BUFFER_SIZE=1e4;function Yn(e,t=null){return new class extends gp{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function Qj(e){return Yn(async()=>JC(e),e.length)}function e5(e){if(!Jl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await YC(e,a=>{if(a instanceof gp)return{value:a.iterator(),recurse:!1};if(Jl(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Wj(n,ss.SHORTEST)},t)}function t5(e){if(e===null)return null;let t=e[0];return Mj(t)?{value:n5(e),recurse:!1}:{value:null,recurse:!0}}function n5(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ce?Dt(e):bn(e)}var t_=class extends gp{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` +`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Oh='"',Qp=Symbol("out"),ZI=Symbol("field"),Lh=Symbol("quote"),fx=Symbol("quoteafterquote"),JI=Symbol("quoteinquote"),n_=class extends gp{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&&v.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(v.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}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 t_(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.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 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;r14||!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_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new a_(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]===-1/0&&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(v.sizeFromShape(t));return n.set(e,n.length-e.length),bn(n,t)}},r_=class extends sn{constructor(e,t){if(super(),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=je([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=$a([s,r,o,i],[1,4])}else this.cropBox=$a([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!G().get("IS_BROWSER"))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 r_(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Uo.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return P(()=>{let t=tn(ie(e,"float32"),0),n;n=ea.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return W(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},s_=class{},i_=class extends sn{split(e){return new a5(this,e)}},a5=class extends i_{constructor(e,t){super(),this.upstream=e,this.impl=new r5(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},r5=class extends C1{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},s5=class extends sn{decodeUTF8(){return new i5(this)}},i5=class extends i_{constructor(e){super(),this.upstream=e,this.impl=new o5(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},o5=class extends C1{constructor(e){if(super(),this.upstream=e,G().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=RS();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return G().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},o_=class extends s5{constructor(e,t={}){super(),this.file=e,this.options=t,v.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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof 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p=N.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,b=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,w=p.effectiveFilterDepth,I=p.effectiveFilterHeight,T=p.effectiveFilterWidth,C=w-1-p.padInfo.front,E=T-1-p.padInfo.left,F=I-1-p.padInfo.top,D=ze(s.shape,"float32"),$=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M=p.outDepth||Math.floor(te)!==te))for(let re=0;re=p.outHeight||Math.floor(se)!==se))for(let ye=0;ye=p.outWidth||Math.floor(ue)!==ue)continue;let be=S.get(M,te,se,ue,B);ee+=be}}}D.set(ee*$,M,U,H,j,B)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var YK={kernelName:Rc,backendName:"cpu",kernelFunc:XK};function ZK(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,b=p.effectiveFilterHeight,y=p.effectiveFilterWidth,x=y-1-p.padInfo.left,w=b-1-p.padInfo.top,I=ze(i.shape,"float32"),T=1/(h*m),C=n.data.get(r.dataId).values,E=ze(r.shape,"float32",C);for(let F=0;F=p.outHeight||Math.floor(j)!==j))for(let K=0;K=p.outWidth||Math.floor(Z)!==Z)continue;let J=E.get(F,j,Z,D);U+=J}}I.set(U*T,F,$,S,D)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var JK={kernelName:$m,backendName:"cpu",kernelFunc:ZK};function QK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),ge([r,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=a;u==null&&(u=.001);let p=n.data.get(r.dataId).values,d=n.data.get(o.dataId).values,c=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(p.length),g=m.length,b=h.length,y=c.length,x=d.length,w=0,I=0,T=0,C=0;for(let E=0;E=g&&(w=0),I>=x&&(I=0),T>=b&&(T=0),C>=y&&(C=0);return n.makeTensorInfo(r.shape,r.dtype,f)}var e8={kernelName:Ki,backendName:"cpu",kernelFunc:QK};function t8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;ge([r],"batchToSpaceND");let o=s.reduce((b,y)=>b*y),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=xt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Un({inputs:{x:h},backend:n,attrs:{perm:u}}),f=xt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=bi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var n8={kernelName:uu,backendName:"cpu",kernelFunc:t8};function a8(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,u=_1(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var r8={kernelName:pu,backendName:"cpu",kernelFunc:a8};function s8(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var i8={kernelName:Mc,backendName:"cpu",kernelFunc:s8},o8=it(Is,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(v.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 u=0;uf.shape);N.assertParamsConsistent(i,s);let o=N.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>v.sizeFromShape(f.shape)>0);if(l.length===1)return dr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(w=>gi({inputs:{input:w},backend:n})),g=l.map(w=>Ql({inputs:{input:w},backend:n})),b=eu({inputs:f,backend:n,attrs:{axis:s}}),y=eu({inputs:g,backend:n,attrs:{axis:s}}),x=Jn({inputs:{real:b,imag:y},backend:n});return f.forEach(w=>n.disposeIntermediateTensorInfo(w)),g.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),x}let u=l.map(f=>{let g=[-1,v.sizeFromShape(f.shape.slice(s))];return xt({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=N.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=E1(p,o,t[0].dtype,d),h=N.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var d8={kernelName:cu,backendName:"cpu",kernelFunc:eu};function X_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,b=c.padInfo.left,y=c.padInfo.top,x=c.dataFormat==="channelsLast",w=new Bt(c.outShape,r.dtype),I=v.computeStrides(r.shape),T=v.computeStrides(s.shape),C=I[0],E=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=w.strides[0],S=x?w.strides[1]:w.strides[2],M=x?w.strides[2]:1,B=x?1:w.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,j=w.values;for(let K=0;K=c.inHeight)continue;let ye=re*T[0],ue=Z+se*E;for(let be=0;be=c.inWidth)continue;let ht=ye+We*T[1],st=ue+Ge*F,at=ht;for(let rt=0;rt=u.inDepth)continue;let K=H*F[0],Z=$+j*E[1];for(let J=0;J=u.inHeight)continue;let se=K+te*F[1],ye=Z+re*E[2];for(let ue=0;ue=u.inWidth)continue;let Ge=se+Se*F[2],ht=ye+We*u.inChannels,st=Ge;for(let at=0;atMath.cos(e)),N8={kernelName:Oi,backendName:"cpu",kernelFunc:S8},T8=it(Li,e=>Math.cosh(e)),C8={kernelName:Li,backendName:"cpu",kernelFunc:T8};function _8(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=ze([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,w=n.data.get(r.dataId).values,I=v.computeStrides(r.shape),T=v.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-F)*(d-1)/(f-1):0,U=g>1?(S-D)*(c-1)/(g-1):0;for(let H=0;H1?F*(d-1)+H*B:.5*(F+$)*(d-1);if(j<0||j>d-1){for(let K=0;K1?D*(c-1)+ee*U:.5*(D+S)*(c-1);if(ae<0||ae>c-1){for(let ye=0;ye1?D*(c-1)+K*U:.5*(D+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aeb+m-y-1:(b,y)=>b+y;for(let b=0;bb+m-y-1:(b,y)=>b+y;for(let b=0;b`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=N.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:b,padInfo:y}=h,x=y.left,w=y.top,I=h.outChannels/h.inChannels,T=new Bt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,E=n.data.get(s.dataId).values,F=T.values;for(let D=0;D=h.inHeight)continue;let K=H*d[0],Z=$+j*p[1];for(let J=0;J=h.inWidth)continue;let se=K+te*d[1],ye=Z+re*h.inChannels,ue=ee,be=se;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:b,outWidth:y,padInfo:x,strideHeight:w,strideWidth:I,filterHeight:T,filterWidth:C,dilationHeight:E,dilationWidth:F,outShape:D}=N.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=v.sizeFromShape(D),S=D.length,M=v.getArrayFromDType(a.dtype,$);for(let B=0;B=0&&te=0&&seJ&&(J=be)}}}let ee=v.locToIndex([B,U,j,Z],S,v.computeStrides(D));M[ee]=J}}}return{dataId:l.write(v.toTypedArray(M,a.dtype),D,a.dtype),shape:D,dtype:a.dtype}}},q8={kernelName:Ol,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:F}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===F.length,()=>`Error in ${Ol}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=v.toNestedArray(F,u.data.get(s.dataId).values),$=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=se,Z=ee,J=te)}}}$[Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(v.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},j8={kernelName:Pl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:F}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===F.length,()=>`Error in ${Pl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=v.toNestedArray(F,u.data.get(s.dataId).values),$=v.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=se,Z=ae,J=re)}}}$[S][Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(v.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function _d(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ge(r,"sum");let o;r.dtype==="bool"?o=ys({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=dr({inputs:{x:r},backend:n});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=N.getAxesPermutation(u,l),d=u,c=o;p!=null&&(c=Un({inputs:{x:o},backend:n,attrs:{perm:p}}),d=N.getInnerMostAxes(d.length,l)),N.assertAxesAreInnerMostDims("sum",d,c.shape.length);let[h,m]=N.computeOutAndReduceShapes(c.shape,d),f=N.upcastType(c.dtype,"int32"),g=xm(n,h,f),b=v.sizeFromShape(m),y=n.data.get(g.dataId).values,x=n.data.get(c.dataId).values;for(let w=0;w=0&&(c=_d({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var Y8={kernelName:Om,backendName:"cpu",kernelFunc:X8};function Z8(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;ge([a,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l=0?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",s)}var J8={kernelName:bu,backendName:"cpu",kernelFunc:Z8},Q8=N.ERF_P,eX=N.ERF_A1,tX=N.ERF_A2,nX=N.ERF_A3,aX=N.ERF_A4,rX=N.ERF_A5,sX=it(yu,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+Q8*n);return t*(1-((((rX*a+aX)*a+nX)*a+tX)*a+eX)*a*Math.exp(-n*n))}),iX={kernelName:yu,backendName:"cpu",kernelFunc:sX};function km(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),xt({inputs:{x:r},backend:n,attrs:{shape:o}})}var oX={kernelName:vu,backendName:"cpu",kernelFunc:km},lX=Gt((e,t)=>e/t),W1=sn(Vi,lX),tv={kernelName:Vi,backendName:"cpu",kernelFunc:W1};function Z_(e,t,n){let a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),d=v.getTypedArrayFromDType("float32",p),c=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:a}=e,r=n,s=v.getTypedArrayFromDType(a.dtype,v.sizeFromShape(a.shape)),[i,o,l,u]=a.shape,p=r.data.get(a.dataId).values;for(let d=0;d=0&&y=0,()=>`GatherV2: the index value ${I} is not in [0, ${p-1}]`)}let d=o;o==null&&(d=0);let c=v.sizeFromShape(s.shape),h=N.segment_util.collectGatherOpShapeInfo(r,s,l,d),m=xt({inputs:{x:r},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=xt({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),g=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],b=n.bufferSync(f),y=n.bufferSync(m),x=m_(y,b,g);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.makeTensorInfo(h.outputShape,x.dtype,x.values)}var SX={kernelName:ku,backendName:"cpu",kernelFunc:IX};function NX(e){let{inputs:t,backend:n}=e,{input:a}=t,r=v.sizeFromShape(a.shape),s=a.shape[a.shape.length-1],i=r/s,o=xt({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=Z_(o,!0,n),u=xt({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var TX={kernelName:zm,backendName:"cpu",kernelFunc:NX},CX=it(Zi,e=>Number.isFinite(e)?1:0,"bool"),_X={kernelName:Zi,backendName:"cpu",kernelFunc:CX},EX=it(Ji,e=>Math.abs(e)===1/0?1:0,"bool"),AX={kernelName:Ji,backendName:"cpu",kernelFunc:EX},FX=it(Qi,e=>Number.isNaN(e)?1:0,"bool"),$X={kernelName:Qi,backendName:"cpu",kernelFunc:FX};function DX(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=x_(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var RX={kernelName:Cu,backendName:"cpu",kernelFunc:DX},MX=it(no,e=>Math.log1p(e)),PX={kernelName:no,backendName:"cpu",kernelFunc:MX},OX=Gt((e,t)=>e&&t),LX=sn(_u,OX,null,"bool"),zX={kernelName:_u,backendName:"cpu",kernelFunc:LX},WX=it(Eu,e=>e?0:1,"bool"),BX={kernelName:Eu,backendName:"cpu",kernelFunc:WX},VX=Gt((e,t)=>e||t),UX=sn(Au,VX,null,"bool"),GX={kernelName:Au,backendName:"cpu",kernelFunc:UX};function HX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;ge(r,"LRN");let u=r.shape[3],p=u-1,d=n.data.get(r.dataId).values,c=v.sizeFromShape(r.shape),h=new Float32Array(c);function m(f){let g=f%u,b=f-g+Math.max(0,g-s),y=f-g+Math.min(g+s,p),x=0;for(;b<=y;b++){let w=d[b];x+=w*w}return x}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. 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b8(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,b=p.effectiveFilterHeight,y=p.effectiveFilterWidth,x=y-1-p.padInfo.left,w=b-1-p.padInfo.top,I=ze(i.shape,"float32"),T=1/(h*m),C=n.data.get(r.dataId).values,E=ze(r.shape,"float32",C);for(let F=0;F=p.outHeight||Math.floor(j)!==j))for(let K=0;K=p.outWidth||Math.floor(Z)!==Z)continue;let J=E.get(F,j,Z,D);U+=J}}I.set(U*T,F,$,S,D)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var y8={kernelName:$m,backendName:"cpu",kernelFunc:b8};function x8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((b,y)=>b*y),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=xt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Un({inputs:{x:h},backend:n,attrs:{perm:u}}),f=xt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=xi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var k8={kernelName:pu,backendName:"cpu",kernelFunc:w8};function I8(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,u=A1(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var S8={kernelName:cu,backendName:"cpu",kernelFunc:I8};function N8(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var T8={kernelName:Mc,backendName:"cpu",kernelFunc:N8},C8=it(Is,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(v.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 u=0;uf.shape);N.assertParamsConsistent(i,s);let o=N.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>v.sizeFromShape(f.shape)>0);if(l.length===1)return dr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(w=>yi({inputs:{input:w},backend:n})),g=l.map(w=>eu({inputs:{input:w},backend:n})),b=tu({inputs:f,backend:n,attrs:{axis:s}}),y=tu({inputs:g,backend:n,attrs:{axis:s}}),x=Jn({inputs:{real:b,imag:y},backend:n});return f.forEach(w=>n.disposeIntermediateTensorInfo(w)),g.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),x}let u=l.map(f=>{let g=[-1,v.sizeFromShape(f.shape.slice(s))];return xt({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=N.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=F1(p,o,t[0].dtype,d),h=N.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var $8={kernelName:du,backendName:"cpu",kernelFunc:tu};function iE(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,b=c.padInfo.left,y=c.padInfo.top,x=c.dataFormat==="channelsLast",w=new Vt(c.outShape,r.dtype),I=v.computeStrides(r.shape),T=v.computeStrides(s.shape),C=I[0],E=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=w.strides[0],S=x?w.strides[1]:w.strides[2],M=x?w.strides[2]:1,B=x?1:w.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,j=w.values;for(let K=0;K=c.inHeight)continue;let ye=re*T[0],ue=Z+se*E;for(let be=0;be=c.inWidth)continue;let ht=ye+We*T[1],st=ue+Ge*F,at=ht;for(let rt=0;rt=u.inDepth)continue;let K=H*F[0],Z=$+j*E[1];for(let J=0;J=u.inHeight)continue;let se=K+te*F[1],ye=Z+re*E[2];for(let ue=0;ue=u.inWidth)continue;let Ge=se+Se*F[2],ht=ye+We*u.inChannels,st=Ge;for(let at=0;atMath.cos(e)),H8={kernelName:zi,backendName:"cpu",kernelFunc:G8},q8=it(Wi,e=>Math.cosh(e)),j8={kernelName:Wi,backendName:"cpu",kernelFunc:q8};function K8(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=ze([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,w=n.data.get(r.dataId).values,I=v.computeStrides(r.shape),T=v.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-F)*(d-1)/(f-1):0,U=g>1?(S-D)*(c-1)/(g-1):0;for(let H=0;H1?F*(d-1)+H*B:.5*(F+$)*(d-1);if(j<0||j>d-1){for(let K=0;K1?D*(c-1)+ee*U:.5*(D+S)*(c-1);if(ae<0||ae>c-1){for(let ye=0;ye1?D*(c-1)+K*U:.5*(D+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aeb+m-y-1:(b,y)=>b+y;for(let b=0;bb+m-y-1:(b,y)=>b+y;for(let b=0;b`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=N.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:b,padInfo:y}=h,x=y.left,w=y.top,I=h.outChannels/h.inChannels,T=new Vt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,E=n.data.get(s.dataId).values,F=T.values;for(let D=0;D=h.inHeight)continue;let K=H*d[0],Z=$+j*p[1];for(let J=0;J=h.inWidth)continue;let se=K+te*d[1],ye=Z+re*h.inChannels,ue=ee,be=se;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:b,outWidth:y,padInfo:x,strideHeight:w,strideWidth:I,filterHeight:T,filterWidth:C,dilationHeight:E,dilationWidth:F,outShape:D}=N.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=v.sizeFromShape(D),S=D.length,M=v.getArrayFromDType(a.dtype,$);for(let B=0;B=0&&te=0&&seJ&&(J=be)}}}let ee=v.locToIndex([B,U,j,Z],S,v.computeStrides(D));M[ee]=J}}}return{dataId:l.write(v.toTypedArray(M,a.dtype),D,a.dtype),shape:D,dtype:a.dtype}}},dX={kernelName:Ll,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:F}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===F.length,()=>`Error in ${Ll}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=v.toNestedArray(F,u.data.get(s.dataId).values),$=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=se,Z=ee,J=te)}}}$[Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(v.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},hX={kernelName:Ol,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:F}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===F.length,()=>`Error in ${Ol}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=v.toNestedArray(F,u.data.get(s.dataId).values),$=v.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=se,Z=ae,J=re)}}}$[S][Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(v.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function _d(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ge(r,"sum");let o;r.dtype==="bool"?o=xs({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=dr({inputs:{x:r},backend:n});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=N.getAxesPermutation(u,l),d=u,c=o;p!=null&&(c=Un({inputs:{x:o},backend:n,attrs:{perm:p}}),d=N.getInnerMostAxes(d.length,l)),N.assertAxesAreInnerMostDims("sum",d,c.shape.length);let[h,m]=N.computeOutAndReduceShapes(c.shape,d),f=N.upcastType(c.dtype,"int32"),g=xm(n,h,f),b=v.sizeFromShape(m),y=n.data.get(g.dataId).values,x=n.data.get(c.dataId).values;for(let w=0;w=0&&(c=_d({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var gX={kernelName:Lm,backendName:"cpu",kernelFunc:fX};function bX(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;ge([a,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l=0?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",s)}var yX={kernelName:yu,backendName:"cpu",kernelFunc:bX},xX=N.ERF_P,vX=N.ERF_A1,wX=N.ERF_A2,kX=N.ERF_A3,IX=N.ERF_A4,SX=N.ERF_A5,NX=it(xu,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+xX*n);return t*(1-((((SX*a+IX)*a+kX)*a+wX)*a+vX)*a*Math.exp(-n*n))}),TX={kernelName:xu,backendName:"cpu",kernelFunc:NX};function km(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),xt({inputs:{x:r},backend:n,attrs:{shape:o}})}var CX={kernelName:wu,backendName:"cpu",kernelFunc:km},_X=Ot((e,t)=>e/t),V1=Zt(Gi,_X),nv={kernelName:Gi,backendName:"cpu",kernelFunc:V1};function lE(e,t,n){let a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),d=v.getTypedArrayFromDType("float32",p),c=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:a}=e,r=n,s=v.getTypedArrayFromDType(a.dtype,v.sizeFromShape(a.shape)),[i,o,l,u]=a.shape,p=r.data.get(a.dataId).values;for(let d=0;d=0&&y=0,()=>`GatherV2: the index value ${I} is not in [0, ${p-1}]`)}let d=o;o==null&&(d=0);let c=v.sizeFromShape(s.shape),h=N.segment_util.collectGatherOpShapeInfo(r,s,l,d),m=xt({inputs:{x:r},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=xt({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),g=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],b=n.bufferSync(f),y=n.bufferSync(m),x=N_(y,b,g);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.makeTensorInfo(h.outputShape,x.dtype,x.values)}var GX={kernelName:Iu,backendName:"cpu",kernelFunc:UX};function HX(e){let{inputs:t,backend:n}=e,{input:a}=t,r=v.sizeFromShape(a.shape),s=a.shape[a.shape.length-1],i=r/s,o=xt({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=lE(o,!0,n),u=xt({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var qX={kernelName:Wm,backendName:"cpu",kernelFunc:HX},jX=it(Qi,e=>Number.isFinite(e)?1:0,"bool"),KX={kernelName:Qi,backendName:"cpu",kernelFunc:jX},XX=it(eo,e=>Math.abs(e)===1/0?1:0,"bool"),YX={kernelName:eo,backendName:"cpu",kernelFunc:XX},ZX=it(to,e=>Number.isNaN(e)?1:0,"bool"),JX={kernelName:to,backendName:"cpu",kernelFunc:ZX};function QX(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=A_(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var eY={kernelName:_u,backendName:"cpu",kernelFunc:QX},tY=it(ro,e=>Math.log1p(e)),nY={kernelName:ro,backendName:"cpu",kernelFunc:tY},aY=Ot((e,t)=>e&&t),rY=Zt(Eu,aY,null,"bool"),sY={kernelName:Eu,backendName:"cpu",kernelFunc:rY},iY=it(Au,e=>e?0:1,"bool"),oY={kernelName:Au,backendName:"cpu",kernelFunc:iY},lY=Ot((e,t)=>e||t),uY=Zt(Fu,lY,null,"bool"),pY={kernelName:Fu,backendName:"cpu",kernelFunc:uY};function cY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;ge(r,"LRN");let u=r.shape[3],p=u-1,d=n.data.get(r.dataId).values,c=v.sizeFromShape(r.shape),h=new Float32Array(c);function m(f){let g=f%u,b=f-g+Math.max(0,g-s),y=f-g+Math.min(g+s,p),x=0;for(;b<=y;b++){let w=d[b];x+=w*w}return x}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. 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To speed things up dram 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:u}}function Ho(e,t,n="index"){let a=v.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 Yf(e,t,n="index"){let a=v.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / outShapeStrides[${s}]`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function LZ(e,t){let n=e.length,a=e.map(s=>`${t}[${s}]`),r=new Array(n-1);r[n-2]=a[n-1];for(let s=n-3;s>=0;--s)r[s]=`(${r[s+1]} * ${a[s+1]})`;return r}function zZ(e,t,n="index"){let a=e.map((s,i)=>i),r=LZ(a,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function G1(e){let t=v.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:u}}function jo(e,t,n="index"){let a=v.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 Zf(e,t,n="index"){let a=v.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / outShapeStrides[${s}]`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function rJ(e,t){let n=e.length,a=e.map(s=>`${t}[${s}]`),r=new Array(n-1);r[n-2]=a[n-1];for(let s=n-3;s>=0;--s)r[s]=`(${r[s+1]} * ${a[s+1]})`;return r}function sJ(e,t,n="index"){let a=e.map((s,i)=>i),r=rJ(a,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function q1(e){let t=v.computeStrides(e).map(n=>n.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; } -`}function H1(){return` +`}function j1(){return` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } -`}var EE=` +`}var WE=` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -155,22 +159,22 @@ Hi, looks like you are running TensorFlow.js in Node.js. 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3:return mJ(e,t);default:return gJ(e,t)}}function BZ(e,t,n=!1,a){let r="";n?r+=FE(e,a):r+=xp(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=vJ(e,t):r+=wJ(e,t)),r}function VZ(e,t,n){switch(e.length){case 0:return $E();case 1:return JZ(e,t,n);case 2:return iJ(e,t,n);case 3:return eJ(e,t,n);default:return nJ(e,t,n)}}function UZ(e,t,n){switch(e.length){case 0:return $E();case 1:return QZ(e,t,n);case 2:return oJ(e,t,n);case 3:return tJ(e,t,n);case 4:return aJ(e,t,n);case 5:return rJ(e,t);case 6:return sJ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function GZ(e){return` +`,{getBroadcastDims:BE}=N;function iJ(e,t,n){let a=[];if(e.forEach(c=>{let h=v.sizeFromShape(c.shapeInfo.logicalShape);if(c.shapeInfo.isUniform?a.push(`uniform float ${c.name}${h>1?`[${h}]`:""};`):(a.push(`uniform sampler2D ${c.name};`),a.push(`uniform int offset${c.name};`)),n.enableShapeUniforms){let{uniformShape:m}=K1(n.packedInputs,c.shapeInfo.logicalShape,c.shapeInfo.texShape);switch(m.length){case 1:a.push(`uniform int ${c.name}Shape;`);break;case 2:a.push(`uniform ivec2 ${c.name}Shape;`);break;case 3:a.push(`uniform ivec3 ${c.name}Shape;`);break;case 4:a.push(`uniform ivec4 ${c.name}Shape;`);break;default:break}a.push(`uniform ivec2 ${c.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:a.push("uniform int outShape;");break;case 2:a.push("uniform ivec2 outShape;"),a.push("uniform int outShapeStrides;");break;case 3:a.push("uniform ivec3 outShape;"),a.push("uniform ivec2 outShapeStrides;");break;case 4:a.push("uniform ivec4 outShape;"),a.push("uniform ivec3 outShapeStrides;");break;default:break}a.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(c=>{a.push(`uniform ${c.type} ${c.name}${c.arrayIndex?`[${c.arrayIndex}]`:""};`)});let r=a.join(` +`),s=e.map(c=>oJ(c,t,n.packedInputs,n.enableShapeUniforms)).join(` +`),i=t.texShape,o=En(),l=pJ(o),u,p,d=hJ(o);return t.isPacked?(u=lJ(t.logicalShape,i,n.enableShapeUniforms),p=dJ(o)):(u=uJ(t.logicalShape,i,n.enableShapeUniforms),p=cJ(o)),n.packedInputs&&(d+=bJ),[d,l,p,r,u,s,n.userCode].join(` +`)}function xp(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return EJ(e,t);case 1:return FJ(e,t);case 2:return DJ(e,t);case 3:return MJ(e,t);case 4:return OJ(e,t);case 5:return LJ(e);case 6:return zJ(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function VE(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return _J(e);case 1:return AJ(e,t);case 2:return $J(e,t);case 3:return RJ(e,t);default:return PJ(e,t)}}function oJ(e,t,n=!1,a){let r="";n?r+=VE(e,a):r+=xp(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=WJ(e,t):r+=BJ(e,t)),r}function lJ(e,t,n){switch(e.length){case 0:return UE();case 1:return yJ(e,t,n);case 2:return TJ(e,t,n);case 3:return vJ(e,t,n);default:return kJ(e,t,n)}}function uJ(e,t,n){switch(e.length){case 0:return UE();case 1:return xJ(e,t,n);case 2:return CJ(e,t,n);case 3:return wJ(e,t,n);case 4:return IJ(e,t,n);case 5:return SJ(e,t);case 6:return NJ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function pJ(e){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${e.texture2D}(textureSampler, uv).r; } - `}function HZ(e){return` + `}function cJ(e){return` void setOutput(float val) { ${e.output} = vec4(val, 0, 0, 0); } - `}function qZ(e){return` + `}function dJ(e){return` void setOutput(vec4 val) { ${e.output} = val; } - `}function jZ(e){return`${e.version} + `}function hJ(e){return`${e.version} precision highp float; precision highp int; precision highp sampler2D; @@ -225,10 +229,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return fract((p3.x + p3.y) * p3.z); } - ${KZ} - ${XZ} - ${YZ} - `}var KZ=` + ${mJ} + ${fJ} + ${gJ} + `}var mJ=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -240,7 +244,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,XZ=` +`,fJ=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -248,7 +252,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,YZ=` +`,gJ=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -257,7 +261,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,ZZ=` +`,bJ=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -268,11 +272,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function $E(){return` +`;function UE(){return` int getOutputCoords() { return 0; } - `}function JZ(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?` + `}function yJ(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } @@ -301,7 +305,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${a[0]}, ${a[1]})); return 2 * (resTexRC.x * ${a[1]} + resTexRC.y); } - `}function QZ(e,t,n){return t[0]===1?n?` + `}function xJ(e,t,n){return t[0]===1?n?` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } @@ -329,7 +333,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${t[0]}, ${t[1]})); return resTexRC.x * ${t[1]} + resTexRC.y; } - `}function eJ(e,t,n){if(n)return` + `}function vJ(e,t,n){if(n)return` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); @@ -360,15 +364,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `}function tJ(e,t,n){if(n)return` + `}function wJ(e,t,n){if(n)return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Yf(["r","c","d"],e)} + ${Zf(["r","c","d"],e)} return ivec3(r, c, d); } -`;let a=Ho(["r","c","d"],e);return` +`;let a=jo(["r","c","d"],e);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -376,7 +380,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a} return ivec3(r, c, d); } - `}function nJ(e,t,n){if(n)return` + `}function kJ(e,t,n){if(n)return` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -417,15 +421,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec${e.length}(${l}); } - `}function aJ(e,t,n){if(n)return` + `}function IJ(e,t,n){if(n)return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Yf(["r","c","d","d2"],e)} + ${Zf(["r","c","d","d2"],e)} return ivec4(r, c, d, d2); } - `;let a=Ho(["r","c","d","d2"],e);return` + `;let a=jo(["r","c","d","d2"],e);return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -433,7 +437,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a} return ivec4(r, c, d, d2); } - `}function rJ(e,t){let n=Ho(["r","c","d","d2","d3"],e);return` + `}function SJ(e,t){let n=jo(["r","c","d","d2","d3"],e);return` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -445,7 +449,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } - `}function sJ(e,t){let n=Ho(["r","c","d","d2","d3","d4"],e);return` + `}function NJ(e,t){let n=jo(["r","c","d","d2","d3","d4"],e);return` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -456,7 +460,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec6 result = ivec6(r, c, d, d2, d3, d4); return result; } - `}function iJ(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?` + `}function TJ(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); @@ -489,7 +493,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec2(r, c); } - `}function oJ(e,t,n){return v.arraysEqual(e,t)?n?` + `}function CJ(e,t,n){return v.arraysEqual(e,t)?n?` ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); } @@ -543,15 +547,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${e[1]}; return ivec2(r, c); } - `}function qo(e){return`offset${e}`}function lJ(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=En();return` + `}function Ko(e){return`offset${e}`}function _J(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=En();return` vec4 ${n}() { return ${a.texture2D}(${t}, halfCR); } - `}function uJ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${a}() {return ${n};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return` + `}function EJ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${a}() {return ${n};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return` float ${a}() { return sampleTexture(${n}, halfCR); } - `;let i=qo(n);if(t)return` + `;let i=Ko(n);if(t)return` float ${a}() { vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i}); return sampleTexture(${n}, uv); @@ -561,7 +565,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${o}, ${l}, ${i}); return sampleTexture(${n}, uv); } - `}function pJ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=En();if(t)return` + `}function AJ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=En();if(t)return` vec4 ${a}(int index) { ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( @@ -574,7 +578,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${i[0]}, ${i[1]}, index); return ${s.texture2D}(${n}, uv); } - `}function cJ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return` + `}function FJ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return` float ${a}(int index) { ${vp(e)} } @@ -582,7 +586,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float ${a}(int index) { return sampleTexture(${n}, halfCR); } - `;let o=qo(n);return i===1?t?` + `;let o=Ko(n);return i===1?t?` float ${a}(int index) { vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0])); return sampleTexture(${n}, uv); @@ -612,7 +616,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${s}, ${i}, index + ${o}); return sampleTexture(${n}, uv); } - `}function dJ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=En();if(s!=null&&v.arraysEqual(n,s))return t?` + `}function $J(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=En();if(s!=null&&v.arraysEqual(n,s))return t?` vec4 ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]); @@ -636,7 +640,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = packedUVfrom2D(${p}, ${u[0]}, ${u[1]}, row, col); return ${l.texture2D}(${a}, uv); } - `}function hJ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&v.arraysEqual(n,s)){if(t)return` + `}function DJ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&v.arraysEqual(n,s)){if(t)return` float ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]); return sampleTexture(${a}, uv); @@ -656,7 +660,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int index = round(dot(vec2(row, col), vec2(${n[1]}, 1))); ${vp(e)} } - `;let u=s[0],p=s[1],d=qo(a);return p===1?t?` + `;let u=s[0],p=s[1],d=Ko(a);return p===1?t?` float ${r}(int row, int col) { float index = dot(vec3(row, col, ${d}), vec3(${a}Shape[1], 1, 1)); vec2 uv = vec2(0.5, (index + 0.5) / float(${a}TexShape[0])); @@ -694,8 +698,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${u}, ${p}, index); return sampleTexture(${a}, uv); } -`}function mJ(e,t){let n=e.shapeInfo.logicalShape,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)];if(n[0]===1){let c=n.slice(1),h=[1,2],m=wp(e,c),f=["b","row","col"];return` - ${FE(m,t)} +`}function RJ(e,t){let n=e.shapeInfo.logicalShape,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)];if(n[0]===1){let c=n.slice(1),h=[1,2],m=wp(e,c),f=["b","row","col"];return` + ${VE(m,t)} vec4 ${r}(int b, int row, int col) { return ${r}(${kp(f,h)}); } @@ -714,7 +718,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${l}, ${u}, ${d}, ${p}, b, row, col); return ${o.texture2D}(${a}, uv); } - `}function fJ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[1]*n[2],i=n[2],{newShape:o,keptDims:l}=v.squeezeShape(n),u=o;if(u.length=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(` + `}function WJ(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=BE(e.shapeInfo.logicalShape,t.logicalShape),l=dt(i),u=i-s,p,d=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(` `);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((g,b)=>`coords.${d[b+u]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,f=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=` return vec4(outputValue.xy, outputValue.xy); `;else if(m&&!f)i===1?h=` @@ -973,20 +977,20 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 outputValue = get${a}(${c}); ${h} } - `}function wJ(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&&v.arraysEqual(i,s))return` + `}function BJ(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&&v.arraysEqual(i,s))return` float ${r}() { return sampleTexture(${n}, resultUV); } - `;let u=dt(l),p=AE(e.shapeInfo.logicalShape,t.logicalShape),d=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&p.length>=1?c="coords = 0;":c=p.map(f=>`coords.${h[f+d]} = 0;`).join(` + `;let u=dt(l),p=BE(e.shapeInfo.logicalShape,t.logicalShape),d=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&p.length>=1?c="coords = 0;":c=p.map(f=>`coords.${h[f+d]} = 0;`).join(` `);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+d]}`).join(", "),` float ${r}() { ${u} coords = getOutputCoords(); ${c} return get${a}(${m}); } - `}function dt(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 ${e} is not yet supported`)}function q1(e,t,n){let{newShape:a,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!v.arraysEqual(t,n)&&a.lengthe[n]).join(", ")}function kJ(e,t,n,a){let r=n.map((p,d)=>{let c={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(c.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[d],shapeInfo:c}}),s=r.map(p=>p.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=WZ(r,i,t),l=lE(e.gl,o),u=e.createProgram(l);return G().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},DE(e,t,u))}function DE(e,t,n){let a=[],r=[],s,i,o,l=null,u=null;u=e.getUniformLocation(n,"NAN",!1),G().getNumber("WEBGL_VERSION")===1&&(l=e.getUniformLocation(n,"INFINITY",!1));let p=!1;for(let d of t.variableNames){let c={name:d,uniform:e.getUniformLocation(n,d,p),offset:e.getUniformLocation(n,`offset${d}`,p)};t.enableShapeUniforms&&(c.shape=e.getUniformLocation(n,`${d}Shape`,p),c.texShape=e.getUniformLocation(n,`${d}TexShape`,p)),a.push(c)}if(t.enableShapeUniforms&&(s=e.getUniformLocation(n,"outShape",p),o=e.getUniformLocation(n,"outShapeStrides",p),i=e.getUniformLocation(n,"outTexShape",p)),t.customUniforms)for(let d of t.customUniforms)r.push(e.getUniformLocation(n,d.name,p));return{variablesLocations:a,customUniformLocations:r,infLoc:l,nanLoc:u,outShapeLocation:s,outShapeStridesLocation:o,outTexShapeLocation:i}}function nS(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(!v.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(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function IJ(e,t,n,a,r){t.program.enableShapeUniforms||(nS(t.inShapeInfos,n),nS([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),G().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN);for(let l=0;l{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:d}=q1(e.packedInputs,i.shape,l),c="",h="",m="";if(p.length===1&&e.packedInputs){let I=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];c=`${I[0]>1}_${I[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let I=v.computeStrides(p);m=`${I[0]===l[1]}_${I[I.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),b=v.sizeFromShape(i.shape)===1,y=N.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&f===n.shape.length&&v.arraysEqual(l,n.texData.texShape),w=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${f}_${x}_${u?d:""}_${p.length}_${b}_${y}_${g}_${c}_${h}_${m}_${w}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r+`${G().getNumber("WEBGL_VERSION")}`,s}function vn(e){return G().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var NJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Tc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=En();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` + `}function dt(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 ${e} is not yet supported`)}function K1(e,t,n){let{newShape:a,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!v.arraysEqual(t,n)&&a.lengthe[n]).join(", ")}function VJ(e,t,n,a){let r=n.map((p,d)=>{let c={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(c.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[d],shapeInfo:c}}),s=r.map(p=>p.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=iJ(r,i,t),l=xE(e.gl,o),u=e.createProgram(l);return G().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(e.buildVao(u),Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},GE(e,t,u)))}function GE(e,t,n){let a=[],r=[],s,i,o,l=null,u=null;u=e.getUniformLocation(n,"NAN",!1),G().getNumber("WEBGL_VERSION")===1&&(l=e.getUniformLocation(n,"INFINITY",!1));let p=!1;for(let d of t.variableNames){let c={name:d,uniform:e.getUniformLocation(n,d,p),offset:e.getUniformLocation(n,`offset${d}`,p)};t.enableShapeUniforms&&(c.shape=e.getUniformLocation(n,`${d}Shape`,p),c.texShape=e.getUniformLocation(n,`${d}TexShape`,p)),a.push(c)}if(t.enableShapeUniforms&&(s=e.getUniformLocation(n,"outShape",p),o=e.getUniformLocation(n,"outShapeStrides",p),i=e.getUniformLocation(n,"outTexShape",p)),t.customUniforms)for(let d of t.customUniforms)r.push(e.getUniformLocation(n,d.name,p));return{variablesLocations:a,customUniformLocations:r,infLoc:l,nanLoc:u,outShapeLocation:s,outShapeStridesLocation:o,outTexShapeLocation:i}}function sS(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(!v.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(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function UJ(e,t,n,a,r){t.program.enableShapeUniforms||(sS(t.inShapeInfos,n),sS([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),e.bindVertexArray(t.webGLProgram.vao),G().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN);for(let l=0;l{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:d}=K1(e.packedInputs,i.shape,l),c="",h="",m="";if(p.length===1&&e.packedInputs){let I=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];c=`${I[0]>1}_${I[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let I=v.computeStrides(p);m=`${I[0]===l[1]}_${I[I.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),b=v.sizeFromShape(i.shape)===1,y=N.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&f===n.shape.length&&v.arraysEqual(l,n.texData.texShape),w=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${f}_${x}_${u?d:""}_${p.length}_${b}_${y}_${g}_${c}_${h}_${m}_${w}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r+`${G().getNumber("WEBGL_VERSION")}`,s}function vn(e){return G().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var HJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Tc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=En();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Yf(["r","c","d"],e):Ho(["r","c","d"],e)} + ${this.enableShapeUniforms?Zf(["r","c","d"],e):jo(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1004,9 +1008,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},TJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Tc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=En();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` + `}},qJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Tc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=En();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Yf(["r","c","d"],e):Ho(["r","c","d"],e)} + ${this.enableShapeUniforms?Zf(["r","c","d"],e):jo(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1024,26 +1028,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},CJ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=da.DOWNLOAD;let t=En();this.outputShape=e,this.userCode=` - ${EE} + `}},jJ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ha.DOWNLOAD;let t=En();this.outputShape=e,this.userCode=` + ${WE} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } - `}},_J=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=da.DOWNLOAD;let t=En();this.outputShape=e,this.userCode=` - ${EE} + `}},KJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ha.DOWNLOAD;let t=En();this.outputShape=e,this.userCode=` + ${WE} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } - `}},EJ={R:0,G:1,B:2,A:3},aS=class{constructor(e,t=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let a=En();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let s="";for(let i=0;iUE,createBufferFromOutputTexture:()=>qE,createFloat16MatrixTexture:()=>zE,createFloat16PackedMatrixTexture:()=>VE,createFloat32MatrixTexture:()=>LE,createIndexBuffer:()=>OE,createPackedMatrixTexture:()=>BE,createUnsignedBytesMatrixTexture:()=>WE,createVertexBuffer:()=>PE,createVertexShader:()=>ME,downloadByteEncodedFloatMatrixFromOutputTexture:()=>KE,downloadFloat32MatrixFromBuffer:()=>jE,downloadMatrixFromPackedOutputTexture:()=>YE,downloadPackedMatrixFromBuffer:()=>XE,getInternalFormatForFloat16MatrixTexture:()=>K1,getInternalFormatForFloat16PackedMatrixTexture:()=>Z1,getInternalFormatForFloat32MatrixTexture:()=>j1,getInternalFormatForPackedMatrixTexture:()=>Y1,getInternalFormatForUnsignedBytesMatrixTexture:()=>X1,uploadDenseMatrixToTexture:()=>GE,uploadPixelDataToTexture:()=>HE});function ME(e){let t=En(),n=`${t.version} + `}},HE={};Ee(HE,{bindVertexProgramAttributeStreams:()=>eA,createBufferFromOutputTexture:()=>aA,createFloat16MatrixTexture:()=>YE,createFloat16PackedMatrixTexture:()=>QE,createFloat32MatrixTexture:()=>XE,createIndexBuffer:()=>KE,createPackedMatrixTexture:()=>JE,createUnsignedBytesMatrixTexture:()=>ZE,createVertexBuffer:()=>jE,createVertexShader:()=>qE,downloadByteEncodedFloatMatrixFromOutputTexture:()=>sA,downloadFloat32MatrixFromBuffer:()=>rA,downloadMatrixFromPackedOutputTexture:()=>oA,downloadPackedMatrixFromBuffer:()=>iA,getInternalFormatForFloat16MatrixTexture:()=>Y1,getInternalFormatForFloat16PackedMatrixTexture:()=>Q1,getInternalFormatForFloat32MatrixTexture:()=>X1,getInternalFormatForPackedMatrixTexture:()=>J1,getInternalFormatForUnsignedBytesMatrixTexture:()=>Z1,uploadDenseMatrixToTexture:()=>tA,uploadPixelDataToTexture:()=>nA});function qE(e){let t=En(),n=`${t.version} precision highp float; ${t.attribute} vec3 clipSpacePos; ${t.attribute} vec2 uv; @@ -1115,7 +1119,7 @@ vec2 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de(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function iA(e,t,n,a,r,s,i,o){let l=e,u=new Float32Array(jZ(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function oA(e,t,n){let a=new Float32Array(t*n*4);return de(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var Yh=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=G().getNumber("WEBGL_VERSION");if(e!=null?(this.gl=e,fE(t,e)):this.gl=Ka(t),e=this.gl,G().getNumber("WEBGL_VERSION")===2){let r=e;this.createVertexArray=()=>de(r,()=>r.createVertexArray()),this.bindVertexArray=s=>de(r,()=>r.bindVertexArray(s)),this.deleteVertexArray=s=>de(r,()=>r.deleteVertexArray(s)),this.getVertexArray=()=>de(r,()=>r.getParameter(r.VERTEX_ARRAY_BINDING))}else if(e!=null){let r=e.getExtension("OES_vertex_array_object");if(r==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>de(e,()=>r.createVertexArrayOES()),this.bindVertexArray=s=>de(e,()=>r.bindVertexArrayOES(s)),this.deleteVertexArray=s=>de(e,()=>r.deleteVertexArrayOES(s)),this.getVertexArray=()=>de(e,()=>e.getParameter(r.VERTEX_ARRAY_BINDING_OES))}let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),G().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=sc(this.gl,r),ma(this.gl,s))this.textureHalfFloatExtension=sc(this.gl,s);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(n),ma(this.gl,a))this.colorBufferHalfFloatExtension=sc(this.gl,a);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(n="EXT_color_buffer_float",ma(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(ma(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=jE(this.gl),this.indexBuffer=KE(this.gl),this.framebuffer=TE(this.gl),this.textureConfig=G1(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. 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;de(e,()=>e.finish()),de(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),de(e,()=>e.deleteFramebuffer(this.framebuffer)),de(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),de(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),de(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),XE(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),YE(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),ZE(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),nA(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),tA(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),QE(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),JE(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(sv(this.gl,this.framebuffer),this.outputTexture=null),de(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>sA(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return iA(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return rA(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=aA(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(G().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 G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>oA(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=qE(t));let n=vE(t);de(t,()=>t.attachShader(n,this.vertexShader)),de(t,()=>t.attachShader(n,e)),wE(t,n);let a=Object.assign(n,{vao:this.createVertexArray()});return this.debug&&qh(t,a),a}buildVao(e){this.setProgram(e),this.bindVertexArray(e.vao);let t=this.gl;de(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),eA(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(de(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&qh(this.gl,this.program),de(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?_E(this.gl,e,t):EE(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),de(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(),AE(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=bp(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&&qh(this.gl,this.program),ic(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}de(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),de(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=sc(this.gl,G().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(G().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(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,G().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=ZJ(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){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let n;"setTimeoutCustom"in G().platform&&(n=G().platform.setTimeoutCustom.bind(G().platform)),v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),jh(this.gl,e,this.framebuffer),this.debug&&ic(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(jh(this.gl,this.outputTexture,this.framebuffer),this.debug&&ic(this.gl)):sv(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;jh(a,e,this.framebuffer),this.debug&&ic(a),this.outputTexture=e,de(a,()=>a.viewport(0,0,t,n)),de(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),de(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 ZJ(e){let t=0;for(;t`${e}.${n}`)}function Sn(e,t){return t===1?[e]:cA(e,t)}function V9(e,t){if(e===1)return"rc";let n="";for(let a=0;a= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), cEdge ? 0. : getA(${t[1]}), rEdge ? 0. : getA(${t[2]}), - rEdge || cEdge ? 0. : getA(${t[3]})`}},tA=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2===1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=` + rEdge || cEdge ? 0. : getA(${t[3]})`}},dA=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);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); @@ -1154,8 +1158,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${a>0?"}":""} `}this.userCode=` - ${S9(t,this.enableShapeUniforms)} - ${this.enableShapeUniforms?H1():G1(e)} + ${G9(t,this.enableShapeUniforms)} + ${this.enableShapeUniforms?j1():q1(e)} void main() { ivec3 rc = getOutputCoords(); @@ -1170,12 +1174,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function S9(e,t){return` + `}};function G9(e,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?zZ(["r","c","d"],"inputShape"):Ho(["r","c","d"],e)} + ${t?sJ(["r","c","d"],"inputShape"):jo(["r","c","d"],e)} return ivec3(r, c, d); } - `}var N9=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){let a=sS(t,n),r=iS(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=rS(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return a===hn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===hn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===hn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===hn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===hn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=sS(n,a),s=iS(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=rS(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=G().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function T9(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;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function rS(e,t,n,a,r){let s=C9(t,a),i;if(r){let[l,u]=bp(e[0],e[1]);i=l*u}else{let[l,u]=Ed(e[0],e[1]);i=l*u}let o=T9(n,s);return i*o}function C9(e,t){switch(e){case hn.PACKED_2X2_FLOAT32:return Y1(t);case hn.PACKED_2X2_FLOAT16:return Z1(t);case hn.UNPACKED_FLOAT32:return j1(t);case hn.UNPACKED_FLOAT16:return K1(t);case hn.PACKED_4X1_UNSIGNED_BYTE:return X1(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function _9(e){return G().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?hn.PACKED_2X2_FLOAT32:hn.UNPACKED_FLOAT32:e?hn.PACKED_2X2_FLOAT16:hn.UNPACKED_FLOAT16}function sS(e,t){if(e===da.UPLOAD)return hn.PACKED_2X2_FLOAT32;if(e===da.RENDER||e==null)return _9(t);if(e===da.DOWNLOAD||e===da.PIXELS)return hn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function iS(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ir=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` + `}var H9=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){let a=lS(t,n),r=uS(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=oS(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return a===hn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===hn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===hn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===hn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===hn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=lS(n,a),s=uS(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=oS(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=G().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function q9(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;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function oS(e,t,n,a,r){let s=j9(t,a),i;if(r){let[l,u]=bp(e[0],e[1]);i=l*u}else{let[l,u]=Ed(e[0],e[1]);i=l*u}let o=q9(n,s);return i*o}function j9(e,t){switch(e){case hn.PACKED_2X2_FLOAT32:return J1(t);case hn.PACKED_2X2_FLOAT16:return Q1(t);case hn.UNPACKED_FLOAT32:return X1(t);case hn.UNPACKED_FLOAT16:return Y1(t);case hn.PACKED_4X1_UNSIGNED_BYTE:return Z1(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function K9(e){return G().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?hn.PACKED_2X2_FLOAT32:hn.UNPACKED_FLOAT32:e?hn.PACKED_2X2_FLOAT16:hn.UNPACKED_FLOAT16}function lS(e,t){if(e===ha.UPLOAD)return hn.PACKED_2X2_FLOAT32;if(e===ha.RENDER||e==null)return K9(t);if(e===ha.DOWNLOAD||e===ha.PIXELS)return hn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function uS(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ir=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } @@ -1186,11 +1190,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},Oa="if (isnan(x)) return x;",E9="return x;",oS="return abs(x);",A9="return (x >= 0.0) ? x : (exp(x) - 1.0);",F9=Oa+` + `}},Oa="if (isnan(x)) return x;",X9="return x;",pS="return abs(x);",Y9="return (x >= 0.0) ? x : (exp(x) - 1.0);",Z9=Oa+` return (x < 0.0) ? 0.0 : x; -`,$9=Oa+` +`,J9=Oa+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,es="return x;",D9="return 1.0 / (1.0 + exp(-1.0 * x));",R9="return x;",M9=` +`,ts="return x;",Q9="return 1.0 / (1.0 + exp(-1.0 * x));",eQ="return x;",tQ=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1199,7 +1203,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,P9=` +`,nQ=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1209,7 +1213,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,O9=` +`,aQ=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1219,7 +1223,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,L9="return 1.0 / (1.0 + exp(-1.0 * x));",ss=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` +`,rQ="return 1.0 / (1.0 + exp(-1.0 * x));",is=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } @@ -1230,17 +1234,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},z9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.length,n=Sn("rc",t),a=dt(t),r=k9(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` + `}},sQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.length,n=Sn("rc",t),a=dt(t),r=V9(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})); } - `}},W9=fr.whereImpl,B9=1e-7,V9=1e-4,fx={};function U9(e){return e in fx||(fx[e]={}),fx[e]}var G9=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),H9=600;function q9(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*H9/1024/1024}var Zf=class extends $c{nextDataId(){return Zf.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Yh)t=e;else{let n=Ka(G().getNumber("WEBGL_VERSION"),e);t=new Yh(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Ka(G().getNumber("WEBGL_VERSION"));t=new Yh(n),this.binaryCache=U9(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new N9(this.gpgpu),this.numMBBeforeWarning=q9(),this.texData=new Em(this,Ea())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,n,a,r,s){let i=this.makeTensorInfo(t,n),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[a,r]},o.texShape=[a,r];let l=oc(t),u=new aS(l,!1,s),p=this.runWebGLProgram(u,[i],n,[[a,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,n){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().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:da.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(G().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:da.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 d;o?d=new ss(i,es):d=new ir(i,es);let c=this.runWebGLProgram(d,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(a==="complex64"){let d=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);p=N.mergeRealAndImagArrays(d,c)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}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 ss(a,es):h=new ir(a,es);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(G().getBool("DEBUG")&&!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 l=null,u;if(s!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...Wh(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];p=N.mergeRealAndImagArrays(m,f)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(a);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;de(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,p),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ea().removeDataId(e,this),this.pendingDeletes--),d}readToGPU(e,t={}){let n=this.texData.get(e),{values:a,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let c;o?c=new ss(r,es):c=new ir(r,es);let h=this.runWebGLProgram(c,[{dataId:e,shape:r,dtype:i}],i),m=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),m}if(l==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=Ea().makeTensorFromTensorInfo(u),d=this.texData.get(u.dataId);return Object.assign({tensorRef:p},d.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(a=>v.decodeString(a));return ze(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return ze(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}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=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.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,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 u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=G9){return G().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)0&&v.isString(n[0])){let r=n.map(s=>v.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){return Ea().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new z9(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new I9(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[yi(e.shape),...xi(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[yi(t),...xi(t)],s=new tA(r,n),i=!0,o=[n],l=this.runWebGLProgram(s,[a],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:a,shape:r,dtype:s}=n;if(t!=null){let d=v.sizeFromShape(r),c=t[0]*t[1]*4;v.assert(d<=c,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=oc(r),o;a?o=new TJ(i):o=new NJ(i);let l=!0,u=[t!=null?t:Wh(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,n,a,r=!1,s){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===Tc.DENSE){let g=s!=null?s:Wh(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!Cc(b.shape,g.shape)){let y=g,x=g.shape;g.shape=b.shape,g=this.packedReshape(g,x),l.push(g),b=this.texData.get(g.dataId),y.shape=x}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},d=SJ(e,u,p),c=this.getAndSaveBinary(d,()=>kJ(this.gpgpu,e,u,p)),h=this.activeTimers!=null,m;h&&(m=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||IJ(this.gpgpu,c,u,p,a),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=G().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=v.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}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||(G().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=P(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=G().getBool("DEBUG");G().set("DEBUG",!1);let t=this.abs(ve(1e-8)).dataSync()[0];if(G().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?B9:V9}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,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=kE(n,o),t.texShape=p),r!=null){let d=oc(n),c,h=p[1],m=p[0],f=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!f)&&([h,m]=bp(p[0],p[1])),o?c=new AJ(d,f):c=new aS(d,f);let g=f?[m,h]:p,b=this.makeTensorInfo(g,a),y=this.texData.get(b.dataId);f?y.usage=da.PIXELS:y.usage=da.UPLOAD,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,m,r);let x=[[m,h]],w=!0,I=this.runWebGLProgram(c,[b],a,x,w),T=this.texData.get(I.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=v.now()-u)}else{let d=this.acquireTexture(p,i,a,o);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return t!=null&&(n.values=j9(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]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(a=>{try{this.checkCompletion_(t),a(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await Gw(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(U1(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){let{variablesLocations:t,customUniformLocations:n,infLoc:a,nanLoc:r,outShapeLocation:s,outShapeStridesLocation:i,outTexShapeLocation:o}=DE(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=n,e.infLoc=a,e.nanLoc=r,e.outShapeLocation=s,e.outShapeStridesLocation=i,e.outTexShapeLocation=o}}createTensorFromGPUData(e,t,n){e.channels=e.channels||"RGBA";let{texture:a,height:r,width:s,channels:i}=e,o=Ea().backend;if(!o.gpgpu.gl.isTexture(a))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(a,t,n,r,s,i);return Ea().makeTensorFromDataId(l,t,n,o)}};Zf.nextDataId=0;function j9(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;anew Zf,2);var X9={forceHalfFloat:nA},Q1=` + `}},iQ=fr.whereImpl,oQ=1e-7,lQ=1e-4,gx={};function uQ(e){return e in gx||(gx[e]={}),gx[e]}var pQ=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),cQ=600;function dQ(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*cQ/1024/1024}var Jf=class extends $c{nextDataId(){return Jf.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Yh)t=e;else{let n=Ka(G().getNumber("WEBGL_VERSION"),e);t=new Yh(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Ka(G().getNumber("WEBGL_VERSION"));t=new Yh(n),this.binaryCache=uQ(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new H9(this.gpgpu),this.numMBBeforeWarning=dQ(),this.texData=new Em(this,Aa())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,n,a,r,s){let i=this.makeTensorInfo(t,n),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[a,r]},o.texShape=[a,r];let l=oc(t),u=new iS(l,!1,s),p=this.runWebGLProgram(u,[i],n,[[a,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,n){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().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:ha.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(G().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:ha.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 d;o?d=new is(i,ts):d=new ir(i,ts);let c=this.runWebGLProgram(d,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(a==="complex64"){let d=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);p=N.mergeRealAndImagArrays(d,c)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}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 is(a,ts):h=new ir(a,ts);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(G().getBool("DEBUG")&&!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 l=null,u;if(s!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...Wh(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];p=N.mergeRealAndImagArrays(m,f)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(a);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;de(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,p),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Aa().removeDataId(e,this),this.pendingDeletes--),d}readToGPU(e,t={}){let n=this.texData.get(e),{values:a,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let c;o?c=new is(r,ts):c=new ir(r,ts);let h=this.runWebGLProgram(c,[{dataId:e,shape:r,dtype:i}],i),m=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),m}if(l==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=Aa().makeTensorFromTensorInfo(u),d=this.texData.get(u.dataId);return Object.assign({tensorRef:p},d.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(a=>v.decodeString(a));return ze(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return ze(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}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=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.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,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 u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=pQ){return G().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)0&&v.isString(n[0])){let r=n.map(s=>v.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){return Aa().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new sQ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new U9(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[vi(e.shape),...wi(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[vi(t),...wi(t)],s=new dA(r,n),i=!0,o=[n],l=this.runWebGLProgram(s,[a],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:a,shape:r,dtype:s}=n;if(t!=null){let d=v.sizeFromShape(r),c=t[0]*t[1]*4;v.assert(d<=c,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=oc(r),o;a?o=new qJ(i):o=new HJ(i);let l=!0,u=[t!=null?t:Wh(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,n,a,r=!1,s){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===Tc.DENSE){let g=s!=null?s:Wh(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!Cc(b.shape,g.shape)){let y=g,x=g.shape;g.shape=b.shape,g=this.packedReshape(g,x),l.push(g),b=this.texData.get(g.dataId),y.shape=x}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},d=GJ(e,u,p),c=this.getAndSaveBinary(d,()=>VJ(this.gpgpu,e,u,p)),h=this.activeTimers!=null,m;h&&(m=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||UJ(this.gpgpu,c,u,p,a),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=G().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=v.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}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||(G().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=P(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=G().getBool("DEBUG");G().set("DEBUG",!1);let t=this.abs(ve(1e-8)).dataSync()[0];if(G().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?oQ:lQ}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,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=DE(n,o),t.texShape=p),r!=null){let d=oc(n),c,h=p[1],m=p[0],f=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!f)&&([h,m]=bp(p[0],p[1])),o?c=new YJ(d,f):c=new iS(d,f);let g=f?[m,h]:p,b=this.makeTensorInfo(g,a),y=this.texData.get(b.dataId);f?y.usage=ha.PIXELS:y.usage=ha.UPLOAD,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,m,r);let x=[[m,h]],w=!0,I=this.runWebGLProgram(c,[b],a,x,w),T=this.texData.get(I.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=v.now()-u)}else{let d=this.acquireTexture(p,i,a,o);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return t!=null&&(n.values=hQ(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]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(a=>{try{this.checkCompletion_(t),a(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await Hw(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(H1(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);let{variablesLocations:t,customUniformLocations:n,infLoc:a,nanLoc:r,outShapeLocation:s,outShapeStridesLocation:i,outTexShapeLocation:o}=GE(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=n,e.infLoc=a,e.nanLoc=r,e.outShapeLocation=s,e.outShapeStridesLocation=i,e.outTexShapeLocation=o}}createTensorFromGPUData(e,t,n){e.channels=e.channels||"RGBA";let{texture:a,height:r,width:s,channels:i}=e,o=Aa().backend;if(!o.gpgpu.gl.isTexture(a))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(a,t,n,r,s,i);return Aa().makeTensorFromDataId(l,t,n,o)}};Jf.nextDataId=0;function hQ(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;anew Jf,2);var fQ={forceHalfFloat:hA},tk=` if (isnan(a)) return a; if (isnan(b)) return b; -`,tu=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` +`,nu=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -1250,7 +1254,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}},jo=` + `}},Xo=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; @@ -1299,13 +1303,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function aa(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 Y9={kernelName:Yi,backendName:"webgl",kernelFunc:aa};function Ds(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=aa({inputs:{x:a},backend:n}),l=aa({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var Z9={kernelName:Dm,backendName:"webgl",kernelFunc:Ds},aA="return (a < 0.) ? b * a : a;",rA=` + `}};function ra(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 gQ={kernelName:Ji,backendName:"webgl",kernelFunc:ra};function Rs(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=ra({inputs:{x:a},backend:n}),l=ra({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var bQ={kernelName:Rm,backendName:"webgl",kernelFunc:Rs},mA="return (a < 0.) ? b * a : a;",fA=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function J9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Fd(rA,r.shape,i.shape):new tu(aA,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var Q9={kernelName:eo,backendName:"webgl",kernelFunc:J9},sA="return (a < 0.) ? b * a : a;",iA=` +`;function yQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Fd(fA,r.shape,i.shape):new nu(mA,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var xQ={kernelName:no,backendName:"webgl",kernelFunc:yQ},gA="return (a < 0.) ? b * a : a;",bA=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function eQ(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Fd(iA,a.shape,r.shape):new tu(sA,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var tQ={kernelName:go,backendName:"webgl",kernelFunc:eQ},Ip="if (isnan(x)) return x;";function Ye({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 d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=G().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new ss(i.shape,t):p=new ir(i.shape,e),o.runWebGLProgram(p,[i],l)}}function fn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,b]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[w,I]=x,T={dataId:w.dataId,dtype:w.dtype,shape:l.shape},C={dataId:I.dataId,dtype:I.dtype,shape:u.shape},E=new tu(e,l.shape,u.shape);return p.runWebGLProgram(E,[T,C],ga(w.dtype,I.dtype))}),y=Ds({inputs:{real:g,imag:b},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(b),y}let d=s||ga(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(m):m,b=l.dtype==="string"?N.fromUint8ToStringArray(f):f,[y,x]=r(l.shape,u.shape,g,b,d),w=p.makeTensorInfo(x,d),I=p.texData.get(w.dataId);return I.values=y,w}let c=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Fd(t,l.shape,u.shape,n):h=new tu(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function _c(e,t=!1){if(e==="linear")return t?R9:E9;if(e==="relu")return t?P9:F9;if(e==="elu")return t?M9:A9;if(e==="relu6")return t?O9:$9;if(e==="prelu")return t?iA:sA;if(e==="leakyrelu")return t?rA:aA;if(e==="sigmoid")return t?L9:D9;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var oA=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,this.enableShapeUniforms=vn(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=a?"i * 2, rc.y":"rc.y, i * 2",c=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 vQ(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Fd(bA,a.shape,r.shape):new nu(gA,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var wQ={kernelName:yo,backendName:"webgl",kernelFunc:vQ},Ip="if (isnan(x)) return x;";function Ye({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 d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=G().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new is(i.shape,t):p=new ir(i.shape,e),o.runWebGLProgram(p,[i],l)}}function fn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,b]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[w,I]=x,T={dataId:w.dataId,dtype:w.dtype,shape:l.shape},C={dataId:I.dataId,dtype:I.dtype,shape:u.shape},E=new nu(e,l.shape,u.shape);return p.runWebGLProgram(E,[T,C],ba(w.dtype,I.dtype))}),y=Rs({inputs:{real:g,imag:b},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(b),y}let d=s||ba(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(m):m,b=l.dtype==="string"?N.fromUint8ToStringArray(f):f,[y,x]=r(l.shape,u.shape,g,b,d),w=p.makeTensorInfo(x,d),I=p.texData.get(w.dataId);return I.values=y,w}let c=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Fd(t,l.shape,u.shape,n):h=new nu(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function _c(e,t=!1){if(e==="linear")return t?eQ:X9;if(e==="relu")return t?nQ:Z9;if(e==="elu")return t?tQ:Y9;if(e==="relu6")return t?aQ:J9;if(e==="prelu")return t?bA:gA;if(e==="leakyrelu")return t?fA:mA;if(e==="sigmoid")return t?rQ:Q9;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var yA=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,this.enableShapeUniforms=vn(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=a?"i * 2, rc.y":"rc.y, i * 2",c=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) { @@ -1344,7 +1348,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}},lS={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},uS=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=` + `}},cS={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},dS=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=` float binaryOpComplex( float areal, float aimag, float breal, float bimag) { ${e} @@ -1357,7 +1361,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float bimag = getBImagAtOutCoords(); setOutput(binaryOpComplex(areal, aimag, breal, bimag)); } - `}},pS="return a * b;";function ek(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=N.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new uS(lS.REAL,a.shape,r.shape),p=new uS(lS.IMAG,a.shape,r.shape),d=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Ds({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[u,p]=JJ(a.shape,r.shape,o.values,l.values,s),d=n.makeTensorInfo(p,s),c=n.texData.get(d.dataId);return c.values=u,d}let i;return G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Fd(pS,a.shape,r.shape):i=new tu(pS,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var nQ={kernelName:co,backendName:"webgl",kernelFunc:ek};function aQ(e,t,n){let a=[yi(e.shape),...xi(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[yi(t),...xi(t)],i=new tA(s,a),o=!0,l=[a],u=n.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ce(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!Cc(r.shape,l)&&!(p.texture!==null&&Cc(p.shape,l))?aQ(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var rQ={kernelName:Vu,backendName:"webgl",kernelFunc:ce},cS=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 p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%n>0&&(u=` + `}},hS="return a * b;";function nk(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=N.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new dS(cS.REAL,a.shape,r.shape),p=new dS(cS.IMAG,a.shape,r.shape),d=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Rs({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[u,p]=y9(a.shape,r.shape,o.values,l.values,s),d=n.makeTensorInfo(p,s),c=n.texData.get(d.dataId);return c.values=u,d}let i;return G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Fd(hS,a.shape,r.shape):i=new nu(hS,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var kQ={kernelName:mo,backendName:"webgl",kernelFunc:nk};function IQ(e,t,n){let a=[vi(e.shape),...wi(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[vi(t),...wi(t)],i=new dA(s,a),o=!0,l=[a],u=n.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ce(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!Cc(r.shape,l)&&!(p.texture!==null&&Cc(p.shape,l))?IQ(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var SQ={kernelName:Uu,backendName:"webgl",kernelFunc:ce},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 p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%n>0&&(u=` if (inIdx < 0 || inIdx >= ${r}) { return 0.0; } @@ -1410,7 +1414,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(sumValue); } - `}},sQ=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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 u=Math.floor(n/4)*4,p=n%4,d=` + `}},NQ=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}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,p=n%4,d=` if (${t==="sum"}) { sumValue += dot(values, ones); } else if (${t==="prod"}) { @@ -1502,12 +1506,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function iQ(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=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function Ko(e,t,n,a){let r=iQ(e.shape),s=e;for(let i=0;i6)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;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=dt(this.rank),r=eA("rc",this.rank),s=new Array(this.rank);for(let u=0;u6)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;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=dt(this.rank),r=cA("rc",this.rank),s=new Array(this.rank);for(let u=0;u`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let w=n?[b,d,h]:[b,h,d],I=a?[y,m,c]:[y,c,m],T=ce({inputs:{x:e},backend:r,attrs:{shape:w}}),C=ce({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[T,C],F=Math.max(b,y),D=n?T.shape[1]:T.shape[2],$=s!=null,S=i!=null,M=l==="leakyrelu",B=l!=null?_c(l,!0):null,U=$||S||M||B!=null,H;if((h===1||m===1)&&D>lA&&U===!1){let K=T,Z=C;n&&(K=Nn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),E.push(K)),a&&(Z=Nn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(Z));let J=m!==1,ee=m===1,ae=K;J&&(ae=ce({inputs:{x:K},backend:r,attrs:{shape:[F,D,1]}}),E.push(ae));let te=m===1?2:1,re=Z;ee&&(re=ce({inputs:{x:Z},backend:r,attrs:{shape:[F,1,D]}}),E.push(re));let se=ek({inputs:{a:ae,b:re},backend:r});H=Qf({inputs:{x:se},backend:r,attrs:{axis:te,keepDims:!0}}),E.push(se)}else{let K=ga(e.dtype,t.dtype),Z=new oA(w,I,[F,h,m],n,a,$,B,S,M),J=[T,C];if(s!=null&&J.push(s),S&&J.push(i),M){let ee=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));J.push(ee),E.push(ee)}H=r.runWebGLProgram(Z,J,K)}let j=ce({inputs:{x:H},backend:r,attrs:{shape:x}});E.push(H);for(let K of E)r.disposeIntermediateTensorInfo(K);return j}function hQ(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return Sm({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var mQ={kernelName:ri,backendName:"webgl",kernelFunc:hQ},dS="return abs(x);";function fQ(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=JE(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ss(a.shape,dS):r=new ir(a.shape,dS),n.runWebGLProgram(r,[a],a.dtype)}var gQ={kernelName:au,backendName:"webgl",kernelFunc:fQ},bQ=Oa+` + `}};function Qf(e,t,n){let a=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new EQ(e.shape,t):new CQ(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function AQ(e,t,n,a){let r=t,s=e.shape.length,i=v.parseAxisParam(r,e.shape),o=i,l=N.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=Qf(e,l,a),o=N.getInnerMostAxes(o.length,s)),N.assertAxesAreInnerMostDims("sum",o,s);let[d,c]=N.computeOutAndReduceShapes(p.shape,o),h=d;n&&(h=N.expandShapeToKeepDim(d,i));let m=v.sizeFromShape(c),f=v.sizeFromShape(e.shape)/m,g=ce({inputs:{x:p},attrs:{shape:[f,m]},backend:a}),b=Xm(e.dtype),y=Yo(g,b,"sum",a),x=ce({inputs:{x:y},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(y),u&&a.disposeIntermediateTensorInfo(p),x}function eg(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return AQ(r,s,i,n)}var FQ={kernelName:Ro,backendName:"webgl",kernelFunc:eg};function Nn(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 p=0;p`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let w=n?[b,d,h]:[b,h,d],I=a?[y,m,c]:[y,c,m],T=ce({inputs:{x:e},backend:r,attrs:{shape:w}}),C=ce({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[T,C],F=Math.max(b,y),D=n?T.shape[1]:T.shape[2],$=s!=null,S=i!=null,M=l==="leakyrelu",B=l!=null?_c(l,!0):null,U=$||S||M||B!=null,H;if((h===1||m===1)&&D>xA&&U===!1){let K=T,Z=C;n&&(K=Nn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),E.push(K)),a&&(Z=Nn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(Z));let J=m!==1,ee=m===1,ae=K;J&&(ae=ce({inputs:{x:K},backend:r,attrs:{shape:[F,D,1]}}),E.push(ae));let te=m===1?2:1,re=Z;ee&&(re=ce({inputs:{x:Z},backend:r,attrs:{shape:[F,1,D]}}),E.push(re));let se=nk({inputs:{a:ae,b:re},backend:r});H=eg({inputs:{x:se},backend:r,attrs:{axis:te,keepDims:!0}}),E.push(se)}else{let K=ba(e.dtype,t.dtype),Z=new yA(w,I,[F,h,m],n,a,$,B,S,M),J=[T,C];if(s!=null&&J.push(s),S&&J.push(i),M){let ee=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));J.push(ee),E.push(ee)}H=r.runWebGLProgram(Z,J,K)}let j=ce({inputs:{x:H},backend:r,attrs:{shape:x}});E.push(H);for(let K of E)r.disposeIntermediateTensorInfo(K);return j}function DQ(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return Sm({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var RQ={kernelName:ii,backendName:"webgl",kernelFunc:DQ},fS="return abs(x);";function MQ(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=uA(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new is(a.shape,fS):r=new ir(a.shape,fS),n.runWebGLProgram(r,[a],a.dtype)}var PQ={kernelName:ru,backendName:"webgl",kernelFunc:MQ},OQ=Oa+` if (abs(x) > 1.) { return NAN; } return acos(x); -`,yQ=Ye({opSnippet:bQ}),xQ={kernelName:ki,backendName:"webgl",kernelFunc:yQ},vQ=Oa+` +`,LQ=Ye({opSnippet:OQ}),zQ={kernelName:Si,backendName:"webgl",kernelFunc:LQ},WQ=Oa+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,wQ=Ye({opSnippet:vQ}),kQ={kernelName:Ii,backendName:"webgl",kernelFunc:wQ},hS="return a + b;",IQ=fn({opSnippet:hS,packedOpSnippet:hS,supportsComplex:!0,cpuKernelImpl:$J}),SQ={kernelName:ks,backendName:"webgl",kernelFunc:IQ},NQ=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` +return log(x + sqrt(x * x - 1.0));`,BQ=Ye({opSnippet:WQ}),VQ={kernelName:Ni,backendName:"webgl",kernelFunc:BQ},gS="return a + b;",UQ=fn({opSnippet:gS,packedOpSnippet:gS,supportsComplex:!0,cpuKernelImpl:JJ}),GQ={kernelName:ks,backendName:"webgl",kernelFunc:UQ},HQ=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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(` `)} @@ -1539,7 +1543,7 @@ return log(x + sqrt(x * x - 1.0));`,wQ=Ye({opSnippet:vQ}),kQ={kernelName:Ii,back float result = ${a}; setOutput(result); } - `}},TQ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` + `}},qQ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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(` `)} @@ -1547,7 +1551,7 @@ return log(x + sqrt(x * x - 1.0));`,wQ=Ye({opSnippet:vQ}),kQ={kernelName:Ii,back vec4 result = ${a}; setOutput(result); } - `}};function Zh(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return aa({inputs:{x:a[0]},backend:n});if(a.length>G().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Zh({inputs:a.slice(0,o),backend:n}),u=Zh({inputs:a.slice(o),backend:n});return Zh({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ga(o,l)),s=a.map(o=>o.shape),i=G().getBool("WEBGL_PACK")?new TQ(a[0].shape,s):new NQ(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var CQ={kernelName:Si,backendName:"webgl",kernelFunc:Zh};function _Q(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=Nn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=Ko(f,f.dtype,"all",n),b;if(i){let y=N.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var EQ={kernelName:ru,backendName:"webgl",kernelFunc:_Q};function AQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=Nn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=Ko(f,f.dtype,"any",n),b;if(i){let y=N.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var FQ={kernelName:su,backendName:"webgl",kernelFunc:AQ},$Q=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 Zh(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return ra({inputs:{x:a[0]},backend:n});if(a.length>G().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Zh({inputs:a.slice(0,o),backend:n}),u=Zh({inputs:a.slice(o),backend:n});return Zh({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ba(o,l)),s=a.map(o=>o.shape),i=G().getBool("WEBGL_PACK")?new qQ(a[0].shape,s):new HQ(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var jQ={kernelName:Ti,backendName:"webgl",kernelFunc:Zh};function KQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=Nn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=Yo(f,f.dtype,"all",n),b;if(i){let y=N.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var XQ={kernelName:su,backendName:"webgl",kernelFunc:KQ};function YQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=Nn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=Yo(f,f.dtype,"any",n),b;if(i){let y=N.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var ZQ={kernelName:iu,backendName:"webgl",kernelFunc:YQ},JQ=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]; @@ -1567,7 +1571,7 @@ return log(x + sqrt(x * x - 1.0));`,wQ=Ye({opSnippet:vQ}),kQ={kernelName:Ii,back } setOutput(float(bestIndex)); } - `}},DQ=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=dt(o),u=Sn("coords",o),p,d;if(s===1){d=o+1;let C=dt(d);p=` + `}},QQ=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let 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=dt(o),u=Sn("coords",o),p,d;if(s===1){d=o+1;let C=dt(d);p=` ${C} sourceLocR = ${C}(${u.join()}, 0); ++${u[o-1]}; ${C} sourceLocG = ${C}(${u.join()}, 0); @@ -1629,25 +1633,25 @@ return log(x + sqrt(x * x - 1.0));`,wQ=Ye({opSnippet:vQ}),kQ={kernelName:Ii,back } setOutput(bestIndex); } - `}};function uA(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=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new $Q(o,n,a==null),u=[t];a!=null&&u.push(a);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let d=uA(e,t,n,p);return e.disposeIntermediateTensorInfo(p),d}function pA(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=N.computeOptimalWindowSize(s),o=new DQ(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=pA(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}return u}function cA(e,t,n,a){let r=[n];if(N.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!G().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=N.computeOutAndReduceShapes(l.shape,r),d=v.sizeFromShape(p),c=ce({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});s.push(c);let h=uA(e,c,a);s.push(h);let m=ce({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return pA(e,t,a)}function RQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Nn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=cA(n,l,i[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var MQ={kernelName:iu,backendName:"webgl",kernelFunc:RQ};function PQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Nn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=cA(n,l,i[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var OQ={kernelName:ou,backendName:"webgl",kernelFunc:PQ},LQ=Oa+` + `}};function vA(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=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new JQ(o,n,a==null),u=[t];a!=null&&u.push(a);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let d=vA(e,t,n,p);return e.disposeIntermediateTensorInfo(p),d}function wA(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=N.computeOptimalWindowSize(s),o=new 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2.0;`,yee=Ye({opSnippet:bee}),xee={kernelName:Ai,backendName:"webgl",kernelFunc:yee},Ec=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,u=e.dilationWidth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,c=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) * ${e.inChannels} + d`,b="0.0";if(m||(b="-1.0 / 1e-20"),n){let C=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${c}, ${h}); @@ -1788,7 +1792,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(${x}); } - `}},tk=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,u=e.dilationDepth,p=e.dilationHeight,d=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let F=">=";this.userCode=` + `}},ak=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,u=e.dilationDepth,p=e.dilationHeight,d=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let F=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${g}, ${b}); @@ -1951,7 +1955,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(${I}); } - `}};function eee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;yp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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Got strides ${i} and dilations '${u}'`);let p=N.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return ra({inputs:{x:r},backend:n});let d=new Ec(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var wee={kernelName:$i,backendName:"webgl",kernelFunc:vee};function kee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=[1,1,1],d=N.computePool3DInfo(r.shape,s,i,p,o,l,u),c=new ak(d,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var Iee={kernelName:uu,backendName:"webgl",kernelFunc:kee},See=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,d=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${u}, ${p}); const float avgMultiplier = float(${d}); @@ -1993,7 +1997,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(dotProd); } - `}},see=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,u=e.dilationWidth,p=e.effectiveFilterDepth,d=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=p-1-e.padInfo.front,m=d-1-e.padInfo.top,f=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=` + `}},Nee=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,u=e.dilationWidth,p=e.effectiveFilterDepth,d=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=p-1-e.padInfo.front,m=d-1-e.padInfo.top,f=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=` const ivec3 pads = ivec3(${h}, ${m}, ${f}); const float avgMultiplier = float(${g}); @@ -2049,7 +2053,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(dotProd); } - `}};function iee(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=N.computePool3DInfo(i.shape,o,l,d,u,p),h=new see(c);return n.runWebGLProgram(h,[r],i.dtype)}var oee={kernelName:Rc,backendName:"webgl",kernelFunc:iee};function lee(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;yp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=new ree(p);return n.runWebGLProgram(d,[r],i.dtype)}var uee={kernelName:$m,backendName:"webgl",kernelFunc:lee};function pee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Sm({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var cee={kernelName:Fi,backendName:"webgl",kernelFunc:pee},dee=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function Tee(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=N.computePool3DInfo(i.shape,o,l,d,u,p),h=new Nee(c);return n.runWebGLProgram(h,[r],i.dtype)}var Cee={kernelName:Rc,backendName:"webgl",kernelFunc:Tee};function _ee(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;yp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=new See(p);return n.runWebGLProgram(d,[r],i.dtype)}var Eee={kernelName:$m,backendName:"webgl",kernelFunc:_ee};function Aee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Sm({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var Fee={kernelName:Di,backendName:"webgl",kernelFunc:Aee},$ee=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2059,7 +2063,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam float inv = scale * inversesqrt(variance + float(${s})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}},hee=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}},Dee=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${o}; @@ -2072,7 +2076,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam setOutput((x - mean) * inv + offset); } - `}},mee=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=G().getBool("WEBGL_PACK_NORMALIZATION")?new hee(a.shape,r.shape,s.shape,p,d,l):new dee(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},fee={kernelName:Ki,backendName:"webgl",kernelFunc:mee},gee=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=dt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=bee(this.rank),a,r=e.map((s,i)=>`sourceLoc.${ov[i]} = start[${i}] + coords.${ov[i]};`);a=` + `}},Ree=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.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 u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=G().getBool("WEBGL_PACK_NORMALIZATION")?new Dee(a.shape,r.shape,s.shape,p,d,l):new $ee(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},Mee={kernelName:Yi,backendName:"webgl",kernelFunc:Ree},Pee=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=dt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Oee(this.rank),a,r=e.map((s,i)=>`sourceLoc.${lv[i]} = start[${i}] + coords.${lv[i]};`);a=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${r.join(` @@ -2082,7 +2086,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam ${a} setOutput(getSource(${n})); } - `}},ov=["x","y","z","w","u","v"];function bee(e){if(e===1)return"sourceLoc";if(e<=6)return ov.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var yee=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=dt(this.rank),n=Sn("coords",this.rank),a=Sn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=` + `}},lv=["x","y","z","w","u","v"];function Oee(e){if(e===1)return"sourceLoc";if(e<=6)return lv.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Lee=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=dt(this.rank),n=Sn("coords",this.rank),a=Sn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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]}; @@ -2111,7 +2115,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam ${o} setOutput(result); } - `}};function xee(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=Kt.computeFlatOffset(t,v.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 Sp(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=Kt.parseSliceParams(r,s,i);if(Kt.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=u9(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=Kt.isSliceContinous(r.shape,o,l);if(u||!p){let d=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new yee(l):new gee(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),xee(r,o,l,n)}var vee={kernelName:Xu,backendName:"webgl",kernelFunc:Sp},wee=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=[],m=ce({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Nn({inputs:{x:m},backend:n,attrs:{perm:u}}),g=ce({inputs:{x:f},backend:n,attrs:{shape:p}}),b=Sp({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},kee={kernelName:uu,backendName:"webgl",kernelFunc:wee};function Iee(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),u=ZE(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var See={kernelName:pu,backendName:"webgl",kernelFunc:Iee};function Nee(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var Tee={kernelName:Mc,backendName:"webgl",kernelFunc:Nee},Cee="return float(a != b);",dA=fn({opSnippet:Cee,cpuKernelImpl:e9,dtype:"bool"}),_ee={kernelName:Pu,backendName:"webgl",kernelFunc:dA};function $d(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return aa({inputs:{x:r.complexTensorInfos.real},backend:n})}var Eee={kernelName:qm,backendName:"webgl",kernelFunc:$d},Aee="return float(int(x));";function Fee(e,t){let n=new ir(e.shape,Aee),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function lv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return aa({inputs:{x:r},backend:n});let i=Nt(r.shape),o=lv({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Ds({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=$d({inputs:{input:r},backend:n}),o=lv({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=aa({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(n.shouldExecuteOnCPU([r])){let i=n.texData.get(r.dataId).values,[o,l,u]=RJ(i,r.shape,r.dtype,s);return n.makeTensorInfo(o,l,u)}if(s==="int32")return Fee(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=dA({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 $ee={kernelName:$i,backendName:"webgl",kernelFunc:lv},mS="return ceil(x);",Dee=Ye({opSnippet:mS,packedOpSnippet:mS,cpuKernelImpl:MJ}),Ree={kernelName:Di,backendName:"webgl",kernelFunc:Dee},Mee=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + `}};function zee(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=Kt.computeFlatOffset(t,v.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 Sp(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=Kt.parseSliceParams(r,s,i);if(Kt.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=E9(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=Kt.isSliceContinous(r.shape,o,l);if(u||!p){let d=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Lee(l):new Pee(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),zee(r,o,l,n)}var Wee={kernelName:Yu,backendName:"webgl",kernelFunc:Sp},Bee=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=[],m=ce({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Nn({inputs:{x:m},backend:n,attrs:{perm:u}}),g=ce({inputs:{x:f},backend:n,attrs:{shape:p}}),b=Sp({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},Vee={kernelName:pu,backendName:"webgl",kernelFunc:Bee};function Uee(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),u=lA(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var Gee={kernelName:cu,backendName:"webgl",kernelFunc:Uee};function Hee(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var qee={kernelName:Mc,backendName:"webgl",kernelFunc:Hee},jee="return float(a != b);",IA=fn({opSnippet:jee,cpuKernelImpl:v9,dtype:"bool"}),Kee={kernelName:Ou,backendName:"webgl",kernelFunc:IA};function $d(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return ra({inputs:{x:r.complexTensorInfos.real},backend:n})}var Xee={kernelName:jm,backendName:"webgl",kernelFunc:$d},Yee="return float(int(x));";function Zee(e,t){let n=new ir(e.shape,Yee),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function uv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return ra({inputs:{x:r},backend:n});let i=Nt(r.shape),o=uv({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Rs({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=$d({inputs:{input:r},backend:n}),o=uv({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=ra({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(n.shouldExecuteOnCPU([r])){let i=n.texData.get(r.dataId).values,[o,l,u]=e9(i,r.shape,r.dtype,s);return n.makeTensorInfo(o,l,u)}if(s==="int32")return Zee(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=IA({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 Jee={kernelName:Ri,backendName:"webgl",kernelFunc:uv},bS="return ceil(x);",Qee=Ye({opSnippet:bS,packedOpSnippet:bS,cpuKernelImpl:t9}),ete={kernelName:Mi,backendName:"webgl",kernelFunc:Qee},tte=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { float value = getAAtOutCoords(); @@ -2122,7 +2126,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam setOutput(clamp(value, minVal, maxVal)); } - `}},Pee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + `}},nte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { vec4 value = getAAtOutCoords(); @@ -2133,7 +2137,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function Oee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;G().getBool("WEBGL_PACK_CLIP")?o=new Pee(r.shape):o=new Mee(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var Lee={kernelName:Is,backendName:"webgl",kernelFunc:Oee},zee=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + `}};function ate(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;G().getBool("WEBGL_PACK_CLIP")?o=new nte(r.shape):o=new tte(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var rte={kernelName:Is,backendName:"webgl",kernelFunc:ate},ste=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2146,7 +2150,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function fS(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Wee(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new 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Vee(l.map(h=>h.shape)),d=n.runWebGLProgram(p,l,a);l.forEach(h=>n.disposeIntermediateTensorInfo(h));let c=ce({inputs:{x:d},attrs:{shape:u},backend:n});return n.disposeIntermediateTensorInfo(d),c}function Hee(e,t,n){let a=N.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ce({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function hA(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);N.assertParamsConsistent(i,s);let o=N.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?aa({inputs:{x:l[0]},backend:n}):lc(l,s,n)}var qee={kernelName:cu,backendName:"webgl",kernelFunc:hA},mA=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let 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r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let h=e.map(w=>{let I=[-1,v.sizeFromShape(w.shape.slice(t))];return ce({inputs:{x:w},backend:n,attrs:{shape:I}})}),m=h.map(w=>({vals:n.readSync(w.dataId),shape:w.shape})),f=N.computeOutShape(h.map(w=>w.shape),1),g=h[0].shape[0]===1,b=n9(m,f,a,g),y=N.computeOutShape(e.map(w=>w.shape),t),x=n.makeTensorInfo(y,a,b);return h.forEach(w=>n.disposeIntermediateTensorInfo(w)),x}let s=e.filter(h=>v.sizeFromShape(h.shape)>0),i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new ir(e[0].shape,ts):new is(e[0].shape,ts);return n.runWebGLProgram(h,e,a)}let o=G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let f=0;fm.shape),t);return n.runWebGLProgram(h,s,a)}let{tensors2D:l,outShape:u}=cte(s,t,n),p=new lte(l.map(h=>h.shape)),d=n.runWebGLProgram(p,l,a);l.forEach(h=>n.disposeIntermediateTensorInfo(h));let c=ce({inputs:{x:d},attrs:{shape:u},backend:n});return n.disposeIntermediateTensorInfo(d),c}function cte(e,t,n){let a=N.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ce({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function SA(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);N.assertParamsConsistent(i,s);let o=N.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?ra({inputs:{x:l[0]},backend:n}):lc(l,s,n)}var dte={kernelName:du,backendName:"webgl",kernelFunc:SA},NA=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,u=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,b=f?2:3,y=f?3:1,x="",w="";n&&(a?x=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?x=`float activation(float a) { @@ -2326,7 +2330,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam ${w} setOutput(result); } - `}},jee=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` + `}},hte=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${r}, ${s}, ${i}); const ivec3 pads = ivec3(${t}, ${n}, ${a}); @@ -2414,7 +2418,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(dotProd); } - `}},fA=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,d=` + `}},TA=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,d=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let f=0;f=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function gA({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),p=n.inChannels,d=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,b=[];if(s!=null){let y=Nm(s.shape,h);y!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:y}}),b.push(s))}if(r!=null){let y=Nm(r.shape,h);y!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:y}}),b.push(r))}if(!((d===1||c===1)&&p>lA)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let y=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,y,n.inChannels],dtype:e.dtype},w=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Cc(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(I);let T=Sm({a:x,b:I,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(T.dataId);v.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=w,C.shape=n.outShape,g=aa({inputs:{x:T},backend:a}),g.shape=n.outShape,b.push(T)}else{let y=n.outHeight*n.outWidth,x=ce({inputs:{x:e},backend:a,attrs:{shape:h?[n.batchSize,y,n.inChannels]:[n.batchSize,n.inChannels,y]}}),w=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=Sm({a:h?x:w,b:h?w:x,transposeA:!h,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ce({inputs:{x:I},backend:a,attrs:{shape:n.outShape}}),b.push(x),b.push(w),b.push(I)}for(let y of b)a.disposeIntermediateTensorInfo(y);return g}function bA({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,b=[n.batchSize,f,g],y=!0,x=!1,w=[];if(s!=null){let K=Nm(s.shape,m);K!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:K}}),w.push(s))}if(r!=null){let K=Nm(r.shape,m);K!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:K}}),w.push(r))}let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});w.push(I);let T=new Kee(b,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],E=a.runWebGLProgram(T,[e],"float32",C),F=ce({inputs:{x:E},backend:a,attrs:{shape:b}});w.push(E),w.push(F);let D=r!=null,$=s!=null,S=o==="leakyrelu",M=o?_c(o,!0):null,B=new oA(m?F.shape:I.shape,m?I.shape:F.shape,m?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,x,D,M,$,S),U=m?[F,I]:[I,F];if(r&&U.push(r),$&&U.push(s),S){let K=a.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));U.push(K),w.push(K)}let H=a.runWebGLProgram(B,U,"float32"),j=ce({inputs:{x:H},backend:a,attrs:{shape:n.outShape}});w.push(H);for(let K of w)a.disposeIntermediateTensorInfo(K);return j}function Xee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=gA({x:r,filter:s,convInfo:c,backend:n});else if(c.strideWidth<=2&&d==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let f=new fA(c),g=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];h=n.runWebGLProgram(f,[r,s],"float32",g)}else if(G().getBool("WEBGL_CONV_IM2COL"))h=bA({x:r,filter:s,convInfo:c,backend:n});else{let f=new mA(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ce({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var Yee={kernelName:Ri,backendName:"webgl",kernelFunc:Xee},Zee=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 Nm(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function CA({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),p=n.inChannels,d=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,b=[];if(s!=null){let y=Nm(s.shape,h);y!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:y}}),b.push(s))}if(r!=null){let y=Nm(r.shape,h);y!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:y}}),b.push(r))}if(!((d===1||c===1)&&p>xA)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let y=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,y,n.inChannels],dtype:e.dtype},w=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Cc(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(I);let T=Sm({a:x,b:I,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(T.dataId);v.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=w,C.shape=n.outShape,g=ra({inputs:{x:T},backend:a}),g.shape=n.outShape,b.push(T)}else{let y=n.outHeight*n.outWidth,x=ce({inputs:{x:e},backend:a,attrs:{shape:h?[n.batchSize,y,n.inChannels]:[n.batchSize,n.inChannels,y]}}),w=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=Sm({a:h?x:w,b:h?w:x,transposeA:!h,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ce({inputs:{x:I},backend:a,attrs:{shape:n.outShape}}),b.push(x),b.push(w),b.push(I)}for(let y of b)a.disposeIntermediateTensorInfo(y);return g}function _A({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,b=[n.batchSize,f,g],y=!0,x=!1,w=[];if(s!=null){let K=Nm(s.shape,m);K!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:K}}),w.push(s))}if(r!=null){let K=Nm(r.shape,m);K!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:K}}),w.push(r))}let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});w.push(I);let T=new mte(b,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],E=a.runWebGLProgram(T,[e],"float32",C),F=ce({inputs:{x:E},backend:a,attrs:{shape:b}});w.push(E),w.push(F);let D=r!=null,$=s!=null,S=o==="leakyrelu",M=o?_c(o,!0):null,B=new yA(m?F.shape:I.shape,m?I.shape:F.shape,m?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,x,D,M,$,S),U=m?[F,I]:[I,F];if(r&&U.push(r),$&&U.push(s),S){let K=a.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));U.push(K),w.push(K)}let H=a.runWebGLProgram(B,U,"float32"),j=ce({inputs:{x:H},backend:a,attrs:{shape:n.outShape}});w.push(H);for(let K of w)a.disposeIntermediateTensorInfo(K);return j}function fte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=CA({x:r,filter:s,convInfo:c,backend:n});else if(c.strideWidth<=2&&d==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let f=new TA(c),g=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];h=n.runWebGLProgram(f,[r,s],"float32",g)}else if(G().getBool("WEBGL_CONV_IM2COL"))h=_A({x:r,filter:s,convInfo:c,backend:n});else{let f=new NA(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ce({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var gte={kernelName:Pi,backendName:"webgl",kernelFunc:fte},bte=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; @@ -2694,7 +2698,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(dotProd); } - `}},Jee=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,u=s?2:3,p=s?3:1;this.userCode=` + `}},yte=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,u=s?2:3,p=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -2747,7 +2751,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(dotProd); } - `}},Qee=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` + `}},xte=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2789,7 +2793,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(dotProd); } - `}},ete=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=` + `}},vte=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${o}, ${l}, ${u}); void main() { @@ -2846,7 +2850,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(dotProd); } - `}};function tte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new Zee(c);return n.runWebGLProgram(h,[r,s],"float32")}var nte={kernelName:Rm,backendName:"webgl",kernelFunc:tte},ate=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.filterHeight,n=e.filterWidth,a=t-1-e.padInfo.top,r=n-1-e.padInfo.left;this.userCode=` + `}};function wte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new bte(c);return n.runWebGLProgram(h,[r,s],"float32")}var kte={kernelName:Mm,backendName:"webgl",kernelFunc:wte},Ite=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.filterHeight,n=e.filterWidth,a=t-1-e.padInfo.top,r=n-1-e.padInfo.left;this.userCode=` const ivec2 pads = ivec2(${a}, ${r}); void main() { @@ -2920,17 +2924,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(result); } - `}};function rte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=N.convertConv2DDataFormat(u),c=N.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d);if(G().getBool("WEBGL_PACK")&&d==="channelsLast"){let h=[[c.strideHeight,c.strideWidth]],m=new ate(c);return n.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new Jee(c);return n.runWebGLProgram(h,[r,s],"float32")}}var ste={kernelName:Mi,backendName:"webgl",kernelFunc:rte};function ite(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=N.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new jee(u);return n.runWebGLProgram(p,[r,s],"float32")}var ote={kernelName:Pi,backendName:"webgl",kernelFunc:ite};function lte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=N.computeConv3DInfo(r.shape,l,i,1,o),p=new Qee(u);return n.runWebGLProgram(p,[r,s],"float32")}var ute={kernelName:du,backendName:"webgl",kernelFunc:lte};function pte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=N.computeConv3DInfo(l,s.shape,o,1,i),p=new ete(u);return n.runWebGLProgram(p,[r,s],"float32")}var cte={kernelName:hu,backendName:"webgl",kernelFunc:pte},dte=Ip+` + `}};function Ste(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=N.convertConv2DDataFormat(u),c=N.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d);if(G().getBool("WEBGL_PACK")&&d==="channelsLast"){let h=[[c.strideHeight,c.strideWidth]],m=new Ite(c);return n.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new yte(c);return n.runWebGLProgram(h,[r,s],"float32")}}var Nte={kernelName:Oi,backendName:"webgl",kernelFunc:Ste};function Tte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=N.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new hte(u);return n.runWebGLProgram(p,[r,s],"float32")}var Cte={kernelName:Li,backendName:"webgl",kernelFunc:Tte};function _te(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=N.computeConv3DInfo(r.shape,l,i,1,o),p=new xte(u);return n.runWebGLProgram(p,[r,s],"float32")}var Ete={kernelName:hu,backendName:"webgl",kernelFunc:_te};function Ate(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=N.computeConv3DInfo(l,s.shape,o,1,i),p=new vte(u);return n.runWebGLProgram(p,[r,s],"float32")}var Fte={kernelName:mu,backendName:"webgl",kernelFunc:Ate},$te=Ip+` return cos(x); -`,hte=` +`,Dte=` vec4 result = cos(x); bvec4 isNaN = isnan(x); - ${jo} + ${Xo} return result; -`,mte=Ye({opSnippet:dte,packedOpSnippet:hte}),fte={kernelName:Oi,backendName:"webgl",kernelFunc:mte},gte=` +`,Rte=Ye({opSnippet:$te,packedOpSnippet:Dte}),Mte={kernelName:zi,backendName:"webgl",kernelFunc:Rte},Pte=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,bte=Ye({opSnippet:gte}),yte={kernelName:Li,backendName:"webgl",kernelFunc:bte},xte=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,w]=d>1?[`${(o-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` +`,Ote=Ye({opSnippet:Pte}),Lte={kernelName:Wi,backendName:"webgl",kernelFunc:Ote},zte=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,w]=d>1?[`${(o-1)/(d-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(${y}); void main() { @@ -2991,20 +2995,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam setOutput(newValue); } } - `}},vte=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new xte(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},wte={kernelName:fu,backendName:"webgl",kernelFunc:vte},Ac;(function(e){e.Prod="*",e.Sum="+"})(Ac||(Ac={}));var gS=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===Ac.Prod?"1.0":"0.0",i=n?s:`getX(${bS(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` + `}},Wte=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new zte(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},Bte={kernelName:gu,backendName:"webgl",kernelFunc:Wte},Ac;(function(e){e.Prod="*",e.Sum="+"})(Ac||(Ac={}));var xS=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===Ac.Prod?"1.0":"0.0",i=n?s:`getX(${vS(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` void main() { ${dt(r)} coords = getOutputCoords(); - int end = ${yS(r,"coords",this.op)}; + int end = ${wS(r,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${l}) { int idx = ${u}; - ${yS(r,"coords",this.op)} = idx; - val ${this.op}= getX(${bS(r,"coords",this.op)}); + ${wS(r,"coords",this.op)} = idx; + val ${this.op}= getX(${vS(r,"coords",this.op)}); } setOutput(val); } - `}};function bS(e,t,n){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 new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function yS(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function yA(e,t,n,a,r,s){let i=t.shape.length,o=N.getAxesPermutation([a],i),l=t;o!=null&&(l=Nn({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=N.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=aa({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new gS(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new gS(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=N.getUndoAxesPermutation(o),h=Nn({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function kte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return yA(Ac.Prod,r,n,s,i,o)}var Ite={kernelName:mu,backendName:"webgl",kernelFunc:kte};function Ste(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return yA(Ac.Sum,r,n,s,i,o)}var Nte={kernelName:zi,backendName:"webgl",kernelFunc:Ste};function Tte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=ZE(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=DJ(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Cte={kernelName:Oc,backendName:"webgl",kernelFunc:Tte},_te=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` + `}};function vS(e,t,n){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 new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function wS(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function EA(e,t,n,a,r,s){let i=t.shape.length,o=N.getAxesPermutation([a],i),l=t;o!=null&&(l=Nn({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=N.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=ra({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new xS(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new xS(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=N.getUndoAxesPermutation(o),h=Nn({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function Vte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return EA(Ac.Prod,r,n,s,i,o)}var Ute={kernelName:fu,backendName:"webgl",kernelFunc:Vte};function Gte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return EA(Ac.Sum,r,n,s,i,o)}var Hte={kernelName:Bi,backendName:"webgl",kernelFunc:Gte};function qte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=lA(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=QJ(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var jte={kernelName:Oc,backendName:"webgl",kernelFunc:qte},Kte=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]; @@ -3023,7 +3027,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={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 Ete(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new _te(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var Ate={kernelName:gu,backendName:"webgl",kernelFunc:Ete},xA=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`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 Xte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new Kte(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var Yte={kernelName:bu,backendName:"webgl",kernelFunc:Xte},AA=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?l=`float activation(float a) { @@ -3076,7 +3080,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam ${u} setOutput(result); } - `}},vA=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=` + `}},FA=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=N.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;G().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new vA(d):c=new xA(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var $te={kernelName:Wi,backendName:"webgl",kernelFunc:Fte},Dte=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 Zte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a,p=l;p==null&&(p=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=N.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;G().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new FA(d):c=new AA(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var Jte={kernelName:Vi,backendName:"webgl",kernelFunc:Zte},Qte=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; @@ -3300,7 +3304,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(dotProd); } - `}},Rte=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=` + `}},ene=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() { @@ -3345,13 +3349,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(dotProd); } - `}};function Mte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=N.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new Dte(d);return n.runWebGLProgram(c,[r,s],"float32")}var Pte={kernelName:Mm,backendName:"webgl",kernelFunc:Mte};function Ote(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=N.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new Rte(d);return n.runWebGLProgram(c,[r,s],"float32")}var Lte={kernelName:Pm,backendName:"webgl",kernelFunc:Ote},zte=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function tne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=N.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new Qte(d);return n.runWebGLProgram(c,[r,s],"float32")}var nne={kernelName:Pm,backendName:"webgl",kernelFunc:tne};function ane(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=N.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new ene(d);return n.runWebGLProgram(c,[r,s],"float32")}var rne={kernelName:Om,backendName:"webgl",kernelFunc:ane},sne=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 Wte(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=v.sizeFromShape(a.shape),i=ce({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new zte(s),l=n.runWebGLProgram(o,[i],i.dtype),u=ce({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var Bte={kernelName:Lc,backendName:"webgl",kernelFunc:Wte},Vte=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:u}=e,{top:p,left:d}=a;this.userCode=` + `}};function ine(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=v.sizeFromShape(a.shape),i=ce({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new sne(s),l=n.runWebGLProgram(o,[i],i.dtype),u=ce({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var one={kernelName:Lc,backendName:"webgl",kernelFunc:ine},lne=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:u}=e,{top:p,left:d}=a;this.userCode=` const ivec2 strides = ivec2(${r}, ${s}); const ivec2 pads = ivec2(${p}, ${d}); const float neg_infinity = -3.4e38; @@ -3389,7 +3393,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam float result = curVal; setOutput(result); } - `}};function Ute(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=N.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new Vte(u);p=n.runWebGLProgram(d,[r,s],"float32");let c=ce({inputs:{x:p},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(p),c}var Gte={kernelName:Bi,backendName:"webgl",kernelFunc:Ute};function Hte(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(r,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=N.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f=0&&(c=Qf({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var qte={kernelName:Om,backendName:"webgl",kernelFunc:Hte},jte="return (x >= 0.0) ? x : (exp(x) - 1.0);",Kte=` + `}};function une(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=N.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new lne(u);p=n.runWebGLProgram(d,[r,s],"float32");let c=ce({inputs:{x:p},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(p),c}var pne={kernelName:Ui,backendName:"webgl",kernelFunc:une};function cne(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(r,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=N.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f=0&&(c=eg({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var dne={kernelName:Lm,backendName:"webgl",kernelFunc:cne},hne="return (x >= 0.0) ? x : (exp(x) - 1.0);",mne=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3398,12 +3402,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,Xte=Ye({opSnippet:jte,packedOpSnippet:Kte}),Yte={kernelName:Ui,backendName:"webgl",kernelFunc:Xte},Zte="return (b >= 0.0) ? a : a * (b + 1.0);",Jte=` +`,fne=Ye({opSnippet:hne,packedOpSnippet:mne}),gne={kernelName:Hi,backendName:"webgl",kernelFunc:fne},bne="return (b >= 0.0) ? a : a * (b + 1.0);",yne=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,Qte=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Fd(Jte,a.shape,r.shape):new tu(Zte,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},ene={kernelName:bu,backendName:"webgl",kernelFunc:Qte},tne=` +`,xne=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Fd(yne,a.shape,r.shape):new nu(bne,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},vne={kernelName:yu,backendName:"webgl",kernelFunc:xne},wne=` return vec4(equal(a, b)); -`,nne="return float(a == b);",ane=fn({opSnippet:nne,packedOpSnippet:tne,dtype:"bool",cpuKernelImpl:OJ}),rne={kernelName:xu,backendName:"webgl",kernelFunc:ane},sne=` +`,kne="return float(a == b);",Ine=fn({opSnippet:kne,packedOpSnippet:wne,dtype:"bool",cpuKernelImpl:a9}),Sne={kernelName:vu,backendName:"webgl",kernelFunc:Ine},Nne=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -3418,9 +3422,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={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)); -`,ine=Ye({opSnippet:sne}),one={kernelName:yu,backendName:"webgl",kernelFunc:ine},lne=Ip+` +`,Tne=Ye({opSnippet:Nne}),Cne={kernelName:xu,backendName:"webgl",kernelFunc:Tne},_ne=Ip+` return exp(x); -`,une=` +`,Ene=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -3429,7 +3433,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam result.a = isNaN.a ? x.a : result.a; return result; -`,wA=Ye({opSnippet:lne,packedOpSnippet:une,cpuKernelImpl:LJ,dtype:"float32"}),pne={kernelName:Gi,backendName:"webgl",kernelFunc:wA};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&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ce({inputs:{x:s},backend:a,attrs:{shape:o}})}var cne={kernelName:vu,backendName:"webgl",kernelFunc:uv},xS="return exp(x) - 1.0;",dne=Ye({opSnippet:xS,packedOpSnippet:xS,cpuKernelImpl:zJ}),hne={kernelName:Hi,backendName:"webgl",kernelFunc:dne},vS=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 ${e}.`);this.userCode=` +`,$A=Ye({opSnippet:_ne,packedOpSnippet:Ene,cpuKernelImpl:r9,dtype:"float32"}),Ane={kernelName:qi,backendName:"webgl",kernelFunc:$A};function pv(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ce({inputs:{x:s},backend:a,attrs:{shape:o}})}var Fne={kernelName:wu,backendName:"webgl",kernelFunc:pv},kS="return exp(x) - 1.0;",$ne=Ye({opSnippet:kS,packedOpSnippet:kS,cpuKernelImpl:s9}),Dne={kernelName:ji,backendName:"webgl",kernelFunc:$ne},IS=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) { @@ -3462,12 +3466,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function kA(e,t,n){let a=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ce({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new vS("real",l,t),p=new vS("imag",l,t),d=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Ds({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=ce({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function mne(e){let{inputs:t,backend:n}=e,{input:a}=t;return kA(a,!1,n)}var fne={kernelName:Lm,backendName:"webgl",kernelFunc:mne},gne=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function DA(e,t,n){let a=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ce({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new IS("real",l,t),p=new IS("imag",l,t),d=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Rs({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=ce({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function Rne(e){let{inputs:t,backend:n}=e,{input:a}=t;return DA(a,!1,n)}var Mne={kernelName:zm,backendName:"webgl",kernelFunc:Rne},Pne=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function Dd(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new gne(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var bne={kernelName:zc,backendName:"webgl",kernelFunc:Dd},yne=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `}};function Dd(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new Pne(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var One={kernelName:zc,backendName:"webgl",kernelFunc:Dd},Lne=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]; @@ -3481,7 +3485,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(outputValue); } - `}},xne={kernelName:wu,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new yne(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},wS="return floor(x);",vne=Ye({opSnippet:wS,packedOpSnippet:wS,cpuKernelImpl:WJ}),wne={kernelName:qi,backendName:"webgl",kernelFunc:vne},kne=` + `}},zne={kernelName:ku,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new Lne(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},SS="return floor(x);",Wne=Ye({opSnippet:SS,packedOpSnippet:SS,cpuKernelImpl:i9}),Bne={kernelName:Ki,backendName:"webgl",kernelFunc:Wne},Vne=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3491,7 +3495,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } else { return NAN; } -`,Ine=` +`,Une=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -3512,7 +3516,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,Sne=fn({opSnippet:kne,packedOpSnippet:Ine,dtype:"int32"}),Nne={kernelName:ji,backendName:"webgl",kernelFunc:Sne},Tne=class{constructor(e){this.variableNames=["A"];let t=En(),[n,a]=e;this.outputShape=e,this.userCode=` +`,Gne=fn({opSnippet:Vne,packedOpSnippet:Une,dtype:"int32"}),Hne={kernelName:Xi,backendName:"webgl",kernelFunc:Gne},qne=class{constructor(e){this.variableNames=["A"];let t=En(),[n,a]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3534,7 +3538,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam setOutput(floor(value * 255.0 + 0.5)); } - `}},Cne=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=En(),[n,a]=e;this.outputShape=e,this.userCode=` + `}},jne=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=En(),[n,a]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); 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-`,Vne=fn({opSnippet:Wne,packedOpSnippet:Bne,cpuKernelImpl:UJ,dtype:"bool"}),Une={kernelName:Su,backendName:"webgl",kernelFunc:Vne},Gne="return float(a >= b);",Hne=` +`,lae=fn({opSnippet:iae,packedOpSnippet:oae,cpuKernelImpl:u9,dtype:"bool"}),uae={kernelName:Nu,backendName:"webgl",kernelFunc:lae},pae="return float(a >= b);",cae=` return vec4(greaterThanEqual(a, b)); -`,qne=fn({opSnippet:Gne,packedOpSnippet:Hne,dtype:"bool",cpuKernelImpl:GJ}),jne={kernelName:Xi,backendName:"webgl",kernelFunc:qne};function Kne(e){let{inputs:t,backend:n}=e,{input:a}=t;return kA(a,!0,n)}var Xne={kernelName:zm,backendName:"webgl",kernelFunc:Kne},Yne="return float(!isnan(x) && !isinf(x));",Zne=Ye({opSnippet:Yne,dtype:"bool"}),Jne={kernelName:Zi,backendName:"webgl",kernelFunc:Zne},Qne="return float(isinf(x));",eae=Ye({opSnippet:Qne,dtype:"bool"}),tae={kernelName:Ji,backendName:"webgl",kernelFunc:eae},nae="return float(isnan(x));",aae=Ye({opSnippet:nae,dtype:"bool"}),rae={kernelName:Qi,backendName:"webgl",kernelFunc:aae},sae="return float(a < b);",iae=` +`,dae=fn({opSnippet:pae,packedOpSnippet:cae,dtype:"bool",cpuKernelImpl:p9}),hae={kernelName:Zi,backendName:"webgl",kernelFunc:dae};function mae(e){let{inputs:t,backend:n}=e,{input:a}=t;return DA(a,!0,n)}var fae={kernelName:Wm,backendName:"webgl",kernelFunc:mae},gae="return float(!isnan(x) && !isinf(x));",bae=Ye({opSnippet:gae,dtype:"bool"}),yae={kernelName:Qi,backendName:"webgl",kernelFunc:bae},xae="return float(isinf(x));",vae=Ye({opSnippet:xae,dtype:"bool"}),wae={kernelName:eo,backendName:"webgl",kernelFunc:vae},kae="return float(isnan(x));",Iae=Ye({opSnippet:kae,dtype:"bool"}),Sae={kernelName:to,backendName:"webgl",kernelFunc:Iae},Nae="return float(a < b);",Tae=` return vec4(lessThan(a, b)); -`,oae=fn({opSnippet:sae,packedOpSnippet:iae,cpuKernelImpl:HJ,dtype:"bool"}),lae={kernelName:Nu,backendName:"webgl",kernelFunc:oae},uae="return float(a <= b);",pae=` +`,Cae=fn({opSnippet:Nae,packedOpSnippet:Tae,cpuKernelImpl:c9,dtype:"bool"}),_ae={kernelName:Tu,backendName:"webgl",kernelFunc:Cae},Eae="return float(a <= b);",Aae=` return vec4(lessThanEqual(a, b)); -`,cae=fn({opSnippet:uae,packedOpSnippet:pae,cpuKernelImpl:qJ,dtype:"bool"}),dae={kernelName:Tu,backendName:"webgl",kernelFunc:cae};function hae(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=jJ(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var mae={kernelName:Cu,backendName:"webgl",kernelFunc:hae},fae=Ip+` +`,Fae=fn({opSnippet:Eae,packedOpSnippet:Aae,cpuKernelImpl:d9,dtype:"bool"}),$ae={kernelName:Cu,backendName:"webgl",kernelFunc:Fae};function Dae(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=h9(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var Rae={kernelName:_u,backendName:"webgl",kernelFunc:Dae},Mae=Ip+` return x < 0.0 ? 0./0. : log(x); -`,gae=` +`,Pae=` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); @@ -3608,18 +3612,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; -`,bae=Ye({opSnippet:fae,packedOpSnippet:gae,cpuKernelImpl:KJ}),yae={kernelName:to,backendName:"webgl",kernelFunc:bae},xae=Ip+` +`,Oae=Ye({opSnippet:Mae,packedOpSnippet:Pae,cpuKernelImpl:m9}),Lae={kernelName:ao,backendName:"webgl",kernelFunc:Oae},zae=Ip+` return log(1.0 + x); -`,vae=Ye({opSnippet:xae}),wae={kernelName:no,backendName:"webgl",kernelFunc:vae},kae="return float(a >= 1.0 && b >= 1.0);",Iae=` +`,Wae=Ye({opSnippet:zae}),Bae={kernelName:ro,backendName:"webgl",kernelFunc:Wae},Vae="return float(a >= 1.0 && b >= 1.0);",Uae=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,Sae=fn({opSnippet:kae,packedOpSnippet:Iae,dtype:"bool"}),Nae={kernelName:_u,backendName:"webgl",kernelFunc:Sae},Tae="return float(!(x >= 1.0));",Cae=Ye({opSnippet:Tae}),_ae={kernelName:Eu,backendName:"webgl",kernelFunc:Cae},Eae="return float(a >= 1.0 || b >= 1.0);",Aae=` +`,Gae=fn({opSnippet:Vae,packedOpSnippet:Uae,dtype:"bool"}),Hae={kernelName:Eu,backendName:"webgl",kernelFunc:Gae},qae="return float(!(x >= 1.0));",jae=Ye({opSnippet:qae}),Kae={kernelName:Au,backendName:"webgl",kernelFunc:jae},Xae="return float(a >= 1.0 || b >= 1.0);",Yae=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,Fae=fn({opSnippet:Eae,packedOpSnippet:Aae,dtype:"bool"}),$ae={kernelName:Au,backendName:"webgl",kernelFunc:Fae},Dae=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` +`,Zae=fn({opSnippet:Xae,packedOpSnippet:Yae,dtype:"bool"}),Jae={kernelName:Fu,backendName:"webgl",kernelFunc:Zae},Qae=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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]; @@ -3638,7 +3642,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam float val = x * ${o}; setOutput(val); } - `}},Rae=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` + `}},ere=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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; @@ -3700,7 +3704,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam vec4 result = xAtOutputCoords * ${o}; setOutput(result); } - `}},Mae=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=G().getBool("WEBGL_PACK_NORMALIZATION")?new Rae(r.shape,s,i,o,l):new Dae(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},Pae={kernelName:ao,backendName:"webgl",kernelFunc:Mae},Oae=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=` + `}},tre=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=G().getBool("WEBGL_PACK_NORMALIZATION")?new ere(r.shape,s,i,o,l):new Qae(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},nre={kernelName:so,backendName:"webgl",kernelFunc:tre},are=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]; @@ -3755,16 +3759,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(result); } - `}},Lae=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new Oae(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},zae={kernelName:Fu,backendName:"webgl",kernelFunc:Lae};function Wae(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Ko(i,e.dtype,"max",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function SA(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let y=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new are(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},sre={kernelName:$u,backendName:"webgl",kernelFunc:rre};function ire(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Yo(i,e.dtype,"max",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function MA(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let y=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=N.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return aa({inputs:{x:r},backend:n});let d=new Ec(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var jae={kernelName:io,backendName:"webgl",kernelFunc:qae};function Kae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=N.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new tk(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var Xae={kernelName:$u,backendName:"webgl",kernelFunc:Kae},Yae=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=` +`,pre=fn({opSnippet:lre,packedOpSnippet:ure,cpuKernelImpl:g9}),cre={kernelName:oo,backendName:"webgl",kernelFunc:pre};function dre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;yp(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=N.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return ra({inputs:{x:r},backend:n});let d=new Ec(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var hre={kernelName:lo,backendName:"webgl",kernelFunc:dre};function mre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=N.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new ak(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var fre={kernelName:Du,backendName:"webgl",kernelFunc:mre},gre=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}, ${o}); void main() { @@ -3810,7 +3814,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(dotProd); } - `}},Zae=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,d=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` + `}},bre=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,d=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${p}, ${d}, ${c}); void main() { @@ -3874,16 +3878,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam } setOutput(dotProd); } - `}};function Jae(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=N.computePool3DInfo(i.shape,o,l,d,u,p),h=new tk(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new Zae(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var Qae={kernelName:Wc,backendName:"webgl",kernelFunc:Jae};function ere(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;yp([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=N.computePool2DInfo(o.shape,l,u,1,p,d),h=!0,m=new Ec(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new Yae(c),b=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),b}var tre={kernelName:Bm,backendName:"webgl",kernelFunc:ere};function nre(e,t,n,a){let r=new Ec(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Ec(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var are={kernelName:Vm,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];v.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=N.computePool2DInfo(a.shape,r,s,u,i),[d,c]=nre(a,o,p,l);return[d,c]}};function rre(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Ko(i,"float32","mean",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var sre={kernelName:oo,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=v.parseAxisParam(s,a.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,w=new Array(o);for(let C=0;C{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];v.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=N.computePool2DInfo(a.shape,r,s,u,i),[d,c]=kre(a,o,p,l);return[d,c]}};function Sre(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Yo(i,"float32","mean",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var Nre={kernelName:uo,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=v.parseAxisParam(s,a.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,w=new Array(o);for(let C=0;Cu[0]+e[p]+u[1]);let a=e.length,r=dt(a),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=` +`,Are=fn({opSnippet:_re,packedOpSnippet:Ere,cpuKernelImpl:b9}),Fre={kernelName:co,backendName:"webgl",kernelFunc:Are},$re=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,p)=>u[0]+e[p]+u[1]);let a=e.length,r=dt(a),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).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}; @@ -3912,7 +3916,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam ${r} coords = outC - start; setOutput(getX(${o})); } - `}},hre=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=dt(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=Sn("rc",a),l=Sn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,c="";if(a===1){let h=` + `}},Dre=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=dt(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=Sn("rc",a),l=Sn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,c="";if(a===1){let h=` ${r} source = rc; if (source < start) { source = start * 2 - source - ${d}; @@ -3968,13 +3972,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam ${c} setOutput(result); } - `}},mre=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new hre(a.shape,r,s):new dre(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},fre={kernelName:po,backendName:"webgl",kernelFunc:mre},gre=`if (b == 0.0) return NAN; - return mod(a, b);`,bre=` + `}},Rre=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Dre(a.shape,r,s):new $re(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},Mre={kernelName:ho,backendName:"webgl",kernelFunc:Rre},Pre=`if (b == 0.0) return NAN; + return mod(a, b);`,Ore=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - `+jo+` + `+Xo+` return result; -`,yre=fn({opSnippet:gre,packedOpSnippet:bre}),xre={kernelName:Du,backendName:"webgl",kernelFunc:yre},vre=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` +`,Lre=fn({opSnippet:Pre,packedOpSnippet:Ore}),zre={kernelName:Ru,backendName:"webgl",kernelFunc:Lre},Wre=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -3994,11 +3998,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JQ=Ye({opSnippet:ZQ}),QQ={kernelNam // If no other event happened, last event happened. setOutput(float(${t-1})); } - `}},wre=` + `}},Bre=` if (a == b) { return 1.0; }; -return a / b;`,kre=` +return a / b;`,Vre=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -4016,9 +4020,9 @@ return a / b;`,kre=` } return result; -`,NA=fn({opSnippet:wre,packedOpSnippet:kre,checkOutOfBounds:!0}),Ire={kernelName:Vi,backendName:"webgl",kernelFunc:NA},kS="return a - b;",TA=fn({opSnippet:kS,packedOpSnippet:kS,supportsComplex:!0,cpuKernelImpl:y9}),Sre={kernelName:Mo,backendName:"webgl",kernelFunc:TA};function CA(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=v.parseAxisParam([s],r.shape),o=SA({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=ce({inputs:{x:o},backend:n,attrs:{shape:l}}),p=TA({inputs:{a:r,b:u},backend:n}),d=wA({inputs:{x:p},backend:n}),c=Qf({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=ce({inputs:{x:c},backend:n,attrs:{shape:l}}),m=NA({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var Nre={kernelName:Do,backendName:"webgl",kernelFunc:CA};function Tre(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:CA({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new vre(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var Cre={kernelName:Ru,backendName:"webgl",kernelFunc:Tre},_re=Oa+` +`,PA=fn({opSnippet:Bre,packedOpSnippet:Vre,checkOutOfBounds:!0}),Ure={kernelName:Gi,backendName:"webgl",kernelFunc:PA},NS="return a - b;",OA=fn({opSnippet:NS,packedOpSnippet:NS,supportsComplex:!0,cpuKernelImpl:L9}),Gre={kernelName:Oo,backendName:"webgl",kernelFunc:OA};function LA(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=v.parseAxisParam([s],r.shape),o=MA({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=ce({inputs:{x:o},backend:n,attrs:{shape:l}}),p=OA({inputs:{a:r,b:u},backend:n}),d=$A({inputs:{x:p},backend:n}),c=eg({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=ce({inputs:{x:c},backend:n,attrs:{shape:l}}),m=PA({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var Hre={kernelName:Mo,backendName:"webgl",kernelFunc:LA};function qre(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:LA({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new Wre(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var jre={kernelName:Mu,backendName:"webgl",kernelFunc:qre},Kre=Oa+` return -x; -`,Ere=` +`,Xre=` vec4 result = -x; bvec4 isNaN = isnan(x); @@ -4028,14 +4032,14 @@ return a / b;`,kre=` result.a = isNaN.a ? x.a : result.a; return result; -`;function Are(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=QJ(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ss(a.shape,Ere):r=new ir(a.shape,_re),n.runWebGLProgram(r,[a],a.dtype)}var Fre={kernelName:Mu,backendName:"webgl",kernelFunc:Are},$re=fr.nonMaxSuppressionV3Impl;function Dre(e){N.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,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=$re(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Rre={kernelName:Ou,backendName:"webgl",kernelFunc:Dre},Mre=fr.nonMaxSuppressionV4Impl;function Pre(e){N.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:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=Mre(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Ore={kernelName:Lu,backendName:"webgl",kernelFunc:Pre},Lre=fr.nonMaxSuppressionV5Impl;function zre(e){N.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:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:b}=Lre(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var Wre={kernelName:zu,backendName:"webgl",kernelFunc:zre},Bre=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` +`;function Yre(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=x9(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new is(a.shape,Xre):r=new ir(a.shape,Kre),n.runWebGLProgram(r,[a],a.dtype)}var Zre={kernelName:Pu,backendName:"webgl",kernelFunc:Yre},Jre=fr.nonMaxSuppressionV3Impl;function Qre(e){N.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,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=Jre(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var ese={kernelName:Lu,backendName:"webgl",kernelFunc:Qre},tse=fr.nonMaxSuppressionV4Impl;function nse(e){N.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:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=tse(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var ase={kernelName:zu,backendName:"webgl",kernelFunc:nse},rse=fr.nonMaxSuppressionV5Impl;function sse(e){N.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:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:b}=rse(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var ise={kernelName:Wu,backendName:"webgl",kernelFunc:sse},ose=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(${n}), float(index == coords.y))); } - `}},Vre=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=v.sizeFromShape(r.shape),p=new Bre(u,i,o,l),d=ce({inputs:{x:r},backend:n,attrs:{shape:[u]}}),c=n.runWebGLProgram(p,[d],s);n.disposeIntermediateTensorInfo(d);let h=[...r.shape,i],m=ce({inputs:{x:c},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(c),m},Ure={kernelName:ho,backendName:"webgl",kernelFunc:Vre};function Tm(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=$d({inputs:{input:a},backend:n}),s=Tm({inputs:{x:r},backend:n}),i=eg({inputs:{input:a},backend:n}),o=Tm({inputs:{x:i},backend:n}),l=Ds({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Dd({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var Gre={kernelName:sp,backendName:"webgl",kernelFunc:Tm};function _A(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=$d({inputs:{input:a},backend:n}),s=_A({inputs:{x:r},backend:n}),i=eg({inputs:{input:a},backend:n}),o=Tm({inputs:{x:i},backend:n}),l=Ds({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Dd({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var Hre={kernelName:Wu,backendName:"webgl",kernelFunc:_A};function qre(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return uv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=uv({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=hA({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var jre={kernelName:Bu,backendName:"webgl",kernelFunc:qre},Kre=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=dt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` + `}},lse=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=v.sizeFromShape(r.shape),p=new ose(u,i,o,l),d=ce({inputs:{x:r},backend:n,attrs:{shape:[u]}}),c=n.runWebGLProgram(p,[d],s);n.disposeIntermediateTensorInfo(d);let h=[...r.shape,i],m=ce({inputs:{x:c},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(c),m},use={kernelName:fo,backendName:"webgl",kernelFunc:lse};function Tm(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=$d({inputs:{input:a},backend:n}),s=Tm({inputs:{x:r},backend:n}),i=tg({inputs:{input:a},backend:n}),o=Tm({inputs:{x:i},backend:n}),l=Rs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Dd({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var pse={kernelName:ip,backendName:"webgl",kernelFunc:Tm};function zA(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=$d({inputs:{input:a},backend:n}),s=zA({inputs:{x:r},backend:n}),i=tg({inputs:{input:a},backend:n}),o=Tm({inputs:{x:i},backend:n}),l=Rs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Dd({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var cse={kernelName:Bu,backendName:"webgl",kernelFunc:zA};function dse(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return pv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=pv({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=SA({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var hse={kernelName:Vu,backendName:"webgl",kernelFunc:dse},mse=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=dt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -4060,7 +4064,7 @@ return a / b;`,kre=` setOutput(getX(${o})); } } - `}},Xre=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=dt(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=Sn("rc",a),l=Sn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${o[a-1]} += 1; + `}},fse=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=dt(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=Sn("rc",a),l=Sn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${o[a-1]} += 1; if(${u}) { `,a===1?"":`} rc = outputLoc; @@ -4084,7 +4088,7 @@ return a / b;`,kre=` ${h} setOutput(result); } - `}},EA=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return Dd({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Xre(r.shape,s,i):new Kre(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},Yre={kernelName:mo,backendName:"webgl",kernelFunc:EA},Zre=` + `}},WA=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return Dd({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fse(r.shape,s,i):new mse(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},gse={kernelName:go,backendName:"webgl",kernelFunc:WA},bse=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4093,7 +4097,7 @@ return a / b;`,kre=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,Jre=` +`,yse=` // 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); @@ -4109,11 +4113,11 @@ return a / b;`,kre=` bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); - `+jo+` + `+Xo+` return result; -`,Qre=fn({opSnippet:Zre,packedOpSnippet:Jre}),ese={kernelName:fo,backendName:"webgl",kernelFunc:Qre};function tse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,d=N.getAxesPermutation(p,o),c=r;d!=null&&(c=Nn({inputs:{x:r},backend:n,attrs:{perm:d}}),p=N.getInnerMostAxes(p.length,o),l.push(c)),N.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:b}=t9(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,b,f)}else{let[m,f]=N.computeOutAndReduceShapes(c.shape,p),g=v.sizeFromShape(f),b=ce({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),y=Km(r.dtype),x=Ko(b,y,"prod",n);h=ce({inputs:{x},backend:n,attrs:{shape:m}}),l.push(b),l.push(x)}if(i){l.push(h);let m=N.expandShapeToKeepDim(h.shape,u);h=ce({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var nse={kernelName:bo,backendName:"webgl",kernelFunc:tse};function ase(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(b=>n.readSync(b.dataId)),u=r.map(b=>b.shape),p=n.readSync(s.dataId),d=n.readSync(i.dataId),[c,h,m]=n9(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(b=>n.makeTensorInfo([b.length],"int32",b)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var rse={kernelName:Um,backendName:"webgl",kernelFunc:ase};function sse(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=a9(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var ise={kernelName:Gm,backendName:"webgl",kernelFunc:sse};function ose(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=n.readSync(i.dataId),c=o.map(g=>n.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=r9(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var lse={kernelName:Hm,backendName:"webgl",kernelFunc:ose},AA=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=s9(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},use={kernelName:Bc,backendName:"webgl",kernelFunc:AA},pse="return 1.0 / x;",cse=Ye({opSnippet:pse}),dse={kernelName:yo,backendName:"webgl",kernelFunc:cse},hse=Oa+` +`,xse=fn({opSnippet:bse,packedOpSnippet:yse}),vse={kernelName:bo,backendName:"webgl",kernelFunc:xse};function wse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,d=N.getAxesPermutation(p,o),c=r;d!=null&&(c=Nn({inputs:{x:r},backend:n,attrs:{perm:d}}),p=N.getInnerMostAxes(p.length,o),l.push(c)),N.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:b}=w9(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,b,f)}else{let[m,f]=N.computeOutAndReduceShapes(c.shape,p),g=v.sizeFromShape(f),b=ce({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),y=Xm(r.dtype),x=Yo(b,y,"prod",n);h=ce({inputs:{x},backend:n,attrs:{shape:m}}),l.push(b),l.push(x)}if(i){l.push(h);let m=N.expandShapeToKeepDim(h.shape,u);h=ce({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var kse={kernelName:xo,backendName:"webgl",kernelFunc:wse};function Ise(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(b=>n.readSync(b.dataId)),u=r.map(b=>b.shape),p=n.readSync(s.dataId),d=n.readSync(i.dataId),[c,h,m]=k9(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(b=>n.makeTensorInfo([b.length],"int32",b)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var Sse={kernelName:Gm,backendName:"webgl",kernelFunc:Ise};function Nse(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=I9(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var Tse={kernelName:Hm,backendName:"webgl",kernelFunc:Nse};function Cse(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=n.readSync(i.dataId),c=o.map(g=>n.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=S9(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var _se={kernelName:qm,backendName:"webgl",kernelFunc:Cse},BA=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=N9(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},Ese={kernelName:Bc,backendName:"webgl",kernelFunc:BA},Ase="return 1.0 / x;",Fse=Ye({opSnippet:Ase}),$se={kernelName:vo,backendName:"webgl",kernelFunc:Fse},Dse=Oa+` return (x < 0.0) ? 0.0 : x; -`,mse=` +`,Rse=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4123,9 +4127,9 @@ return a / b;`,kre=` result.a = isNaN.a ? x.a : result.a; return result; -`,fse=Ye({opSnippet:hse,packedOpSnippet:mse}),gse={kernelName:xo,backendName:"webgl",kernelFunc:fse},bse=Oa+` +`,Mse=Ye({opSnippet:Dse,packedOpSnippet:Rse}),Pse={kernelName:wo,backendName:"webgl",kernelFunc:Mse},Ose=Oa+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,yse=` +`,Lse=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4135,7 +4139,7 @@ return a / b;`,kre=` result.a = isNaN.a ? x.a : result.a; return result; -`,xse=Ye({opSnippet:bse,packedOpSnippet:yse}),vse={kernelName:ko,backendName:"webgl",kernelFunc:xse},wse=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,zse=Ye({opSnippet:Ose,packedOpSnippet:Lse}),Wse={kernelName:So,backendName:"webgl",kernelFunc:zse},Bse=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4168,7 +4172,7 @@ return a / b;`,kre=` setOutput(newValue); } - `}},kse=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},Vse=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/p[0]}, ${u[1]/p[1]}, @@ -4245,7 +4249,7 @@ return a / b;`,kre=` setOutput(newValue); } - `}};function Ise(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new kse(r.shape,l,u,s,i):new wse(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var Sse={kernelName:wo,backendName:"webgl",kernelFunc:Ise},Nse=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],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + `}};function Use(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Vse(r.shape,l,u,s,i):new Bse(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var Gse={kernelName:Io,backendName:"webgl",kernelFunc:Use},Hse=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],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4326,7 +4330,7 @@ return a / b;`,kre=` setOutput(accumulator); } - `}};function Tse(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Nse(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Cse={kernelName:Gu,backendName:"webgl",kernelFunc:Tse},_se=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function qse(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Hse(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var jse={kernelName:Hu,backendName:"webgl",kernelFunc:qse},Kse=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4348,7 +4352,7 @@ return a / b;`,kre=` setOutput(newValue); } - `}},Ese=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},Xse=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 u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/p[0]}, ${u[1]/p[1]}, @@ -4389,7 +4393,7 @@ return a / b;`,kre=` setOutput(newValue); } - `}};function Ase(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Ese(r.shape,l,u,s,i):new _se(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var Fse={kernelName:vo,backendName:"webgl",kernelFunc:Ase},$se=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],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + `}};function Yse(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Xse(r.shape,l,u,s,i):new Kse(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var Zse={kernelName:ko,backendName:"webgl",kernelFunc:Yse},Jse=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],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4459,7 +4463,7 @@ return a / b;`,kre=` setOutput(accumulator); } - `}};function Dse(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new $se(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Rse={kernelName:Uu,backendName:"webgl",kernelFunc:Dse},Mse=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` + `}};function Qse(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Jse(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var eie={kernelName:Gu,backendName:"webgl",kernelFunc:Qse},tie=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)); @@ -4469,7 +4473,7 @@ return a / b;`,kre=` ${s} coords = getOutputCoords(); setOutput(getX(${r})); } - `}},Pse=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-${n} tensor is not yet supported`);this.outputShape=e;let a=Sn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=dt(n);n===1?this.userCode=` + `}},nie=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let a=Sn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=dt(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -4497,7 +4501,7 @@ return a / b;`,kre=` } setOutput(result); } - `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((b,y)=>c(y,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function Ose(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return aa({inputs:{x:r},backend:n});let l=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Pse(r.shape,o):new Mse(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var Lse={kernelName:Io,backendName:"webgl",kernelFunc:Ose},zse=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` + `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((b,y)=>c(y,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function aie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return ra({inputs:{x:r},backend:n});let l=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new nie(r.shape,o):new tie(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var rie={kernelName:No,backendName:"webgl",kernelFunc:aie},sie=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` vec3 fill = vec3(${t.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -4516,7 +4520,7 @@ return a / b;`,kre=` } setOutput(outputValue); } - `}},Wse={kernelName:ip,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new zse(a.shape,s),[u,p]=N.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},Bse=` + `}},iie={kernelName:op,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new sie(a.shape,s),[u,p]=N.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},oie=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4531,7 +4535,7 @@ return a / b;`,kre=` return base + 1.0; } } -`,Vse=Ye({opSnippet:Bse}),Use={kernelName:So,backendName:"webgl",kernelFunc:Vse},Gse="return inversesqrt(x);",Hse=Ye({opSnippet:Gse,cpuKernelImpl:i9}),qse={kernelName:No,backendName:"webgl",kernelFunc:Hse},nk=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let l=dt(r.length),u=dt(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides";this.userCode=` +`,lie=Ye({opSnippet:oie}),uie={kernelName:To,backendName:"webgl",kernelFunc:lie},pie="return inversesqrt(x);",cie=Ye({opSnippet:pie,cpuKernelImpl:T9}),die={kernelName:Co,backendName:"webgl",kernelFunc:cie},rk=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let l=dt(r.length),u=dt(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides";this.userCode=` ${l} strides = ${l}(${r}); void main() { @@ -4551,7 +4555,7 @@ return a / b;`,kre=` } setOutput(mix(${f}, sum, float(found))); } - `}},jse=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=dt(r.length),u=dt(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides",b=t>1?"strides[j + 1]":"strides";this.userCode=` + `}},hie=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=dt(r.length),u=dt(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides",b=t>1?"strides[j + 1]":"strides";this.userCode=` ${l} strides = ${l}(${r}); void main() { @@ -4588,7 +4592,7 @@ return a / b;`,kre=` } setOutput(mix(${f}, sum, found)); } - `}};function Kse(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=N.calculateShapes(s,r,i),c=[d/u,u];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=ce({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=ce({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g;G().getBool("WEBGL_PACK")?g=new jse(l,o,h.shape.length,m.shape.length,p,c):g=new nk(l,o,h.shape.length,m.shape.length,p,c);let b=n.runWebGLProgram(g,[m,h,f],m.dtype),y=ce({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(f),y}var Xse={kernelName:Hu,backendName:"webgl",kernelFunc:Kse},Yse=class{constructor(e,t,n,a){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=G().getNumber("WEBGL_VERSION")===2?r:s,o=a==="left"?"<":"<=";this.userCode=` + `}};function mie(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=N.calculateShapes(s,r,i),c=[d/u,u];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=ce({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=ce({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g;G().getBool("WEBGL_PACK")?g=new hie(l,o,h.shape.length,m.shape.length,p,c):g=new rk(l,o,h.shape.length,m.shape.length,p,c);let b=n.runWebGLProgram(g,[m,h,f],m.dtype),y=ce({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(f),y}var fie={kernelName:qu,backendName:"webgl",kernelFunc:mie},gie=class{constructor(e,t,n,a){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=G().getNumber("WEBGL_VERSION")===2?r:s,o=a==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; @@ -4613,7 +4617,7 @@ return a / b;`,kre=` setOutput(float(findBound(batch, value))); } - `}};function Zse(e){let{inputs:t,backend:n,attrs:a}=e,{sortedSequence:r,values:s}=t,{side:i}=a,o=new Yse(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return n.runWebGLProgram(o,[r,s],"int32",l)}var Jse={kernelName:ju,backendName:"webgl",kernelFunc:Zse},Qse=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 u=0;u4)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 u=0;u= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,aie=Ye({opSnippet:nie}),rie={kernelName:To,backendName:"webgl",kernelFunc:aie},sie=Ip+` +`,Iie=Ye({opSnippet:kie}),Sie={kernelName:_o,backendName:"webgl",kernelFunc:Iie},Nie=Ip+` return 1.0 / (1.0 + exp(-1.0 * x)); -`,iie=` +`,Tie=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -4641,20 +4645,20 @@ return a / b;`,kre=` result.a = isNaN.a ? x.a : result.a; return result; 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+`,Oie=Ye({opSnippet:Pie}),Lie={kernelName:Zu,backendName:"webgl",kernelFunc:Oie},zie=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -4674,17 +4678,17 @@ return a / b;`,kre=` result = log(exp_x + 1.0); } return result; -`,vie=Ye({opSnippet:xie}),wie={kernelName:Ao,backendName:"webgl",kernelFunc:vie},kie=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,y)=>b*y),l=[[0,0]];l.push(...i);for(let b=1+s.length;bn.disposeIntermediateTensorInfo(b)),g},Iie={kernelName:Zu,backendName:"webgl",kernelFunc:kie};function Sie(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: 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goe(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=a,{data:p,dataSplits:d}=t,c=n.readSync(p.dataId),h=n.readSync(d.dataId),[m,f]=M9(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(d.shape,"int32",f)]}var boe={kernelName:jc,backendName:"webgl",kernelFunc:goe};function yoe(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(s.dataId),l=n.readSync(i.dataId)[0],[u,p,d]=P9(o,l,r),c=p.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",p),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var xoe={kernelName:Kc,backendName:"webgl",kernelFunc:yoe};function 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h=ce({inputs:{x:s},backend:n,attrs:{shape:[l,o]}}),m=ce({inputs:{x:i},backend:n,attrs:{shape:[l,u]}}),f=ce({inputs:{x:r},backend:n,attrs:{shape:c}}),g=new nk(l,o,h.shape.length,m.shape.length,p,c,!1,!0),b=n.runWebGLProgram(g,[m,h,f],f.dtype),y=ce({inputs:{x:b},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),y}var uoe={kernelName:qu,backendName:"webgl",kernelFunc:loe},poe=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(d=>v.decodeString(d)):o,u=ze(r.shape,r.dtype,l),p=x9(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new poe(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var doe={kernelName:Ss,backendName:"webgl",kernelFunc:FA},hoe=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` + `}};function Foe(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(d=>v.decodeString(d)):o,u=ze(r.shape,r.dtype,l),p=z9(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Aoe(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var $oe={kernelName:Ss,backendName:"webgl",kernelFunc:VA},Doe=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4740,7 +4744,7 @@ return a / b;`,kre=` setOutput(float(i1)); } } - `}},moe=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` + `}},Roe=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); @@ -4774,7 +4778,7 @@ return a / b;`,kre=` setOutput(x0 >= x1 ? float(i0) : float(i1)); } - `}};function Hs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function NS(e){let t=1;for(;tl){let F=n.readSync(r.dataId),[D,$]=v9(F,u,r.dtype,s,i);return[n.makeTensorInfo(D.shape,D.dtype,D.values),n.makeTensorInfo($.shape,$.dtype,$.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(p===1)return[r,Dd({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),c=d!==null&&d.isPacked,h=c?n.unpackTensor(r):r,m=v.sizeFromShape(u)/p,f=ce({inputs:{x:h},attrs:{shape:[m,p]},backend:n});c&&Hs(n,h);let g=NS(s),b=NS(p),y=null,x=()=>y===null?[f,f]:[f,y],w=(F,D,$)=>{let S=x(),M=new hoe($),B=[[p],[y===null?1:0],[Number.NEGATIVE_INFINITY],[F],[D]],U=y;y=n.runWebGLProgram(M,S,"int32",B),Hs(n,U)};for(let F=1;F=1;$/=2)w(D,$,[m,b])}for(let F=b;F>g;F/=2){let D=x(),$=new moe([m,F/2]),S=[[p],[y===null?1:0],[g]],M=y;y=n.runWebGLProgram($,D,"int32",S),Hs(n,M);let B=g/2,U=B*2;for(let H=B;H>=1;H/=2)w(U,H,y.shape)}let I=y;y=Sp({inputs:{x:y},backend:n,attrs:{begin:0,size:[m,s]}}),Hs(n,I);let T=IA({inputs:{x:f,indices:y},backend:n,attrs:{axis:1,batchDims:1}});Hs(n,f);let C=u.slice(0,-1);C.push(s),I=y,y=ce({inputs:{x:y},attrs:{shape:C},backend:n}),Hs(n,I);let E=T;return T=ce({inputs:{x:T},attrs:{shape:C},backend:n}),Hs(n,E),[T,y]}var goe={kernelName:np,backendName:"webgl",kernelFunc:foe},boe=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` + `}};function qs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function _S(e){let t=1;for(;tl){let F=n.readSync(r.dataId),[D,$]=W9(F,u,r.dtype,s,i);return[n.makeTensorInfo(D.shape,D.dtype,D.values),n.makeTensorInfo($.shape,$.dtype,$.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(p===1)return[r,Dd({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),c=d!==null&&d.isPacked,h=c?n.unpackTensor(r):r,m=v.sizeFromShape(u)/p,f=ce({inputs:{x:h},attrs:{shape:[m,p]},backend:n});c&&qs(n,h);let g=_S(s),b=_S(p),y=null,x=()=>y===null?[f,f]:[f,y],w=(F,D,$)=>{let S=x(),M=new Doe($),B=[[p],[y===null?1:0],[Number.NEGATIVE_INFINITY],[F],[D]],U=y;y=n.runWebGLProgram(M,S,"int32",B),qs(n,U)};for(let F=1;F=1;$/=2)w(D,$,[m,b])}for(let F=b;F>g;F/=2){let D=x(),$=new Roe([m,F/2]),S=[[p],[y===null?1:0],[g]],M=y;y=n.runWebGLProgram($,D,"int32",S),qs(n,M);let B=g/2,U=B*2;for(let H=B;H>=1;H/=2)w(U,H,y.shape)}let I=y;y=Sp({inputs:{x:y},backend:n,attrs:{begin:0,size:[m,s]}}),qs(n,I);let T=RA({inputs:{x:f,indices:y},backend:n,attrs:{axis:1,batchDims:1}});qs(n,f);let C=u.slice(0,-1);C.push(s),I=y,y=ce({inputs:{x:y},attrs:{shape:C},backend:n}),qs(n,I);let E=T;return T=ce({inputs:{x:T},attrs:{shape:C},backend:n}),qs(n,E),[T,y]}var Poe={kernelName:ap,backendName:"webgl",kernelFunc:Moe},Ooe=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; if(${o} == 2) { @@ -4886,7 +4890,7 @@ return a / b;`,kre=` } setOutput(outputValue); } - `}};function yoe(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[p,d,c,h]=r.shape,[m,f]=u!=null?u:[d,c],g=[p,m,f,h],b=new boe(d,c,i,o,l,g);return n.runWebGLProgram(b,[r,s],"float32")}var xoe={kernelName:ap,backendName:"webgl",kernelFunc:yoe};function voe(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;yp(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as 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uue(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,b=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),y=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),w=t.dataIdMap.get(f.dataId).id;return eF(g,s,i==="NHWC"?1:0,b,r.shape.length-1,y,x,m.length,w),f}var pue={kernelName:gu,backendName:"wasm",setupFunc:lue,kernelFunc:uue},tF;function cue(e){tF=e.wasm.cwrap(Wi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function due(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:u,pad:p,dimRoundingMode:d}=n,c=u==null?[1,1]:u,h=N.computeConv2DInfo(r.shape,s.shape,l,c,p,d,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,x=h.padInfo.left,w=h.dilationHeight,I=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,E=h.inChannels,F=h.outChannels,D=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 $=a.makeOutput(h.outShape,"float32"),S=a.dataIdMap.get($.dataId).id;return tF(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,b,y,x,D,w,I,T,C,E,F,S),$}var hue={kernelName:Wi,backendName:"wasm",setupFunc:cue,kernelFunc:due},nF;function mue(e){nF=e.wasm.cwrap("Diag",null,["number","number","number","number"])}function fue(e){let{inputs:t,backend:n}=e,{x:a}=t,r=v.sizeFromShape(a.shape),s=n.makeOutput([...a.shape,...a.shape],a.dtype);return nF(n.dataIdMap.get(a.dataId).id,et[a.dtype],r,n.dataIdMap.get(s.dataId).id),s}var gue={kernelName:Lc,backendName:"wasm",setupFunc:mue,kernelFunc:fue},aF;function bue(e){aF=e.wasm.cwrap(Bi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function yue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;if(r.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${r.dtype} and ${s.dtype}`);let u=N.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=n.makeOutput(u.outShape,r.dtype);return aF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(p.dataId).id,et[r.dtype],u.batchSize,u.inChannels,u.inHeight,u.inWidth,u.outHeight,u.outWidth,u.strideHeight,u.strideWidth,u.dilationHeight,u.dilationWidth,u.filterHeight,u.filterWidth,u.padInfo.top,u.padInfo.left),p}var xue={kernelName:Bi,backendName:"wasm",setupFunc:bue,kernelFunc:yue},rF;function vue(e){rF=e.wasm.cwrap(Ol,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function wue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=a;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=N.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(s.shape,s.dtype);return rF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,et[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var kue={kernelName:Ol,backendName:"wasm",setupFunc:vue,kernelFunc:wue},sF;function Iue(e){sF=e.wasm.cwrap(Pl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Sue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=a;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=N.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(r.shape,r.dtype);return sF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,et[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var Nue={kernelName:Pl,backendName:"wasm",setupFunc:Iue,kernelFunc:Sue},Tue=Ze(Ui),iF;function Cue(e){iF=e.wasm.cwrap(bu,null,["number","number","number"])}function _ue(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=n.makeOutput(r.shape,"float32"),i=o=>n.dataIdMap.get(o.dataId).id;return iF(i(r),i(a),i(s)),s}var Eue={kernelName:bu,backendName:"wasm",setupFunc:Cue,kernelFunc:_ue},Aue=!1,Fue=on(xu,Aue,"bool"),$ue=Ze(Gi,"float32");function cv(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&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Wn({inputs:{x:r},backend:a,attrs:{shape:o}})}var Due={kernelName:vu,backendName:"wasm",kernelFunc:cv},Rue=Ze(Hi,"float32");function oF(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Mue={kernelName:zc,backendName:"wasm",kernelFunc:oF},lF;function Pue(e){lF=e.wasm.cwrap(wu,null,["number","number","number","number","number","number"])}function Oue(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return lF(s,o,l,u,p,i),r}var Lue={kernelName:wu,backendName:"wasm",kernelFunc:Oue,setupFunc:Pue},zue=Ze(qi),Wue=!1,Bue=on(ji,Wue),uF;function Vue(e){uF=e.wasm.cwrap(Ki,null,["number","number","number","number","number","number","number"])}function Uue(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return uF(p,d,c,h,m,r,g),f}var Gue={kernelName:Ki,backendName:"wasm",setupFunc:Vue,kernelFunc:Uue},pF;function Hue(e){pF=e.wasm.cwrap(si,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function que(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=Fc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return pF(b,j,K,Z,y,I,T,w,C,E,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var jue={kernelName:si,backendName:"wasm",setupFunc:Hue,kernelFunc:que},cF;function Kue(e){cF=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 Xue(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=Fc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return cF(b,j,K,Z,y,I,T,w,C,E,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Yue={kernelName:ii,backendName:"wasm",setupFunc:Kue,kernelFunc:Xue},dF;function Zue(e){dF=e.wasm.cwrap(Iu,null,["number","number","number","number","number","number","array","number"])}function Jue(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Uw.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let p=r.shape,d=p[p.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return dF(c,et[a.dtype],h,i,d,o,m,f),u}var Que={kernelName:Iu,backendName:"wasm",setupFunc:Zue,kernelFunc:Jue},hF;function epe(e){hF=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function tpe(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=v.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let C=0;C=0,()=>`GatherV2: the index value ${E} is not in [0, ${p-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Wn({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),m=Wn({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let b=c.shape.length-1,y=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,w=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(v.computeStrides(c.shape)).buffer),T=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer);return hF(y,et[r.dtype],I,b,x,d.batchSize,T,w),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var npe={kernelName:ku,backendName:"wasm",setupFunc:epe,kernelFunc:tpe},ape=!1,rpe=on(Su,ape,"bool"),spe=!1,ipe=on(Xi,spe,"bool"),ope=Ze(Zi,"bool"),lpe=Ze(Ji,"bool"),upe=Ze(Qi,"bool"),mF;function ppe(e){mF=e.wasm.cwrap(eo,null,["number","number","number","number"])}function cpe(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;mF(r,et[t.dtype],n,i)}return s}var dpe={kernelName:eo,backendName:"wasm",setupFunc:ppe,kernelFunc:cpe},hpe=!1,mpe=on(Nu,hpe,"bool"),fpe=!1,gpe=on(Tu,fpe,"bool"),fF;function bpe(e){fF=e.wasm.cwrap(Cu,null,["number","number","number","number"])}function ype(e){let{attrs:t,backend:n}=e,{start:a,stop:r,num:s}=t,i=Math.floor(s),o=n.makeOutput([i],"float32");return fF(n.dataIdMap.get(o.dataId).id,a,r,i),o}var xpe={kernelName:Cu,backendName:"wasm",setupFunc:bpe,kernelFunc:ype},vpe=Ze(to),wpe=Ze(no),kpe=!1,Ipe=on(_u,kpe,"bool"),Spe=Ze(Eu),Npe=!1,Tpe=on(Au,Npe,"bool"),Cpe=!1,_pe=on(HS,Cpe,"bool"),gF;function Epe(e){gF=e.wasm.cwrap(ao,null,["number","number","number","number","number","number","number"])}function Ape(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=n.makeOutput(r.shape,r.dtype);return gF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var Fpe={kernelName:ao,backendName:"wasm",setupFunc:Epe,kernelFunc:Ape},bF;function $pe(e){bF=e.wasm.cwrap(Fu,null,["number","number","number","number","number","number","number","number","number"])}function Dpe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let d=n.makeOutput(r.shape,r.dtype);return bF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,i.shape[3],o,l,u,p),d}var Rpe={kernelName:Fu,backendName:"wasm",setupFunc:$pe,kernelFunc:Dpe},yF;function Mpe(e){yF=e.wasm.cwrap(ro,null,["number","number","number","number"])}function Ppe(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:u,axes:p,originalAxes:d,inputWasTransposed:c}=Rs(i,r,t);if(c){let y=t.dataIdMap.get(u.dataId).id;l=u,o=y}let h=l.shape.length;N.assertAxesAreInnerMostDims("max",p,h);let[m,f]=N.computeOutAndReduceShapes(l.shape,p),g=v.sizeFromShape(f),b=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let y=t.dataIdMap.get(b.dataId).id;yF(o,et[i.dtype],g,y)}if(c&&t.disposeData(u.dataId),s){let y=N.expandShapeToKeepDim(b.shape,d);b.shape=y}return b}var Ope={kernelName:ro,backendName:"wasm",setupFunc:Mpe,kernelFunc:Ppe},Lpe=!1,zpe=on(so,Lpe),xF;function Wpe(e){xF=e.wasm.cwrap(io,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Bpe(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=N.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,b=p.dilationHeight,y=p.dilationWidth,x=p.strideHeight,w=p.strideWidth,I=p.inChannels,T=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(p.outShape,"float32"),E=a.dataIdMap.get(C.dataId).id;return xF(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,b,y,x,w,I,T,E),C}var Vpe={kernelName:io,backendName:"wasm",setupFunc:Wpe,kernelFunc:Bpe},vF;function Upe(e){vF=e.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Gpe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=N.computePool3DInfo(r.shape,s,i,1,o,l,u),d=n.makeOutput(p.outShape,r.dtype);return vF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),d}var Hpe={kernelName:$u,backendName:"wasm",setupFunc:Upe,kernelFunc:Gpe},wF;function qpe(e){wF=e.wasm.cwrap("MaxPool3DGrad",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 jpe(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a,p=N.computePool3DInfo(s.shape,i,o,1,l,u),d=n.makeOutput(s.shape,s.dtype);return wF(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),d}var Kpe={kernelName:Wc,backendName:"wasm",setupFunc:qpe,kernelFunc:jpe},kF;function Xpe(e){kF=e.wasm.cwrap(oo,null,["number, number, number"])}function Ype(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,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Rs(i,r,t),m=d;if(h){let w=t.dataIdMap.get(p.dataId).id;w!==o&&(u=p,l=w,m=N.getInnerMostAxes(m.length,u.shape.length))}N.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=N.computeOutAndReduceShapes(u.shape,m),b=v.sizeFromShape(g),y=u;u.dtype!=="float32"&&(y=Ms({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(y.dataId).id);let x=t.makeOutput(f,"float32");if(v.sizeFromShape(u.shape)!==0){let w=t.dataIdMap.get(x.dataId).id;kF(l,b,w)}if(h&&t.disposeData(p.dataId),s){let w=N.expandShapeToKeepDim(x.shape,c);x.shape=w}return u.dtype!=="float32"&&t.disposeData(y.dataId),x}var Zpe={kernelName:oo,backendName:"wasm",setupFunc:Xpe,kernelFunc:Ype},IF;function Jpe(e){IF=e.wasm.cwrap(lo,null,["number","number","number","number"])}function Qpe(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,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Rs(i,r,t);if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x)}let m=u.shape.length;N.assertAxesAreInnerMostDims("min",d,m);let[f,g]=N.computeOutAndReduceShapes(u.shape,d),b=v.sizeFromShape(g),y=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;IF(l,et[i.dtype],b,x)}if(h&&t.disposeData(p.dataId),s){let x=N.expandShapeToKeepDim(y.shape,c);y.shape=x}return y}var ece={kernelName:lo,backendName:"wasm",setupFunc:Jpe,kernelFunc:Qpe},tce=!1,nce=on(uo,tce),dv;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(dv||(dv={}));var SF;function ace(e){SF=e.wasm.cwrap(po,null,["number","array","number","number","array","array","number","number"])}function rce(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode: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,u=new Uint8Array(new Int32Array(t.shape).buffer),p=a.map(m=>m[0]),d=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new 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CF(n.dataIdMap.get(l.dataId).id,u,p,s,i,n.dataIdMap.get(d.dataId).id),o||n.disposeData(l.dataId),d}var pce={kernelName:Ru,backendName:"wasm",setupFunc:lce,kernelFunc:uce},cce=!0,dce=on(co,cce),hce=Ze(Mu);function ak(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 _F;function mce(e){_F=e.wasm.cwrap(Ou,"number",["number","number","number","number","number"])}function fce(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=_F(u,p,s,r,i),{pSelectedIndices:c,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=ak(t,d);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",c)}var gce={kernelName:Ou,backendName:"wasm",setupFunc:mce,kernelFunc:fce},EF;function bce(e){EF=e.wasm.cwrap(Lu,"number",["number","number","number","number","number","bool"])}function yce(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=EF(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=ak(t,c);t.wasm._free(f);let b=t.makeOutput([m],"int32",h),y=t.makeOutput([],"int32",g);return[b,y]}var xce={kernelName:Lu,backendName:"wasm",setupFunc:bce,kernelFunc:yce},AF;function vce(e){AF=e.wasm.cwrap(zu,"number",["number","number","number","number","number","number"])}function wce(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=AF(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=ak(t,c);t.wasm._free(g);let 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et;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(et||(et={}));var Fc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(Fc||(Fc={}));var UA;function Koe(e){UA=e.wasm.cwrap(ii,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Xoe(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank 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Zoe=Je(ru),Joe=Je(Si),Qoe=Je(Ni);function ln(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:p}=l,d=o.dataIdMap.get(u.dataId).id,c=o.dataIdMap.get(p.dataId).id,h=n!=null?n:u.dtype,m=N.assertAndGetBroadcastShape(u.shape,p.shape),f=o.makeOutput(m,h);if(v.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),b=new Uint8Array(new Int32Array(p.shape).buffer),y=o.dataIdMap.get(f.dataId).id;return a(d,g,u.shape.length,c,b,p.shape.length,et[u.dtype],y),f}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var ele=!0,tle=ln(ks,ele),GA;function nle(e){GA=e.wasm.cwrap(Ti,null,["array","number","number","number"])}function ale(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new 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Ble={kernelName:pu,backendName:"wasm",kernelFunc:Wle},QA;function Vle(e){QA=e.wasm.cwrap(cu,null,["number","number","boolean","number","number","number"])}function Ule(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,weights:s}=n,{size:i}=a,o=s.shape.reduce((d,c)=>d*c,1)!==0,l=r.shape.length===1?[i]:[r.shape[0],i],u=t.makeOutput(l,s.dtype);function p(d){return t.dataIdMap.get(d.dataId).id}return QA(p(r),i,o,p(s),et[s.dtype],p(u)),u}var Gle={kernelName:cu,backendName:"wasm",setupFunc:Vle,kernelFunc:Ule};function Hle(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.typedArrayFromHeap(a),i=n.typedArrayFromHeap(r),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeOutput([o.length],"int32",void 0,new Int32Array(o))}var qle={kernelName:Mc,backendName:"wasm",kernelFunc:Hle};function Ps(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var jle={kernelName:Ri,backendName:"wasm",kernelFunc:Ps},Kle=Je(Mi),eF;function Xle(e){eF=e.wasm.cwrap(Is,null,["number","number","number","number"])}function Yle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return eF(o,s,i,u),l}var Zle={kernelName:Is,backendName:"wasm",setupFunc:Xle,kernelFunc:Yle};function tF(e){let{inputs:t,backend:n}=e,a=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=t.map(h=>h.shape);N.assertParamsConsistent(r,a);let s=N.computeOutShape(t.map(h=>h.shape),a),i=t.filter(h=>v.sizeFromShape(h.shape)>0);if(i.length===1)return ng({inputs:{x:i[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(v.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(x=>{let w=[-1,v.sizeFromShape(x.shape.slice(a))];return 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b=N.getUndoAxesPermutation(u);g=vs({inputs:{x:c},attrs:{perm:b},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var wue={kernelName:fu,backendName:"wasm",setupFunc:xue,kernelFunc:vue},uF;function kue(e){uF=e.wasm.cwrap(Bi,null,["number","number","number","number","number","number"])}function Iue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),p=r;u!==null&&(p=vs({inputs:{x:r},attrs:{perm:u},backend:n}));let d=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumsum",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;uF(m,i?1:0,o?1:0,h,f,et[r.dtype]);let g=c;if(u!==null){let b=N.getUndoAxesPermutation(u);g=vs({inputs:{x:c},attrs:{perm:b},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Sue={kernelName:Bi,backendName:"wasm",setupFunc:kue,kernelFunc:Iue},pF;function Nue(e){pF=e.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function Tue(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,weights:s}=n,{size:i,binaryOutput:o}=a,l=s.shape.reduce((c,h)=>c*h,1)!==0,u=r.shape.length===1?[i]:[r.shape[0],i],p=t.makeOutput(u,s.dtype);function d(c){return t.dataIdMap.get(c.dataId).id}return pF(d(r),new Uint8Array(new Int32Array(r.shape).buffer),r.shape.length,i,l,d(s),et[s.dtype],o,d(p)),p}var Cue={kernelName:Oc,backendName:"wasm",setupFunc:Nue,kernelFunc:Tue},cF;function _ue(e){cF=e.wasm.cwrap(bu,null,["number","number","number","array","number","array","array","number","number"])}function Eue(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,b=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),y=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),w=t.dataIdMap.get(f.dataId).id;return cF(g,s,i==="NHWC"?1:0,b,r.shape.length-1,y,x,m.length,w),f}var Aue={kernelName:bu,backendName:"wasm",setupFunc:_ue,kernelFunc:Eue},dF;function Fue(e){dF=e.wasm.cwrap(Vi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function $ue(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:d}=n,c=u==null?[1,1]:u,h=N.computeConv2DInfo(r.shape,s.shape,l,c,p,d,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,x=h.padInfo.left,w=h.dilationHeight,I=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,E=h.inChannels,F=h.outChannels,D=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let $=a.makeOutput(h.outShape,"float32"),S=a.dataIdMap.get($.dataId).id;return dF(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,b,y,x,D,w,I,T,C,E,F,S),$}var Due={kernelName:Vi,backendName:"wasm",setupFunc:Fue,kernelFunc:$ue},hF;function Rue(e){hF=e.wasm.cwrap("Diag",null,["number","number","number","number"])}function Mue(e){let{inputs:t,backend:n}=e,{x:a}=t,r=v.sizeFromShape(a.shape),s=n.makeOutput([...a.shape,...a.shape],a.dtype);return hF(n.dataIdMap.get(a.dataId).id,et[a.dtype],r,n.dataIdMap.get(s.dataId).id),s}var Pue={kernelName:Lc,backendName:"wasm",setupFunc:Rue,kernelFunc:Mue},mF;function Oue(e){mF=e.wasm.cwrap(Ui,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Lue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;if(r.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. 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Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=N.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(s.shape,s.dtype);return fF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,et[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var Vue={kernelName:Ll,backendName:"wasm",setupFunc:Wue,kernelFunc:Bue},gF;function Uue(e){gF=e.wasm.cwrap(Ol,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Gue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=a;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=N.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(r.shape,r.dtype);return gF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,et[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var Hue={kernelName:Ol,backendName:"wasm",setupFunc:Uue,kernelFunc:Gue},que=Je(Hi),bF;function jue(e){bF=e.wasm.cwrap(yu,null,["number","number","number"])}function Kue(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=n.makeOutput(r.shape,"float32"),i=o=>n.dataIdMap.get(o.dataId).id;return bF(i(r),i(a),i(s)),s}var Xue={kernelName:yu,backendName:"wasm",setupFunc:jue,kernelFunc:Kue},Yue=!1,Zue=ln(vu,Yue,"bool"),Jue=Je(qi,"float32");function dv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Wn({inputs:{x:r},backend:a,attrs:{shape:o}})}var Que={kernelName:wu,backendName:"wasm",kernelFunc:dv},epe=Je(ji,"float32");function yF(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var tpe={kernelName:zc,backendName:"wasm",kernelFunc:yF},xF;function npe(e){xF=e.wasm.cwrap(ku,null,["number","number","number","number","number","number"])}function ape(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return xF(s,o,l,u,p,i),r}var rpe={kernelName:ku,backendName:"wasm",kernelFunc:ape,setupFunc:npe},spe=Je(Ki),ipe=!1,ope=ln(Xi,ipe),vF;function lpe(e){vF=e.wasm.cwrap(Yi,null,["number","number","number","number","number","number","number"])}function upe(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return vF(p,d,c,h,m,r,g),f}var ppe={kernelName:Yi,backendName:"wasm",setupFunc:lpe,kernelFunc:upe},wF;function cpe(e){wF=e.wasm.cwrap(oi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function dpe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=Fc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return wF(b,j,K,Z,y,I,T,w,C,E,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var hpe={kernelName:oi,backendName:"wasm",setupFunc:cpe,kernelFunc:dpe},kF;function mpe(e){kF=e.wasm.cwrap(li,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 fpe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=Fc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return kF(b,j,K,Z,y,I,T,w,C,E,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var gpe={kernelName:li,backendName:"wasm",setupFunc:mpe,kernelFunc:fpe},IF;function bpe(e){IF=e.wasm.cwrap(Su,null,["number","number","number","number","number","number","array","number"])}function ype(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Gw.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let p=r.shape,d=p[p.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return IF(c,et[a.dtype],h,i,d,o,m,f),u}var xpe={kernelName:Su,backendName:"wasm",setupFunc:bpe,kernelFunc:ype},SF;function vpe(e){SF=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function wpe(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=v.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let C=0;C=0,()=>`GatherV2: the index value ${E} is not in [0, ${p-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Wn({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),m=Wn({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let b=c.shape.length-1,y=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,w=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(v.computeStrides(c.shape)).buffer),T=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer);return SF(y,et[r.dtype],I,b,x,d.batchSize,T,w),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var kpe={kernelName:Iu,backendName:"wasm",setupFunc:vpe,kernelFunc:wpe},Ipe=!1,Spe=ln(Nu,Ipe,"bool"),Npe=!1,Tpe=ln(Zi,Npe,"bool"),Cpe=Je(Qi,"bool"),_pe=Je(eo,"bool"),Epe=Je(to,"bool"),NF;function Ape(e){NF=e.wasm.cwrap(no,null,["number","number","number","number"])}function Fpe(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;NF(r,et[t.dtype],n,i)}return s}var $pe={kernelName:no,backendName:"wasm",setupFunc:Ape,kernelFunc:Fpe},Dpe=!1,Rpe=ln(Tu,Dpe,"bool"),Mpe=!1,Ppe=ln(Cu,Mpe,"bool"),TF;function Ope(e){TF=e.wasm.cwrap(_u,null,["number","number","number","number"])}function Lpe(e){let{attrs:t,backend:n}=e,{start:a,stop:r,num:s}=t,i=Math.floor(s),o=n.makeOutput([i],"float32");return TF(n.dataIdMap.get(o.dataId).id,a,r,i),o}var zpe={kernelName:_u,backendName:"wasm",setupFunc:Ope,kernelFunc:Lpe},Wpe=Je(ao),Bpe=Je(ro),Vpe=!1,Upe=ln(Eu,Vpe,"bool"),Gpe=Je(Au),Hpe=!1,qpe=ln(Fu,Hpe,"bool"),jpe=!1,Kpe=ln(XS,jpe,"bool"),CF;function Xpe(e){CF=e.wasm.cwrap(so,null,["number","number","number","number","number","number","number"])}function Ype(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=n.makeOutput(r.shape,r.dtype);return CF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var Zpe={kernelName:so,backendName:"wasm",setupFunc:Xpe,kernelFunc:Ype},_F;function Jpe(e){_F=e.wasm.cwrap($u,null,["number","number","number","number","number","number","number","number","number"])}function Qpe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let d=n.makeOutput(r.shape,r.dtype);return _F(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,i.shape[3],o,l,u,p),d}var ece={kernelName:$u,backendName:"wasm",setupFunc:Jpe,kernelFunc:Qpe},EF;function tce(e){EF=e.wasm.cwrap(io,null,["number","number","number","number"])}function nce(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:u,axes:p,originalAxes:d,inputWasTransposed:c}=Ms(i,r,t);if(c){let y=t.dataIdMap.get(u.dataId).id;l=u,o=y}let h=l.shape.length;N.assertAxesAreInnerMostDims("max",p,h);let[m,f]=N.computeOutAndReduceShapes(l.shape,p),g=v.sizeFromShape(f),b=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let y=t.dataIdMap.get(b.dataId).id;EF(o,et[i.dtype],g,y)}if(c&&t.disposeData(u.dataId),s){let y=N.expandShapeToKeepDim(b.shape,d);b.shape=y}return b}var ace={kernelName:io,backendName:"wasm",setupFunc:tce,kernelFunc:nce},rce=!1,sce=ln(oo,rce),AF;function ice(e){AF=e.wasm.cwrap(lo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function oce(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=N.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,b=p.dilationHeight,y=p.dilationWidth,x=p.strideHeight,w=p.strideWidth,I=p.inChannels,T=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(p.outShape,"float32"),E=a.dataIdMap.get(C.dataId).id;return AF(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,b,y,x,w,I,T,E),C}var lce={kernelName:lo,backendName:"wasm",setupFunc:ice,kernelFunc:oce},FF;function uce(e){FF=e.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function pce(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=N.computePool3DInfo(r.shape,s,i,1,o,l,u),d=n.makeOutput(p.outShape,r.dtype);return FF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),d}var cce={kernelName:Du,backendName:"wasm",setupFunc:uce,kernelFunc:pce},$F;function dce(e){$F=e.wasm.cwrap("MaxPool3DGrad",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 hce(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a,p=N.computePool3DInfo(s.shape,i,o,1,l,u),d=n.makeOutput(s.shape,s.dtype);return $F(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),d}var mce={kernelName:Wc,backendName:"wasm",setupFunc:dce,kernelFunc:hce},DF;function fce(e){DF=e.wasm.cwrap(uo,null,["number, number, number"])}function gce(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,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Ms(i,r,t),m=d;if(h){let w=t.dataIdMap.get(p.dataId).id;w!==o&&(u=p,l=w,m=N.getInnerMostAxes(m.length,u.shape.length))}N.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=N.computeOutAndReduceShapes(u.shape,m),b=v.sizeFromShape(g),y=u;u.dtype!=="float32"&&(y=Ps({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(y.dataId).id);let x=t.makeOutput(f,"float32");if(v.sizeFromShape(u.shape)!==0){let w=t.dataIdMap.get(x.dataId).id;DF(l,b,w)}if(h&&t.disposeData(p.dataId),s){let w=N.expandShapeToKeepDim(x.shape,c);x.shape=w}return u.dtype!=="float32"&&t.disposeData(y.dataId),x}var bce={kernelName:uo,backendName:"wasm",setupFunc:fce,kernelFunc:gce},RF;function yce(e){RF=e.wasm.cwrap(po,null,["number","number","number","number"])}function xce(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,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Ms(i,r,t);if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x)}let m=u.shape.length;N.assertAxesAreInnerMostDims("min",d,m);let[f,g]=N.computeOutAndReduceShapes(u.shape,d),b=v.sizeFromShape(g),y=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;RF(l,et[i.dtype],b,x)}if(h&&t.disposeData(p.dataId),s){let x=N.expandShapeToKeepDim(y.shape,c);y.shape=x}return y}var vce={kernelName:po,backendName:"wasm",setupFunc:yce,kernelFunc:xce},wce=!1,kce=ln(co,wce),hv;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(hv||(hv={}));var MF;function Ice(e){MF=e.wasm.cwrap(ho,null,["number","array","number","number","array","array","number","number"])}function Sce(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode: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,u=new Uint8Array(new Int32Array(t.shape).buffer),p=a.map(m=>m[0]),d=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(d).buffer);return MF(i,u,t.shape.length,et[t.dtype],c,h,hv[r],l),o}var Nce={kernelName:ho,backendName:"wasm",kernelFunc:Sce,setupFunc:Ice},PF;function Tce(e){PF=e.wasm.cwrap(Mo,null,["number","number","number","number"])}function OF(e){let{backend:t,inputs:{logits:n},attrs:{dim:a}}=e,r=t.dataIdMap.get(n.dataId).id,s=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(s.dataId).id,o=n.shape[a],l=v.sizeFromShape(n.shape)/o;return v.sizeFromShape(s.shape)===0||PF(r,i,o,l),s}var Cce={kernelName:Mo,backendName:"wasm",setupFunc:Tce,kernelFunc:OF},LF;function _ce(e){LF=e.wasm.cwrap(Mu,null,["number","number","number","number","number","number"])}function Ece(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a;if(r.dtype!=="float32")throw new Error(`Tensor logits must have dtype float32, got ${r.dtype}`);let l=o?r:OF({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),[u,p]=l.shape,d=n.makeOutput([u,s],"int32");return LF(n.dataIdMap.get(l.dataId).id,u,p,s,i,n.dataIdMap.get(d.dataId).id),o||n.disposeData(l.dataId),d}var Ace={kernelName:Mu,backendName:"wasm",setupFunc:_ce,kernelFunc:Ece},Fce=!0,$ce=ln(mo,Fce),Dce=Je(Pu);function sk(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 zF;function Rce(e){zF=e.wasm.cwrap(Lu,"number",["number","number","number","number","number"])}function Mce(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=zF(u,p,s,r,i),{pSelectedIndices:c,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=sk(t,d);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",c)}var Pce={kernelName:Lu,backendName:"wasm",setupFunc:Rce,kernelFunc:Mce},WF;function Oce(e){WF=e.wasm.cwrap(zu,"number",["number","number","number","number","number","bool"])}function Lce(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=WF(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=sk(t,c);t.wasm._free(f);let b=t.makeOutput([m],"int32",h),y=t.makeOutput([],"int32",g);return[b,y]}var zce={kernelName:zu,backendName:"wasm",setupFunc:Oce,kernelFunc:Lce},BF;function Wce(e){BF=e.wasm.cwrap(Wu,"number",["number","number","number","number","number","number"])}function Bce(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=BF(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=sk(t,c);t.wasm._free(g);let b=t.makeOutput([m],"int32",h),y=t.makeOutput([m],"float32",f);return[b,y]}var Vce={kernelName:Wu,backendName:"wasm",setupFunc:Wce,kernelFunc:Bce},Uce=!1,Gce=ln(Ou,Uce,"bool"),VF;function Hce(e){VF=e.wasm.cwrap(fo,null,["number","number","number","number","number"])}function qce(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=n.makeOutput([...r.shape,i],s),p=n.dataIdMap.get(u.dataId).id,d=n.dataIdMap.get(r.dataId).id;return VF(d,i,o,l,p),u}var jce={kernelName:fo,backendName:"wasm",setupFunc:Hce,kernelFunc:qce};function Kce(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Xce={kernelName:Bu,backendName:"wasm",kernelFunc:Kce};function Yce(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return dv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching 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GF={kernelName:go,backendName:"wasm",kernelFunc:Qce,setupFunc:Jce},ede=!1,tde=ln(bo,ede),HF;function nde(e){HF=e.wasm.cwrap(yo,null,["number","number","number"])}function ade(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=s,l=a,u=l;l.dtype!=="float32"&&(u=Ps({backend:n,inputs:{x:a},attrs:{dtype:"float32"}}),o=n.dataIdMap.get(u.dataId).id);let p=n.makeOutput(a.shape,"float32"),d=n.dataIdMap.get(p.dataId).id;return HF(o,i,d),l.dtype!=="float32"&&n.disposeData(u.dataId),p}var rde={kernelName:yo,backendName:"wasm",setupFunc:nde,kernelFunc:ade},qF;function sde(e){qF=e.wasm.cwrap(xo,null,["number","number","number","number"])}function ide(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,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Ms(i,r,t),m=d;if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x,m=N.getInnerMostAxes(m.length,u.shape.length))}N.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=N.computeOutAndReduceShapes(u.shape,m),b=v.sizeFromShape(g),y=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;qF(l,b,et[y.dtype],x)}if(h&&t.disposeData(p.dataId),s){let x=N.expandShapeToKeepDim(y.shape,c);y.shape=x}return y}var ode={kernelName:xo,backendName:"wasm",setupFunc:sde,kernelFunc:ide},lde=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=R1(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},ude={kernelName:Bc,backendName:"wasm",kernelFunc:lde},pde=!0,cde=ln(Gi,pde),dde=Je(vo),hde=Je(wo),mde=Je(So),jF;function fde(e){jF=e.wasm.cwrap(Io,null,["number","number","number","number","number","number","number","number","number","number"])}function gde(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,[p,d,c,h]=r.shape,m=[p,l,u,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=Ps({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let b=f.id,y=t.makeOutput(m,"float32");if(v.sizeFromShape(r.shape)===0)return y;let x=t.dataIdMap.get(y.dataId).id;return jF(b,p,d,c,h,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),y}var bde={kernelName:Io,backendName:"wasm",setupFunc:fde,kernelFunc:gde},KF;function yde(e){KF=e.wasm.cwrap(Hu,null,["number","number","number","array","array","boolean"])}function xde(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=n.makeOutput(r.shape,"float32"),l=n.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=Ps({backend:n,inputs:{x:r},attrs:{dtype:"float32"}}),l=n.dataIdMap.get(u.dataId)),KF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(o.dataId).id,new 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Int32Array(r.shape).buffer);ZF(l,p,i.length,d,r.shape.length,u);let c=Wn({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),c}var Ede={kernelName:No,backendName:"wasm",kernelFunc:_de,setupFunc:Cde},JF;function Ade(e){JF=e.wasm.cwrap(op,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Fde(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),u=n.dataIdMap.get(r.dataId).id,p=n.dataIdMap.get(l.dataId).id,[d,c,h,m]=r.shape,[f,g]=N.getImageCenter(o,c,h),b=i===0,y=255,x=typeof i=="number"?[i,i,i,b?0:y]:[...i,y],w=new Uint8Array(new Int32Array(x).buffer);return JF(u,d,c,h,m,s,f,g,w,x.length,p),l}var $de={kernelName:op,backendName:"wasm",kernelFunc:Fde,setupFunc:Ade},Dde=Je(To),Rde=Je(Co),QF;function Mde(e){QF=e.wasm.cwrap(qu,null,["number","number","number","number","number","number","array","number","number"])}function Pde(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(v.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:p,strides:d,outputSize:c}=vf.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(d).buffer),g=t.dataIdMap.get(o.dataId).id;return QF(h,m,et[s.dtype],l,u,p,f,c,g),o}var Ode={kernelName:qu,backendName:"wasm",setupFunc:Mde,kernelFunc:Pde},e$;function Lde(e){e$=e.wasm.cwrap(Ku,null,["number","number","number","number","number","number","bool","number"])}function zde(e){let{inputs:t,backend:n,attrs:a}=e,{sortedSequence:r,values:s}=t,{side:i}=a;if(r.dtype!==s.dtype)throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. 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qde(e){let{backend:t,inputs:{x:n}}=e,a=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(r.dataId).id;return v.sizeFromShape(r.shape)===0||n$(a,s),r}var jde={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Hde,kernelFunc:qde},Kde=Je(Ao),Xde=Je(Eo),Yde=Je($o);function Zde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a,o=v.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g0?l+1:0;if(u<0)throw new Error(N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let p=r.shape.slice();p[0]=u;let d=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(s.dataId).id,h=n.dataIdMap.get(i.dataId).id,m=n.makeOutput(p,r.dtype),f=n.dataIdMap.get(m.dataId).id,g=n.makeOutput([4],"int32"),b=n.dataIdMap.get(g.dataId).id;KF(d,et[r.dtype],r.shape[0],c,h,f,b,t,0);let y=n.readSync(g.dataId),x;switch(y[0]){case 0:{x=N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{x=N.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:x=N.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(y[1],y[2]);break;case 3:x=N.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(y[1],y[2],y[3]);break;default:x=""}if(n.disposeData(g.dataId),x)throw n.disposeData(m.dataId),new Error(x);return m}function zde(e){return YF(e,!0)}var Wde={kernelName:Uc,backendName:"wasm",setupFunc:XF,kernelFunc:zde};function Bde(e){return YF(e,!1)}var Vde={kernelName:Gc,backendName:"wasm",setupFunc:XF,kernelFunc:Bde},ZF;function Ude(e){ZF=e.wasm.cwrap(ep,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Gde(e){let{backend:t,inputs:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=n,{outputShape:o}=a,l=t.makeOutput(o,i.dtype);if(v.sizeFromShape(o)===0)return 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Re(this.left,this.top)}get topRight(){return new Re(this.right,this.top)}get bottomLeft(){return new Re(this.left,this.bottom)}get bottomRight(){return new Re(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 an&&(o=-d+n+a,d=n),c>t&&(l=-c+t+r,c=t),u<1&&(l=2-u,u=1),p<1&&(l=2-p,p=1),{dy:i,edy:l,dx:s,edx:o,y:p,ey:c,x:u,ex:d,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 Jo=class extends ot{constructor(t,n,a,r,s=!1){super({left:t,top:n,right:a,bottom:r},s)}};var Br=class{constructor(t,n,a,r,s){this._imageDims=new wn(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 Br(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var vt=class extends Br{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 vt(a,r,s)}};function uk(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 pk(e){let t=e.map(o=>o.x),n=e.map(o=>o.y),a=t.reduce((o,l)=>lloo({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 u=0;ul[p]<=n)}return s}function Ja(e,t){return P(()=>{let[n,a,r]=t,s=xn([...e.shape.slice(0,3),1],n,"float32"),i=xn([...e.shape.slice(0,3),1],a,"float32"),o=xn([...e.shape.slice(0,3),1],r,"float32"),l=Je([s,i,o],3);return pe(e,l)})}function dk(e,t=!1){return P(()=>{let[n,a]=e.shape.slice(1);if(n===a)return e;let r=Math.abs(n-a),s=Math.round(r*(t?.5:1)),i=n>a?2:1,o=c=>{let h=e.shape.slice();return h[i]=c,xn(h,0,"float32")},l=o(s),u=r-l.shape[i],d=[t&&u?o(u):null,e,l].filter(c=>!!c).map(c=>ie(c,"float32"));return Je(d,i)})}function eme(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let a=Math.floor(Math.random()*(n+1)),r=t[n];t[n]=t[a],t[a]=r}return t}function Rd(e){return 1/(1+Math.exp(-e))}function tme(e){return Math.log(e/(1-e))}var Qo=class extends ot{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var nme=.5,ame=.43,rme=.45,sa=class{constructor(t,n,a=new Re(0,0)){let{width:r,height:s}=n;this._imgDims=new wn(r,s),this._shift=a,this._positions=t.map(i=>i.mul(new Re(r,s)).add(a))}get shift(){return new Re(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new Re(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new Re(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let s=t instanceof vt?t.box.floor():new ot(t);return this.shiftBy(s.x,s.y).align(null,n)}let{useDlibAlignment:a,minBoxPadding:r}={useDlibAlignment:!1,minBoxPadding:.2,...n};return a?this.alignDlib():this.alignMinBbox(r)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,a,r]=t,s=d=>r.sub(d).magnitude(),i=(s(n)+s(a))/2,o=Math.floor(i/rme),l=Zo(t),u=Math.floor(Math.max(0,l.x-nme*o)),p=Math.floor(Math.max(0,l.y-ame*o));return new Qo(u,p,Math.min(o,this.imageWidth+u),Math.min(o,this.imageHeight+p))}alignMinBbox(t){let n=pk(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var hk=class extends sa{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],Zo([t[3],t[4]])]}};var el=class extends sa{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(Zo)}};var Tp=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}${t?` (${Yo(this.distance)})`:""}`}};var Cp=class extends ot{constructor(n,a){super(n);this._label=a}static assertIsValidLabeledBox(n,a){if(ot.assertIsValidBox(n,a),!Za(n.label))throw new Error(`${a} - expected property label (${n.label}) to be a number`)}get label(){return this._label}};var xr=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 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 xr(t.label,n)}};var mk=class extends Cp{constructor(n,a,r,s){super(n,a);this._score=r,this._classScore=s}static assertIsValidPredictedBox(n,a){if(Cp.assertIsValidLabeledBox(n,a),!Np(n.score)||!Np(n.classScore))throw new Error(`${a} - expected properties score (${n.score}) and (${n.classScore}) to be a number between [0, 1]`)}get score(){return this._score}get classScore(){return this._classScore}};function vr(e){return e.detection instanceof vt}function tl(e,t){return{...e,...{detection:t}}}function fk(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser 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bk()?yk(fk()):Md()?yk(gk()):null}function ime(e){if(ln||xk(),!ln)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=ln.Canvas,Image:n=ln.Image}=e;ln.Canvas=t,ln.Image=n,ln.createCanvasElement=e.createCanvasElement||(()=>new t),ln.createImageElement=e.createImageElement||(()=>new n),ln.ImageData=e.ImageData||ln.ImageData,ln.Video=e.Video||ln.Video,ln.fetch=e.fetch||ln.fetch,ln.readFile=e.readFile||ln.readFile}var tt={getEnv:sme,setEnv:yk,initialize:xk,createBrowserEnv:fk,createFileSystem:rg,createNodejsEnv:gk,monkeyPatch:ime,isBrowser:bk,isNodejs:Md};xk();function nl(e){return!tt.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function Hn(e){let{Canvas:t,CanvasRenderingContext2D:n}=tt.getEnv();if(e instanceof n)return e;let a=nl(e);if(!(a instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let r=a.getContext("2d");if(!r)throw new Error("resolveContext2d - canvas 2d context is 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wr=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Wr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(wa(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof tt.getEnv().Canvas?a:Ld(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return yr(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),a=this.getInputHeight(t);return ok({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,P(()=>{let a=yr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof Te){let o=wa(i)?i:Qt(i);return o=dk(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Fa.resizeBilinear(o,[t,t],!1,!1)),o.as3D(t,t,3)}if(i instanceof tt.getEnv().Canvas)return Bo.fromPixels(Sk(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return Dt(a.map(s=>ie(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function wt(e){if(e instanceof wr)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");let n=r=>Array.isArray(e)?` at input index ${r}:`:"",a=t.map(nl);return a.forEach((r,s)=>{if(!ig(r)&&!Wr(r)&&!wa(r))throw typeof t[s]=="string"?new Error(`toNetInput -${n(s)} string passed, but could not resolve HTMLElement for element id ${t[s]}`):new Error(`toNetInput -${n(s)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(wa(r)){let i=r.shape[0];if(i!==1)throw new Error(`toNetInput -${n(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(a.map(r=>ig(r)&&wk(r))),new wr(a,Array.isArray(e))}async function 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Ck=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Gr=class{constructor(t){this.neutral=0;this.happy=0;this.sad=0;this.angry=0;this.fearful=0;this.disgusted=0;this.surprised=0;if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Ck.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return Ck.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var Vd=class extends Mp{constructor(t=new Rp){super("FaceExpressionNet",t)}forwardInput(t){return P(()=>Xa(this.runNet(t)))}async forward(t){return this.forwardInput(await wt(t))}async predictExpressions(t){let n=await wt(t),a=await this.forwardInput(n),r=await Promise.all(ct(a).map(async i=>{let o=i.dataSync();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Gr(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 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n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=yme(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),u=s("entry_flow/reduction_block_1"),p={conv_in:o,reduction_block_0:l,reduction_block_1:u},d={};yr(t,0,1).forEach(f=>{d[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let c=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:c,separable_conv:h};return An(e,n),{params:{entry_flow:p,middle_flow:d,exit_flow:m},paramMappings:n}}function f$(e,t,n){return X(Rt(e,t.filters,n,"same"),t.bias)}function Ak(e,t,n=!0){let a=n?Ke(e):e;return a=qn(a,t.separable_conv0,[1,1]),a=qn(Ke(a),t.separable_conv1,[1,1]),a=Mt(a,[3,3],[2,2],"same"),a=X(a,f$(e,t.expansion_conv,[2,2])),a}function xme(e,t){let n=qn(Ke(e),t.separable_conv0,[1,1]);return n=qn(Ke(n),t.separable_conv1,[1,1]),n=qn(Ke(n),t.separable_conv2,[1,1]),n=X(n,e),n}var bg=class extends 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Fme(e){let t=ct(De(e,[1,0])),n=[pe(t[2],t[0]),pe(t[3],t[1])],a=[X(t[0],he(n[0],2)),X(t[1],he(n[1],2))];return{sizes:n,centers:a}}function $me(e,t){let{sizes:n,centers:a}=Fme(e),r=ct(De(t,[1,0])),s=he(z(yn(he(r[2],5)),n[0]),2),i=X(z(he(r[0],10),n[0]),a[0]),o=he(z(yn(he(r[3],5)),n[1]),2),l=X(z(he(r[1],10),n[1]),a[1]);return De(Dt([pe(i,s),pe(l,o),X(i,s),X(l,o)]),[1,0])}function _$(e,t,n){return P(()=>{let a=e.shape[0],r=$me(W(Ln(n.extra_dim,[a,1,1]),[-1,4]),W(e,[-1,4]));r=W(r,[a,r.shape[0]/a,4]);let s=ma(Ve(t,[0,0,1],[-1,-1,-1])),i=Ve(s,[0,0,0],[-1,-1,1]);i=W(i,[a,i.shape[1]]);let o=ct(r),l=ct(i);return{boxes:o,scores:l}})}function ul(e,t){return P(()=>{let n=e.shape[0],a=W(sl(e,t.box_encoding_predictor),[n,-1,1,4]),r=W(sl(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function E$(e,t,n){return P(()=>{let 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l.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return N$(t)}extractParams(t){return S$(t)}};function A$(e){let t=new Ps;return t.extractWeights(e),t}function Dme(e){return A$(e)}var Rk=class extends Ps{};var F$=.4,$$=[new Re(.738768,.874946),new Re(2.42204,2.65704),new Re(4.30971,7.04493),new Re(10.246,4.59428),new Re(12.6868,11.8741)],D$=[new Re(1.603231,2.094468),new Re(6.041143,7.080126),new Re(2.882459,3.518061),new Re(4.266906,5.178857),new Re(9.041765,10.66308)],R$=[117.001,114.697,97.404],M$="tiny_yolov2_model",P$="tiny_yolov2_separable_conv_model";var Sg=e=>typeof e=="number";function Mk(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(!Sg(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=>Sg(t.x)&&Sg(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(Sg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Lp(e){return P(()=>{let t=z(e,ve(.10000000149011612));return X(Ke(pe(e,t)),t)})}function Hr(e,t){return P(()=>{let n=xa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Rt(n,t.conv.filters,[1,1],"valid"),n=pe(n,t.bn.sub),n=z(n,t.bn.truediv),n=X(n,t.conv.bias),Lp(n)})}function qr(e,t){return P(()=>{let n=xa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=As(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),Lp(n)})}function Rme(e,t){let n=Fp(e,t);function a(i,o){let l=je(e(i)),u=je(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=$p(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function O$(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=Fn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=Rme(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,b,y,x]=a,w=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),T=u(h,m,"conv2"),C=u(m,f,"conv3"),E=u(f,g,"conv4"),F=u(g,b,"conv5"),D=y?u(b,y,"conv6"):void 0,$=x?u(y,x,"conv7"):void 0,S=o(x||y||b,5*n,1,"conv8");p={conv0:w,conv1:I,conv2:T,conv3:C,conv4:E,conv5:F,conv6:D,conv7:$,conv8:S}}else{let[d,c,h,m,f,g,b,y,x]=a,w=l(d,c,"conv0"),I=l(c,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),E=l(f,g,"conv4"),F=l(g,b,"conv5"),D=l(b,y,"conv6"),$=l(y,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:w,conv1:I,conv2:T,conv3:C,conv4:E,conv5:F,conv6:D,conv7:$,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function Mme(e,t){let n=ia(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Dp(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function L$(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Mme(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 An(e,n),{params:i,paramMappings:n}}var er=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 Pk=class extends un{constructor(n){super("TinyYolov2");Mk(n),this._config=n}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(n,a){let r=Hr(n,a.conv0);return r=Mt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv1),r=Mt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv2),r=Mt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv3),r=Mt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv4),r=Mt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv5),r=Mt(r,[2,2],[1,1],"same"),r=Hr(r,a.conv6),r=Hr(r,a.conv7),sl(r,a.conv8,"valid",!1)}runMobilenet(n,a){let r=this.config.isFirstLayerConv2d?Lp(sl(n,a.conv0,"valid",!1)):qr(n,a.conv0);return r=Mt(r,[2,2],[2,2],"same"),r=qr(r,a.conv1),r=Mt(r,[2,2],[2,2],"same"),r=qr(r,a.conv2),r=Mt(r,[2,2],[2,2],"same"),r=qr(r,a.conv3),r=Mt(r,[2,2],[2,2],"same"),r=qr(r,a.conv4),r=Mt(r,[2,2],[2,2],"same"),r=qr(r,a.conv5),r=Mt(r,[2,2],[1,1],"same"),r=a.conv6?qr(r,a.conv6):r,r=a.conv7?qr(r,a.conv7):r,sl(r,a.conv8,"valid",!1)}forwardInput(n,a){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return P(()=>{let s=ie(n.toBatchTensor(a,!1),"float32");return s=this.config.meanRgb?Ja(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(n,a){return this.forwardInput(await wt(n),a)}async detect(n,a={}){let{inputSize:r,scoreThreshold:s}=new er(a),i=await wt(n),o=await this.forwardInput(i,r),l=P(()=>ct(o)[0].expandDims()),u={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(l,i.getReshapedInputDimensions(0),s);o.dispose(),l.dispose();let d=p.map(b=>b.box),c=p.map(b=>b.score),h=p.map(b=>b.classScore),m=p.map(b=>this.config.classes[b.label]);return ck(d.map(b=>b.rescale(r)),c,this.config.iouThreshold,!0).map(b=>new Br(c[b],h[b],m[b],d[b],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(n){return L$(n,this.config)}extractParams(n){let a=this.config.filterSizes||Pk.DEFAULT_FILTER_SIZES,r=a?a.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 O$(n,this.config,this.boxEncodingSize,a)}async extractBoxes(n,a,r){let{width:s,height:i}=a,o=Math.max(s,i),l=o/s,u=o/i,p=n.shape[1],d=this.config.anchors.length,[c,h,m]=P(()=>{let y=n.reshape([p,p,d,this.boxEncodingSize]),x=y.slice([0,0,0,0],[p,p,d,4]),w=y.slice([0,0,0,4],[p,p,d,1]),I=this.withClassScores?Xa(y.slice([0,0,0,5],[p,p,d,this.config.classes.length]),3):ve(0);return[x,w,I]}),f=[],g=await h.array(),b=await c.array();for(let y=0;yr){let T=(x+Rd(b[y][x][w][0]))/p*l,C=(y+Rd(b[y][x][w][1]))/p*u,E=Math.exp(b[y][x][w][2])*this.config.anchors[w].x/p*l,F=Math.exp(b[y][x][w][3])*this.config.anchors[w].y/p*u,D=T-E/2,$=C-F/2,S={row:y,col:x,anchor:w},{classScore:M,label:B}=this.withClassScores?await this.extractPredictedClass(m,S):{classScore:1,label:0};f.push({box:new Jo(D,$,D+E,$+F),score:I,classScore:I*M,label:B,...S})}}return c.dispose(),h.dispose(),m.dispose(),f}async extractPredictedClass(n,a){let{row:r,col:s,anchor:i}=a,o=await n.array();return Array(this.config.classes.length).fill(0).map((l,u)=>o[r][s][i][u]).map((l,u)=>({classScore:l,label:u})).reduce((l,u)=>l.classScore>u.classScore?l:u)}},pl=Pk;pl.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var cl=class extends pl{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:F$,classes:["face"],...t?{anchors:D$,meanRgb:R$}:{anchors:$$,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 vt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?P$:M$}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Pme(e,t=!0){let n=new cl(t);return n.extractWeights(e),n}var qd=class extends er{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Ia=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function dl(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>il(l)?r(l):l.detection),i=a||(t instanceof Te?await Ap(t,s):await Ep(t,s)),o=await n(i);return i.forEach(l=>l instanceof Te&&l.dispose()),o}async function zp(e,t,n,a,r){return dl([e],t,async s=>n(s[0]),a,r)}var z$=.4,W$=[new Re(1.603231,2.094468),new Re(6.041143,7.080126),new Re(2.882459,3.518061),new Re(4.266906,5.178857),new Re(9.041765,10.66308)],B$=[117.001,114.697,97.404];var hl=class extends pl{constructor(){let t={withSeparableConvs:!0,iouThreshold:z$,classes:["face"],anchors:W$,meanRgb:B$,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 vt(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 Ps,tinyFaceDetector:new hl,tinyYolov2:new cl,faceLandmark68Net:new ol,faceLandmark68TinyNet:new Gd,faceRecognitionNet:new ll,faceExpressionNet:new Vd,ageGenderNet:new Ud},V$=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),Ome=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),Lme=(e,t)=>nt.tinyYolov2.locateFaces(e,t),U$=e=>nt.faceLandmark68Net.detectLandmarks(e),zme=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),Wme=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),Bme=e=>nt.faceExpressionNet.predictExpressions(e),Vme=e=>nt.ageGenderNet.predictAgeAndGender(e),G$=e=>nt.ssdMobilenetv1.load(e),Ume=e=>nt.tinyFaceDetector.load(e),Gme=e=>nt.tinyYolov2.load(e),Hme=e=>nt.faceLandmark68Net.load(e),qme=e=>nt.faceLandmark68TinyNet.load(e),jme=e=>nt.faceRecognitionNet.load(e),Kme=e=>nt.faceExpressionNet.load(e),Xme=e=>nt.ageGenderNet.load(e),Yme=G$,Zme=V$,Jme=U$;var Ng=class extends Ia{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},ml=class extends Ng{async run(){let t=await this.parentTask,n=await dl(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return 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pn{constructor(n,a){super(n);this._faceFeatureExtractor=a}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return P(()=>{let r=n instanceof wr?this.faceFeatureExtractor.forwardInput(n):n;return Bd(r.as2D(r.shape[0],-1),a.fc)})}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:a,paramMappings:r}=this.extractClassifierParams(n);this._params=a,this._paramMappings=r}extractClassifierParams(n){return w$(n,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(n){let{featureExtractorMap:a,classifierMap:r}=mg(n);return this.faceFeatureExtractor.loadFromWeightMap(a),k$(r)}extractParams(n){let a=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),s=r*a+r,i=n.slice(0,n.length-s),o=n.slice(n.length-s);return this.faceFeatureExtractor.extractWeights(i),this.extractClassifierParams(o)}};var Ek=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Hr=class{constructor(t){this.neutral=0;this.happy=0;this.sad=0;this.angry=0;this.fearful=0;this.disgusted=0;this.surprised=0;if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Ek.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return Ek.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var Vd=class extends Mp{constructor(t=new Rp){super("FaceExpressionNet",t)}forwardInput(t){return P(()=>Xa(this.runNet(t)))}async forward(t){return this.forwardInput(await wt(t))}async predictExpressions(t){let n=await wt(t),a=await this.forwardInput(n),r=await Promise.all(ct(a).map(async i=>{let o=i.dataSync();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Hr(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Ak(e){return e.expressions instanceof Hr}function fg(e,t){return{...e,...{expressions:t}}}function $me(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof Hr?s:Ak(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(d=>d.probability>n),u=vr(s)?s.detection.box.bottomLeft:a||new Re(0,0);new Ur(l.map(d=>`${d.expression} (${Jo(d.probability)})`),u).draw(e)})}function ll(e){return vr(e)&&e.landmarks instanceof ia&&e.unshiftedLandmarks instanceof ia&&e.alignedRect instanceof vt}function Dme(e){let t=l=>l*180/Math.PI,n=(l,u)=>Math.sqrt((l._x-u._x)**2+(l._y-u._y)**2),a={roll:void 0,pitch:void 0,yaw:void 0},r=(l,u,p)=>{let d=Math.floor(l._x-u._x),c=Math.floor(u._x-p._x);return d-c},s=(l,u)=>{let p=Math.hypot(u._x-l._x,u._y-l._y),d=u._y-l._y,c=Math.asin(d/p),h=t(c),m=Math.floor(90-h),f=u._x-l._x<0?-1:1;return m*f},i=(l,u,p)=>{let d=n(l,p),c={_x:(l._x+p._x)/2,_y:(l._y+p._y)/2},h=n(u,c),m=Math.atan(h/d),f=Math.floor(t(m)),g=c._y-u._y<0?-1:1;return f*g};if(!e||!e._positions||e._positions.length!==68)return a;let o=e._positions;return a.roll=s(o[27],o[66]),a.pitch=i(o[14],o[30],o[2]),a.yaw=r(o[14],o[33],o[2]),a}function Pp(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new vt(e.detection.score,r.rescale(s.reverse()),s),o=Dme(t);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i,angle:o}}}var gg=class{constructor(t={}){let{drawLines:n=!0,drawPoints:a=!0,lineWidth:r,lineColor:s,pointSize:i,pointColor:o}=t;this.drawLines=n,this.drawPoints=a,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=s||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}},bg=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new gg(n)}draw(t){let n=Hn(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof nl&&(n.strokeStyle=i,n.lineWidth=s,Wr(n,this.faceLandmarks.getJawOutline()),Wr(n,this.faceLandmarks.getLeftEyeBrow()),Wr(n,this.faceLandmarks.getRightEyeBrow()),Wr(n,this.faceLandmarks.getNose()),Wr(n,this.faceLandmarks.getLeftEye(),!0),Wr(n,this.faceLandmarks.getRightEye(),!0),Wr(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let u=p=>{n.beginPath(),n.arc(p.x,p.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(u)}}};function Rme(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof ia?a:ll(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new bg(r).draw(e)})}var I$="1.7.11";function Ome(e,t){let n=Fp(e,t),a=$p(e,t);function r(i,o,l){let u=a(i,o,`${l}/separable_conv0`),p=a(o,o,`${l}/separable_conv1`),d=n(i,o,1,`${l}/expansion_conv`);return{separable_conv0:u,separable_conv1:p,expansion_conv:d}}function s(i,o){let l=a(i,i,`${o}/separable_conv0`),u=a(i,i,`${o}/separable_conv1`),p=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function S$(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=Fn(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=Ome(a,n),u=s(3,32,3,"entry_flow/conv_in"),p=o(32,64,"entry_flow/reduction_block_0"),d=o(64,128,"entry_flow/reduction_block_1"),c={conv_in:u,reduction_block_0:p,reduction_block_1:d},h={};yr(t,0,1).forEach(b=>{h[`main_block_${b}`]=l(128,`middle_flow/main_block_${b}`)});let m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:c,middle_flow:h,exit_flow:g}}}function Lme(e,t){let n=oa(e,t),a=dg(n),r=Dp(n);function s(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:u,expansion_conv:p}}function i(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function N$(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=Lme(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),u=s("entry_flow/reduction_block_1"),p={conv_in:o,reduction_block_0:l,reduction_block_1:u},d={};yr(t,0,1).forEach(f=>{d[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let c=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:c,separable_conv:h};return An(e,n),{params:{entry_flow:p,middle_flow:d,exit_flow:m},paramMappings:n}}function T$(e,t,n){return X(Rt(e,t.filters,n,"same"),t.bias)}function $k(e,t,n=!0){let a=n?Ke(e):e;return a=qn(a,t.separable_conv0,[1,1]),a=qn(Ke(a),t.separable_conv1,[1,1]),a=Mt(a,[3,3],[2,2],"same"),a=X(a,T$(e,t.expansion_conv,[2,2])),a}function zme(e,t){let n=qn(Ke(e),t.separable_conv0,[1,1]);return n=qn(Ke(n),t.separable_conv1,[1,1]),n=qn(Ke(n),t.separable_conv2,[1,1]),n=X(n,e),n}var yg=class extends pn{constructor(n){super("TinyXception");this._numMainBlocks=n}forwardInput(n){let{params:a}=this;if(!a)throw new Error("TinyXception - load model before inference");return P(()=>{let r=ie(n.toBatchTensor(112,!0),"float32"),i=Ja(r,[122.782,117.001,104.298]).div(255),o=Ke(T$(i,a.entry_flow.conv_in,[2,2]));return o=$k(o,a.entry_flow.reduction_block_0,!1),o=$k(o,a.entry_flow.reduction_block_1),yr(this._numMainBlocks,0,1).forEach(l=>{o=zme(o,a.middle_flow[`main_block_${l}`])}),o=$k(o,a.exit_flow.reduction_block),o=Ke(qn(o,a.exit_flow.separable_conv,[1,1])),o})}async forward(n){return this.forwardInput(await wt(n))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(n){return N$(n,this._numMainBlocks)}extractParams(n){return S$(n,this._numMainBlocks)}};function C$(e){let t=[],{extractWeights:n,getRemainingWeights:a}=Fn(e),r=pg(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: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function _$(e){let t=[],n=oa(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 An(e,t),{params:r,paramMappings:t}}var xg=(n=>(n.FEMALE="female",n.MALE="male",n))(xg||{});var Ud=class extends pn{constructor(n=new yg(2)){super("AgeGenderNet");this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return P(()=>{let r=n instanceof wr?this.faceFeatureExtractor.forwardInput(n):n,s=xa(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),i=Bd(s,a.fc.age).as1D(),o=Bd(s,a.fc.gender);return{age:i,gender:o}})}forwardInput(n){return P(()=>{let{age:a,gender:r}=this.runNet(n);return{age:a,gender:Xa(r)}})}async forward(n){return this.forwardInput(await wt(n))}async predictAgeAndGender(n){let a=await wt(n),r=await this.forwardInput(a),s=ct(r.age),i=ct(r.gender),o=s.map((u,p)=>({ageTensor:u,genderTensor:i[p]})),l=await Promise.all(o.map(async({ageTensor:u,genderTensor:p})=>{let d=u.dataSync()[0],c=p.dataSync()[0],h=c>.5,m=h?"male":"female",f=h?c:1-c;return u.dispose(),p.dispose(),{age:d,gender:m,genderProbability:f}}));return r.age.dispose(),r.gender.dispose(),a.isBatchInput?l:l[0]}getDefaultModelName(){return"age_gender_model"}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:a,paramMappings:r}=this.extractClassifierParams(n);this._params=a,this._paramMappings=r}extractClassifierParams(n){return C$(n)}extractParamsFromWeightMap(n){let{featureExtractorMap:a,classifierMap:r}=mg(n);return this.faceFeatureExtractor.loadFromWeightMap(a),_$(r)}extractParams(n){let r=n.slice(0,n.length-1539),s=n.slice(n.length-1539);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(s)}};var Op=class extends Mp{postProcess(t,n,a){let r=a.map(({width:i,height:o})=>{let l=n/Math.max(o,i);return{width:i*l,height:o*l}}),s=r.length;return P(()=>{let i=(d,c)=>Dt([xn([68],d,"float32"),xn([68],c,"float32")],1).as2D(1,136).as1D(),o=(d,c)=>{let{width:h,height:m}=r[d];return c(h,m)?Math.abs(h-m)/2:0},l=d=>o(d,(c,h)=>co(d,(c,h)=>hi(l(c),u(c))))).div(Dt(Array.from(Array(s),(d,c)=>i(r[c].width,r[c].height))))})}forwardInput(t){return P(()=>{let n=this.runNet(t);return this.postProcess(n,t.inputSize,t.inputDimensions.map(([a,r])=>({height:a,width:r})))})}async forward(t){return this.forwardInput(await wt(t))}async detectLandmarks(t){let n=await wt(t),a=P(()=>ct(this.forwardInput(n))),r=await Promise.all(a.map(async(s,i)=>{let o=Array.from(s.dataSync()),l=o.filter((p,d)=>ag(d)),u=o.filter((p,d)=>!ag(d));return new nl(Array(68).fill(0).map((p,d)=>new Re(l[d],u[d])),{height:n.getInputHeight(i),width:n.getInputWidth(i)})}));return a.forEach(s=>s.dispose()),n.isBatchInput?r:r[0]}getClassifierChannelsOut(){return 136}};var ul=class extends Op{constructor(t=new Rp){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function E$(e){let t=[],{extractDenseBlock3Params:n}=hg(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return An(e,t),{params:a,paramMappings:t}}function A$(e){let t=[],{extractWeights:n,getRemainingWeights:a}=Fn(e),{extractDenseBlock3Params:r}=cg(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var vg=class extends pn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return P(()=>{let a=ie(t.toBatchTensor(112,!0),"float32"),s=Ja(a,[122.782,117.001,104.298]).div(255),i=ug(s,n.dense0,!0);return i=ug(i,n.dense1),i=ug(i,n.dense2),i=xa(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await wt(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return E$(t)}extractParams(t){return A$(t)}};var Gd=class extends Op{constructor(t=new vg){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var Dk=class extends ul{};function F$(e,t){return X(z(e,t.weights),t.biases)}function Rk(e,t,n,a,r="same"){let{filters:s,bias:i}=t.conv,o=Rt(e,s,n,r);return o=X(o,i),o=F$(o,t.scale),a?Ke(o):o}function $$(e,t){return Rk(e,t,[1,1],!0)}function Mk(e,t){return Rk(e,t,[1,1],!1)}function wg(e,t){return Rk(e,t,[2,2],!0,"valid")}function Wme(e,t){function n(o,l,u){let p=e(o),d=p.length/(l*u*u);if(lk(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, numFilters: ${l}, filterSize: ${u}`);return P(()=>De(Ma(p,[l,d,u,u]),[2,3,1,0]))}function a(o,l,u,p){let d=n(o,l,u),c=je(e(l));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:d,bias:c}}function r(o,l){let u=je(e(o)),p=je(e(o));return t.push({paramPath:`${l}/weights`},{paramPath:`${l}/biases`}),{weights:u,biases:p}}function s(o,l,u,p){let d=a(o,l,u,`${p}/conv`),c=r(l,`${p}/scale`);return{conv:d,scale:c}}function i(o,l,u,p,d=!1){let c=s((d?.5:1)*o,l,u,`${p}/conv1`),h=s(o,l,u,`${p}/conv2`);return{conv1:c,conv2:h}}return{extractConvLayerParams:s,extractResidualLayerParams:i}}function D$(e){let{extractWeights:t,getRemainingWeights:n}=Fn(e),a=[],{extractConvLayerParams:r,extractResidualLayerParams:s}=Wme(t,a),i=r(4704,32,7,"conv32_down"),o=s(9216,32,3,"conv32_1"),l=s(9216,32,3,"conv32_2"),u=s(9216,32,3,"conv32_3"),p=s(36864,64,3,"conv64_down",!0),d=s(36864,64,3,"conv64_1"),c=s(36864,64,3,"conv64_2"),h=s(36864,64,3,"conv64_3"),m=s(147456,128,3,"conv128_down",!0),f=s(147456,128,3,"conv128_1"),g=s(147456,128,3,"conv128_2"),b=s(589824,256,3,"conv256_down",!0),y=s(589824,256,3,"conv256_1"),x=s(589824,256,3,"conv256_2"),w=s(589824,256,3,"conv256_down_out"),I=P(()=>De($a(t(256*128),[128,256]),[1,0]));if(a.push({paramPath:"fc"}),n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{params:{conv32_down:i,conv32_1:o,conv32_2:l,conv32_3:u,conv64_down:p,conv64_1:d,conv64_2:c,conv64_3:h,conv128_down:m,conv128_1:f,conv128_2:g,conv256_down:b,conv256_1:y,conv256_2:x,conv256_down_out:w,fc:I},paramMappings:a}}function Bme(e,t){let n=oa(e,t);function a(i){let o=n(`${i}/scale/weights`,1),l=n(`${i}/scale/biases`,1);return{weights:o,biases:l}}function r(i){let o=n(`${i}/conv/filters`,4),l=n(`${i}/conv/bias`,1),u=a(i);return{conv:{filters:o,bias:l},scale:u}}function s(i){return{conv1:r(`${i}/conv1`),conv2:r(`${i}/conv2`)}}return{extractConvLayerParams:r,extractResidualLayerParams:s}}function R$(e){let t=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=Bme(e,t),r=n("conv32_down"),s=a("conv32_1"),i=a("conv32_2"),o=a("conv32_3"),l=a("conv64_down"),u=a("conv64_1"),p=a("conv64_2"),d=a("conv64_3"),c=a("conv128_down"),h=a("conv128_1"),m=a("conv128_2"),f=a("conv256_down"),g=a("conv256_1"),b=a("conv256_2"),y=a("conv256_down_out"),{fc:x}=e;if(t.push({originalPath:"fc",paramPath:"fc"}),!ok(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);let w={conv32_down:r,conv32_1:s,conv32_2:i,conv32_3:o,conv64_down:l,conv64_1:u,conv64_2:p,conv64_3:d,conv128_down:c,conv128_1:h,conv128_2:m,conv256_down:f,conv256_1:g,conv256_2:b,conv256_down_out:y,fc:x};return An(e,t),{params:w,paramMappings:t}}function Qa(e,t){let n=$$(e,t.conv1);return n=Mk(n,t.conv2),n=X(n,e),n=Ke(n),n}function Hd(e,t){let n=wg(e,t.conv1);n=Mk(n,t.conv2);let a=xa(e,2,2,"valid"),r=Nt(a.shape),s=a.shape[3]!==n.shape[3];if(a.shape[1]!==n.shape[1]||a.shape[2]!==n.shape[2]){let o=[...n.shape];o[1]=1;let l=Nt(o);n=Qe([n,l],1);let u=[...n.shape];u[2]=1;let p=Nt(u);n=Qe([n,p],2)}return a=s?Qe([a,r],3):a,n=X(a,n),n=Ke(n),n}var pl=class extends pn{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceRecognitionNet - load model before inference");return P(()=>{let a=ie(t.toBatchTensor(150,!0),"float32"),s=Ja(a,[122.782,117.001,104.298]).div(255),i=wg(s,n.conv32_down);i=Mt(i,3,2,"valid"),i=Qa(i,n.conv32_1),i=Qa(i,n.conv32_2),i=Qa(i,n.conv32_3),i=Hd(i,n.conv64_down),i=Qa(i,n.conv64_1),i=Qa(i,n.conv64_2),i=Qa(i,n.conv64_3),i=Hd(i,n.conv128_down),i=Qa(i,n.conv128_1),i=Qa(i,n.conv128_2),i=Hd(i,n.conv256_down),i=Qa(i,n.conv256_1),i=Qa(i,n.conv256_2),i=Hd(i,n.conv256_down_out);let o=i.mean([1,2]);return $e(o,n.fc)})}async forward(t){return this.forwardInput(await wt(t))}async computeFaceDescriptor(t){var s;if((s=t==null?void 0:t.shape)!=null&&s.some(i=>i<=0))return new Float32Array(128);let n=await wt(t),a=P(()=>ct(this.forwardInput(n))),r=await Promise.all(a.map(i=>i.data()));return a.forEach(i=>i.dispose()),n.isBatchInput?r:r[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return R$(t)}extractParams(t){return D$(t)}};function Vme(e){let t=new pl;return t.extractWeights(e),t}function 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n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=tfe(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return An(e,n),{params:i,paramMappings:n}}var er=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 Lk=class extends pn{constructor(n){super("TinyYolov2");Ok(n),this._config=n}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(n,a){let r=qr(n,a.conv0);return r=Mt(r,[2,2],[2,2],"same"),r=qr(r,a.conv1),r=Mt(r,[2,2],[2,2],"same"),r=qr(r,a.conv2),r=Mt(r,[2,2],[2,2],"same"),r=qr(r,a.conv3),r=Mt(r,[2,2],[2,2],"same"),r=qr(r,a.conv4),r=Mt(r,[2,2],[2,2],"same"),r=qr(r,a.conv5),r=Mt(r,[2,2],[1,1],"same"),r=qr(r,a.conv6),r=qr(r,a.conv7),ol(r,a.conv8,"valid",!1)}runMobilenet(n,a){let r=this.config.isFirstLayerConv2d?Lp(ol(n,a.conv0,"valid",!1)):jr(n,a.conv0);return r=Mt(r,[2,2],[2,2],"same"),r=jr(r,a.conv1),r=Mt(r,[2,2],[2,2],"same"),r=jr(r,a.conv2),r=Mt(r,[2,2],[2,2],"same"),r=jr(r,a.conv3),r=Mt(r,[2,2],[2,2],"same"),r=jr(r,a.conv4),r=Mt(r,[2,2],[2,2],"same"),r=jr(r,a.conv5),r=Mt(r,[2,2],[1,1],"same"),r=a.conv6?jr(r,a.conv6):r,r=a.conv7?jr(r,a.conv7):r,ol(r,a.conv8,"valid",!1)}forwardInput(n,a){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return P(()=>{let s=ie(n.toBatchTensor(a,!1),"float32");return s=this.config.meanRgb?Ja(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(n,a){return this.forwardInput(await wt(n),a)}async detect(n,a={}){let{inputSize:r,scoreThreshold:s}=new er(a),i=await wt(n),o=await this.forwardInput(i,r),l=P(()=>ct(o)[0].expandDims()),u={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(l,i.getReshapedInputDimensions(0),s);o.dispose(),l.dispose();let d=p.map(b=>b.box),c=p.map(b=>b.score),h=p.map(b=>b.classScore),m=p.map(b=>this.config.classes[b.label]);return hk(d.map(b=>b.rescale(r)),c,this.config.iouThreshold,!0).map(b=>new Vr(c[b],h[b],m[b],d[b],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(n){return X$(n,this.config)}extractParams(n){let a=this.config.filterSizes||Lk.DEFAULT_FILTER_SIZES,r=a?a.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return K$(n,this.config,this.boxEncodingSize,a)}async extractBoxes(n,a,r){let{width:s,height:i}=a,o=Math.max(s,i),l=o/s,u=o/i,p=n.shape[1],d=this.config.anchors.length,[c,h,m]=P(()=>{let y=n.reshape([p,p,d,this.boxEncodingSize]),x=y.slice([0,0,0,0],[p,p,d,4]),w=y.slice([0,0,0,4],[p,p,d,1]),I=this.withClassScores?Xa(y.slice([0,0,0,5],[p,p,d,this.config.classes.length]),3):ve(0);return[x,w,I]}),f=[],g=await h.array(),b=await c.array();for(let y=0;yr){let T=(x+Rd(b[y][x][w][0]))/p*l,C=(y+Rd(b[y][x][w][1]))/p*u,E=Math.exp(b[y][x][w][2])*this.config.anchors[w].x/p*l,F=Math.exp(b[y][x][w][3])*this.config.anchors[w].y/p*u,D=T-E/2,$=C-F/2,S={row:y,col:x,anchor:w},{classScore:M,label:B}=this.withClassScores?await this.extractPredictedClass(m,S):{classScore:1,label:0};f.push({box:new el(D,$,D+E,$+F),score:I,classScore:I*M,label:B,...S})}}return c.dispose(),h.dispose(),m.dispose(),f}async extractPredictedClass(n,a){let{row:r,col:s,anchor:i}=a,o=await n.array();return Array(this.config.classes.length).fill(0).map((l,u)=>o[r][s][i][u]).map((l,u)=>({classScore:l,label:u})).reduce((l,u)=>l.classScore>u.classScore?l:u)}},dl=Lk;dl.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var hl=class extends dl{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:V$,classes:["face"],...t?{anchors:G$,meanRgb:H$}:{anchors:U$,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new vt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?j$:q$}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function nfe(e,t=!0){let n=new hl(t);return n.extractWeights(e),n}var qd=class extends er{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Sa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function ml(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>ll(l)?r(l):l.detection),i=a||(t instanceof Ce?await Ap(t,s):await Ep(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ce&&l.dispose()),o}async function zp(e,t,n,a,r){return ml([e],t,async s=>n(s[0]),a,r)}var Y$=.4,Z$=[new Re(1.603231,2.094468),new Re(6.041143,7.080126),new Re(2.882459,3.518061),new Re(4.266906,5.178857),new Re(9.041765,10.66308)],J$=[117.001,114.697,97.404];var fl=class extends dl{constructor(){let t={withSeparableConvs:!0,iouThreshold:Y$,classes:["face"],anchors:Z$,meanRgb:J$,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new vt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var nt={ssdMobilenetv1:new Os,tinyFaceDetector:new fl,tinyYolov2:new hl,faceLandmark68Net:new ul,faceLandmark68TinyNet:new Gd,faceRecognitionNet:new pl,faceExpressionNet:new Vd,ageGenderNet:new Ud},Q$=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),afe=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),rfe=(e,t)=>nt.tinyYolov2.locateFaces(e,t),eD=e=>nt.faceLandmark68Net.detectLandmarks(e),sfe=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),ife=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),ofe=e=>nt.faceExpressionNet.predictExpressions(e),lfe=e=>nt.ageGenderNet.predictAgeAndGender(e),tD=e=>nt.ssdMobilenetv1.load(e),ufe=e=>nt.tinyFaceDetector.load(e),pfe=e=>nt.tinyYolov2.load(e),cfe=e=>nt.faceLandmark68Net.load(e),dfe=e=>nt.faceLandmark68TinyNet.load(e),hfe=e=>nt.faceRecognitionNet.load(e),mfe=e=>nt.faceExpressionNet.load(e),ffe=e=>nt.ageGenderNet.load(e),gfe=tD,bfe=Q$,yfe=eD;var Tg=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},gl=class extends Tg{async run(){let t=await this.parentTask,n=await ml(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>fg(a,n[r]))}withAgeAndGender(){return new yl(this,this.input)}},bl=class extends Tg{async run(){let t=await this.parentTask;if(!t)return;let n=await zp(t,this.input,a=>nt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return fg(t,n)}withAgeAndGender(){return new xl(this,this.input)}},Ls=class extends gl{withAgeAndGender(){return new Ws(this,this.input)}withFaceDescriptors(){return new Kr(this,this.input)}},zs=class extends bl{withAgeAndGender(){return new Bs(this,this.input)}withFaceDescriptor(){return new Xr(this,this.input)}};var Cg=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},yl=class extends Cg{async run(){let t=await this.parentTask,n=await ml(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 Ig(Sg(a,i,o),s)})}withFaceExpressions(){return new gl(this,this.input)}},xl=class extends Cg{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await zp(t,this.input,s=>nt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Ig(Sg(t,a,r),n)}withFaceExpressions(){return new bl(this,this.input)}},Ws=class extends yl{withFaceExpressions(){return new Ls(this,this.input)}withFaceDescriptors(){return new Kr(this,this.input)}},Bs=class extends xl{withFaceExpressions(){return new zs(this,this.input)}withFaceDescriptor(){return new Xr(this,this.input)}};var jd=class extends Sa{constructor(n,a){super();this.parentTask=n;this.input=a}},Kr=class extends jd{async run(){let t=await this.parentTask;return(await ml(t,this.input,a=>Promise.all(a.map(r=>nt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>kg(t[r],a))}withFaceExpressions(){return new Ls(this,this.input)}withAgeAndGender(){return new Ws(this,this.input)}},Xr=class extends jd{async run(){let t=await this.parentTask;if(!t)return;let n=await zp(t,this.input,a=>nt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return kg(t,n)}withFaceExpressions(){return new zs(this,this.input)}withAgeAndGender(){return new Bs(this,this.input)}};var Kd=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},Xd=class extends Kd{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Ce?await Ap(this.input,n):await Ep(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Ce&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>Pp(i,r[o]))}withFaceExpressions(){return new Ls(this,this.input)}withAgeAndGender(){return new Ws(this,this.input)}withFaceDescriptors(){return new Kr(this,this.input)}},Yd=class extends Kd{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ce?await Ap(this.input,[n]):await Ep(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ce&&s.dispose()),Pp(t,r)}withFaceExpressions(){return new zs(this,this.input)}withAgeAndGender(){return new Bs(this,this.input)}withFaceDescriptor(){return new Xr(this,this.input)}};var Zd=class extends Sa{constructor(n,a=new Ia){super();this.input=n;this.options=a}},Wp=class extends Zd{async run(){let{input:t,options:n}=this,a;if(n instanceof qd)a=nt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Ia)a=nt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof er)a=nt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>al({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new Xd(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new gl(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new yl(this.runAndExtendWithFaceDetections(),this.input)}},Jd=class extends Zd{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?al({},n):void 0)})}withFaceLandmarks(t=!1){return new Yd(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new bl(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new xl(this.runAndExtendWithFaceDetection(),this.input)}};function xfe(e,t=new Ia){return new Jd(e,t)}function _g(e,t=new Ia){return new Wp(e,t)}async function nD(e,t){return _g(e,new Ia(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function vfe(e,t={}){return _g(e,new er(t)).withFaceLandmarks().withFaceDescriptors()}var wfe=nD;function zk(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*s,0))}var Qd=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 xr)return i;if(i instanceof Float32Array)return new xr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new xr(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>zk(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Tp(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>xr.fromJSON(a));return new Qd(n,t.distanceThreshold)}};function kfe(e){let t=new fl;return t.extractWeights(e),t}function aD(e,t){let{width:n,height:a}=new wn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>aD(r,{width:n,height:a}));if(ll(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Pp(al(e,r),s)}return vr(e)?al(e,e.detection.forSize(n,a)):e instanceof ia||e instanceof vt?e.forSize(n,a):e}var Ife=I$;return VD(Sfe);})(); diff --git a/dist/face-api.node-gpu.js b/dist/face-api.node-gpu.js index b6c2379..6bf8bbb 100644 --- a/dist/face-api.node-gpu.js +++ b/dist/face-api.node-gpu.js @@ -4,4 +4,4 @@ author: ' */ -"use strict";var dn=Object.create;var er=Object.defineProperty;var hn=Object.getOwnPropertyDescriptor;var bn=Object.getOwnPropertyNames;var gn=Object.getPrototypeOf,xn=Object.prototype.hasOwnProperty;var vn=(o,t)=>()=>(t||o((t={exports:{}}).exports,t),t.exports),Lr=(o,t)=>{for(var e in t)er(o,e,{get:t[e],enumerable:!0})},To=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of bn(t))!xn.call(o,n)&&n!==e&&er(o,n,{get:()=>t[n],enumerable:!(r=hn(t,n))||r.enumerable});return o};var v=(o,t,e)=>(e=o!=null?dn(gn(o)):{},To(t||!o||!o.__esModule?er(e,"default",{value:o,enumerable:!0}):e,o)),yn=o=>To(er({},"__esModule",{value:!0}),o);var x=vn((Va,Br)=>{"use strict";var Wr=Object.defineProperty,_n=Object.getOwnPropertyDescriptor,Tn=Object.getOwnPropertyNames,wn=Object.prototype.hasOwnProperty,Pn=(o,t)=>{for(var e in t)Wr(o,e,{get:t[e],enumerable:!0})},Ar=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of Tn(t))!wn.call(o,n)&&n!==e&&Wr(o,n,{get:()=>t[n],enumerable:!(r=_n(t,n))||r.enumerable});return o},Fn=(o,t,e)=>(Ar(o,t,"default"),e&&Ar(e,t,"default")),Dn=o=>Ar(Wr({},"__esModule",{value:!0}),o),kr={};Pn(kr,{version:()=>Nn});Br.exports=Dn(kr);Fn(kr,require("@tensorflow/tfjs-node-gpu"),Br.exports);var wo="4.3.0",En="4.3.0",Mn="4.3.0",Cn="4.3.0",In="4.3.0",Nn={tfjs:wo,"tfjs-core":wo,"tfjs-converter":En,"tfjs-backend-cpu":Mn,"tfjs-backend-webgl":Cn,"tfjs-backend-wasm":In}});var Ra={};Lr(Ra,{AgeGenderNet:()=>He,BoundingBox:()=>Vt,Box:()=>D,ComposableTask:()=>J,ComputeAllFaceDescriptorsTask:()=>Pt,ComputeFaceDescriptorsTaskBase:()=>Ue,ComputeSingleFaceDescriptorTask:()=>Ft,DetectAllFaceLandmarksTask:()=>qe,DetectAllFacesTask:()=>Ie,DetectFaceLandmarksTaskBase:()=>Je,DetectFacesTaskBase:()=>Ke,DetectSingleFaceLandmarksTask:()=>Ze,DetectSingleFaceTask:()=>Qe,Dimensions:()=>k,FACE_EXPRESSION_LABELS:()=>so,FaceDetection:()=>M,FaceDetectionNet:()=>lo,FaceExpressionNet:()=>Oe,FaceExpressions:()=>yt,FaceLandmark68Net:()=>Kt,FaceLandmark68TinyNet:()=>ze,FaceLandmarkNet:()=>po,FaceLandmarks:()=>z,FaceLandmarks5:()=>jr,FaceLandmarks68:()=>Gt,FaceMatch:()=>pe,FaceMatcher:()=>tr,FaceRecognitionNet:()=>Qt,Gender:()=>Tr,LabeledBox:()=>ue,LabeledFaceDescriptors:()=>mt,NetInput:()=>ut,NeuralNetwork:()=>A,ObjectDetection:()=>bt,Point:()=>g,PredictedBox:()=>Ur,Rect:()=>Yt,SsdMobilenetv1:()=>St,SsdMobilenetv1Options:()=>X,TinyFaceDetector:()=>ne,TinyFaceDetectorOptions:()=>je,TinyYolov2:()=>re,TinyYolov2Options:()=>st,allFaces:()=>Aa,allFacesSsdMobilenetv1:()=>fn,allFacesTinyYolov2:()=>La,awaitMediaLoaded:()=>to,bufferToImage:()=>eo,computeFaceDescriptor:()=>va,createCanvas:()=>Jt,createCanvasFromMedia:()=>We,createFaceDetectionNet:()=>fa,createFaceRecognitionNet:()=>ea,createSsdMobilenetv1:()=>qo,createTinyFaceDetector:()=>Wa,createTinyYolov2:()=>ha,detectAllFaces:()=>Sr,detectFaceLandmarks:()=>pn,detectFaceLandmarksTiny:()=>xa,detectLandmarks:()=>Na,detectSingleFace:()=>Sa,draw:()=>co,env:()=>w,euclideanDistance:()=>vo,extendWithAge:()=>Er,extendWithFaceDescriptor:()=>Dr,extendWithFaceDetection:()=>jt,extendWithFaceExpressions:()=>xr,extendWithFaceLandmarks:()=>Pe,extendWithGender:()=>Mr,extractFaceTensors:()=>de,extractFaces:()=>le,fetchImage:()=>Hn,fetchJson:()=>no,fetchNetWeights:()=>zn,fetchOrThrow:()=>xt,fetchVideo:()=>Vn,getContext2dOrThrow:()=>O,getMediaDimensions:()=>Xt,imageTensorToCanvas:()=>ro,imageToSquare:()=>oo,inverseSigmoid:()=>An,iou:()=>zr,isMediaElement:()=>ir,isMediaLoaded:()=>Ae,isWithAge:()=>ra,isWithFaceDetection:()=>pt,isWithFaceExpressions:()=>io,isWithFaceLandmarks:()=>Zt,isWithGender:()=>oa,loadAgeGenderModel:()=>Ma,loadFaceDetectionModel:()=>Ca,loadFaceExpressionModel:()=>Ea,loadFaceLandmarkModel:()=>Pa,loadFaceLandmarkTinyModel:()=>Fa,loadFaceRecognitionModel:()=>Da,loadSsdMobilenetv1Model:()=>un,loadTinyFaceDetectorModel:()=>Ta,loadTinyYolov2Model:()=>wa,loadWeightMap:()=>ao,locateFaces:()=>Ia,matchDimensions:()=>Yn,minBbox:()=>Vr,nets:()=>P,nonMaxSuppression:()=>Yr,normalize:()=>rt,padToSquare:()=>Gr,predictAgeAndGender:()=>_a,recognizeFaceExpressions:()=>ya,resizeResults:()=>ln,resolveInput:()=>Ut,shuffleArray:()=>Ln,sigmoid:()=>Ne,ssdMobilenetv1:()=>mn,tf:()=>ka,tinyFaceDetector:()=>ba,tinyYolov2:()=>ga,toNetInput:()=>C,utils:()=>Hr,validateConfig:()=>ho,version:()=>Ba});module.exports=yn(Ra);var 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X=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 St=class extends A{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return Lt.tidy(()=>{let r=Lt.cast(t.toBatchTensor(512,!1),"float32"),n=Lt.sub(Lt.div(r,127.5),1),a=jo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Jo(a.out,a.conv11,e.prediction_layer);return Xo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await C(t))}async 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st=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 go=class extends A{constructor(e){super("TinyYolov2");ho(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):wt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?wt(n,r.conv6):n,n=r.conv7?wt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return 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extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=go;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{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 ha(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends 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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 St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ba=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ga=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),xa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),va=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ya=o=>P.faceExpressionNet.predictExpressions(o),_a=o=>P.ageGenderNet.predictAgeAndGender(o),un=o=>P.ssdMobilenetv1.load(o),Ta=o=>P.tinyFaceDetector.load(o),wa=o=>P.tinyYolov2.load(o),Pa=o=>P.faceLandmark68Net.load(o),Fa=o=>P.faceLandmark68TinyNet.load(o),Da=o=>P.faceRecognitionNet.load(o),Ea=o=>P.faceExpressionNet.load(o),Ma=o=>P.ageGenderNet.load(o),Ca=un,Ia=mn,Na=pn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Ir{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>xr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return xr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Nr{async run(){let t=await this.parentTask,e=await oe(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 Er(Mr(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Er(Mr(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},Pt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Dr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Dr(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>Pe(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await de(this.input,[e]):await le(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),Pe(t,n)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=P.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=P.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=P.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>jt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ie(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(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 Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new se(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ce(this.runAndExtendWithFaceDetection(),this.input)}};function Sa(o,t=new X){return new Qe(o,t)}function Sr(o,t=new X){return new Ie(o,t)}async function fn(o,t){return Sr(o,new 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this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>vo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new pe(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>mt.fromJSON(r));return new tr(e,t.distanceThreshold)}};function Wa(o){let t=new ne;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new k(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(Zt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof z||o instanceof M?o.forSize(e,r):o}var 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X=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var St=class extends A{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return Lt.tidy(()=>{let r=Lt.cast(t.toBatchTensor(512,!1),"float32"),n=Lt.sub(Lt.div(r,127.5),1),a=jo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Jo(a.out,a.conv11,e.prediction_layer);return Xo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await C(t))}async 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st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 go=class extends A{constructor(e){super("TinyYolov2");ho(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):wt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?wt(n,r.conv6):n,n=r.conv7?wt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return a=this.config.meanRgb?rt(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,n):this.runTinyYolov2(a,n)})}async forward(e,r){return this.forwardInput(await C(e),r)}async detect(e,r={}){let{inputSize:n,scoreThreshold:a}=new st(r),s=await C(e),i=await this.forwardInput(s,n),c=N.tidy(()=>N.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),b=p.map(h=>this.config.classes[h.label]);return Yr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],b[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return nn(e,this.config)}extractParams(e){let r=this.config.filterSizes||go.DEFAULT_FILTER_SIZES,n=r?r.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return on(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,b]=N.tidy(()=>{let T=e.reshape([p,p,u,this.boxEncodingSize]),_=T.slice([0,0,0,0],[p,p,u,4]),E=T.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(T.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[_,E,W]}),y=[],F=await l.array(),h=await f.array();for(let T=0;Tn){let tt=(_+Ne(h[T][_][E][0]))/p*c,lt=(T+Ne(h[T][_][E][1]))/p*m,q=Math.exp(h[T][_][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[T][_][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:T,col:_,anchor:E},{classScore:yo,label:_o}=this.withClassScores?await this.extractPredictedClass(b,$t):{classScore:1,label:0};y.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*yo,label:_o,...$t})}}return f.dispose(),l.dispose(),b.dispose(),y}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=go;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{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 ha(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var xo=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof xo.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof xo.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],cn=[117.001,114.697,97.404];var ne=class extends ee{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 St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ba=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ga=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),xa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),va=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ya=o=>P.faceExpressionNet.predictExpressions(o),_a=o=>P.ageGenderNet.predictAgeAndGender(o),un=o=>P.ssdMobilenetv1.load(o),Ta=o=>P.tinyFaceDetector.load(o),wa=o=>P.tinyYolov2.load(o),Pa=o=>P.faceLandmark68Net.load(o),Fa=o=>P.faceLandmark68TinyNet.load(o),Da=o=>P.faceRecognitionNet.load(o),Ea=o=>P.faceExpressionNet.load(o),Ma=o=>P.ageGenderNet.load(o),Ca=un,Ia=mn,Na=pn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Ir{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>xr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return xr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Nr{async run(){let t=await this.parentTask,e=await oe(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 Er(Mr(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Er(Mr(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},Pt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Dr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Dr(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>Pe(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await de(this.input,[e]):await le(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),Pe(t,n)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=P.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=P.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=P.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>jt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ie(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(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 Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new se(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ce(this.runAndExtendWithFaceDetection(),this.input)}};function Sa(o,t=new X){return new Qe(o,t)}function Sr(o,t=new X){return new Ie(o,t)}async function fn(o,t){return Sr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Sr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Aa=fn;function vo(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*a,0))}var tr=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 mt)return s;if(s instanceof Float32Array)return new mt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new mt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>vo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new pe(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>mt.fromJSON(r));return new tr(e,t.distanceThreshold)}};function Wa(o){let t=new ne;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new k(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 Pe(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof z||o instanceof M?o.forSize(e,r):o}var Ba=So;0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,fetchVideo,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); diff --git a/dist/face-api.node-wasm.js b/dist/face-api.node-wasm.js index b141119..96df395 100644 --- a/dist/face-api.node-wasm.js +++ b/dist/face-api.node-wasm.js @@ -4,4 +4,4 @@ author: ' */ -"use strict";var hn=Object.create;var er=Object.defineProperty;var bn=Object.getOwnPropertyDescriptor;var gn=Object.getOwnPropertyNames;var xn=Object.getPrototypeOf,vn=Object.prototype.hasOwnProperty;var yn=(o,t)=>()=>(t||o((t={exports:{}}).exports,t),t.exports),Wr=(o,t)=>{for(var e in t)er(o,e,{get:t[e],enumerable:!0})},To=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of gn(t))!vn.call(o,n)&&n!==e&&er(o,n,{get:()=>t[n],enumerable:!(r=bn(t,n))||r.enumerable});return o};var v=(o,t,e)=>(e=o!=null?hn(xn(o)):{},To(t||!o||!o.__esModule?er(e,"default",{value:o,enumerable:!0}):e,o)),_n=o=>To(er({},"__esModule",{value:!0}),o);var x=yn((Va,or)=>{"use strict";var Br=Object.defineProperty,Tn=Object.getOwnPropertyDescriptor,wn=Object.getOwnPropertyNames,Pn=Object.prototype.hasOwnProperty,Fn=(o,t)=>{for(var e in t)Br(o,e,{get:t[e],enumerable:!0})},kr=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of wn(t))!Pn.call(o,n)&&n!==e&&Br(o,n,{get:()=>t[n],enumerable:!(r=Tn(t,n))||r.enumerable});return o},Po=(o,t,e)=>(kr(o,t,"default"),e&&kr(e,t,"default")),Dn=o=>kr(Br({},"__esModule",{value:!0}),o),rr={};Fn(rr,{version:()=>Nn});or.exports=Dn(rr);Po(rr,require("@tensorflow/tfjs"),or.exports);Po(rr,require("@tensorflow/tfjs-backend-wasm"),or.exports);var wo="4.3.0",En="4.3.0",Mn="4.3.0",Cn="4.3.0",In="4.3.0",Nn={tfjs:wo,"tfjs-core":wo,"tfjs-converter":En,"tfjs-backend-cpu":Mn,"tfjs-backend-webgl":Cn,"tfjs-backend-wasm":In}});var Ra={};Wr(Ra,{AgeGenderNet:()=>He,BoundingBox:()=>Vt,Box:()=>D,ComposableTask:()=>J,ComputeAllFaceDescriptorsTask:()=>Pt,ComputeFaceDescriptorsTaskBase:()=>Ue,ComputeSingleFaceDescriptorTask:()=>Ft,DetectAllFaceLandmarksTask:()=>qe,DetectAllFacesTask:()=>Ie,DetectFaceLandmarksTaskBase:()=>Je,DetectFacesTaskBase:()=>Ke,DetectSingleFaceLandmarksTask:()=>Ze,DetectSingleFaceTask:()=>Qe,Dimensions:()=>k,FACE_EXPRESSION_LABELS:()=>so,FaceDetection:()=>M,FaceDetectionNet:()=>lo,FaceExpressionNet:()=>Oe,FaceExpressions:()=>yt,FaceLandmark68Net:()=>Kt,FaceLandmark68TinyNet:()=>ze,FaceLandmarkNet:()=>po,FaceLandmarks:()=>z,FaceLandmarks5:()=>jr,FaceLandmarks68:()=>Gt,FaceMatch:()=>pe,FaceMatcher:()=>tr,FaceRecognitionNet:()=>Qt,Gender:()=>Pr,LabeledBox:()=>ue,LabeledFaceDescriptors:()=>mt,NetInput:()=>ut,NeuralNetwork:()=>A,ObjectDetection:()=>bt,Point:()=>g,PredictedBox:()=>Ur,Rect:()=>Yt,SsdMobilenetv1:()=>St,SsdMobilenetv1Options:()=>X,TinyFaceDetector:()=>ne,TinyFaceDetectorOptions:()=>je,TinyYolov2:()=>re,TinyYolov2Options:()=>st,allFaces:()=>Aa,allFacesSsdMobilenetv1:()=>ln,allFacesTinyYolov2:()=>La,awaitMediaLoaded:()=>to,bufferToImage:()=>eo,computeFaceDescriptor:()=>va,createCanvas:()=>Jt,createCanvasFromMedia:()=>We,createFaceDetectionNet:()=>fa,createFaceRecognitionNet:()=>ea,createSsdMobilenetv1:()=>Zo,createTinyFaceDetector:()=>Wa,createTinyYolov2:()=>ha,detectAllFaces:()=>Ar,detectFaceLandmarks:()=>un,detectFaceLandmarksTiny:()=>xa,detectLandmarks:()=>Na,detectSingleFace:()=>Sa,draw:()=>co,env:()=>w,euclideanDistance:()=>vo,extendWithAge:()=>Cr,extendWithFaceDescriptor:()=>Mr,extendWithFaceDetection:()=>jt,extendWithFaceExpressions:()=>yr,extendWithFaceLandmarks:()=>Pe,extendWithGender:()=>Ir,extractFaceTensors:()=>de,extractFaces:()=>le,fetchImage:()=>Hn,fetchJson:()=>no,fetchNetWeights:()=>zn,fetchOrThrow:()=>xt,fetchVideo:()=>Vn,getContext2dOrThrow:()=>O,getMediaDimensions:()=>Xt,imageTensorToCanvas:()=>ro,imageToSquare:()=>oo,inverseSigmoid:()=>An,iou:()=>zr,isMediaElement:()=>mr,isMediaLoaded:()=>Ae,isWithAge:()=>ra,isWithFaceDetection:()=>pt,isWithFaceExpressions:()=>io,isWithFaceLandmarks:()=>Zt,isWithGender:()=>oa,loadAgeGenderModel:()=>Ma,loadFaceDetectionModel:()=>Ca,loadFaceExpressionModel:()=>Ea,loadFaceLandmarkModel:()=>Pa,loadFaceLandmarkTinyModel:()=>Fa,loadFaceRecognitionModel:()=>Da,loadSsdMobilenetv1Model:()=>fn,loadTinyFaceDetectorModel:()=>Ta,loadTinyYolov2Model:()=>wa,loadWeightMap:()=>ao,locateFaces:()=>Ia,matchDimensions:()=>Yn,minBbox:()=>Vr,nets:()=>P,nonMaxSuppression:()=>Yr,normalize:()=>rt,padToSquare:()=>Gr,predictAgeAndGender:()=>_a,recognizeFaceExpressions:()=>ya,resizeResults:()=>dn,resolveInput:()=>Ut,shuffleArray:()=>Ln,sigmoid:()=>Ne,ssdMobilenetv1:()=>pn,tf:()=>ka,tinyFaceDetector:()=>ba,tinyYolov2:()=>ga,toNetInput:()=>C,utils:()=>Hr,validateConfig:()=>ho,version:()=>Ba});module.exports=_n(Ra);var 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st=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 go=class extends A{constructor(e){super("TinyYolov2");ho(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let n=Tt(e,r.conv0);return 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a=this.config.meanRgb?rt(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,n):this.runTinyYolov2(a,n)})}async forward(e,r){return this.forwardInput(await C(e),r)}async detect(e,r={}){let{inputSize:n,scoreThreshold:a}=new st(r),s=await C(e),i=await this.forwardInput(s,n),c=N.tidy(()=>N.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),b=p.map(h=>this.config.classes[h.label]);return Yr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],b[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return an(e,this.config)}extractParams(e){let r=this.config.filterSizes||go.DEFAULT_FILTER_SIZES,n=r?r.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return nn(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,b]=N.tidy(()=>{let T=e.reshape([p,p,u,this.boxEncodingSize]),_=T.slice([0,0,0,0],[p,p,u,4]),E=T.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(T.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[_,E,W]}),y=[],F=await l.array(),h=await f.array();for(let T=0;Tn){let tt=(_+Ne(h[T][_][E][0]))/p*c,lt=(T+Ne(h[T][_][E][1]))/p*m,q=Math.exp(h[T][_][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[T][_][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:T,col:_,anchor:E},{classScore:yo,label:_o}=this.withClassScores?await this.extractPredictedClass(b,$t):{classScore:1,label:0};y.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*yo,label:_o,...$t})}}return f.dispose(),l.dispose(),b.dispose(),y}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=go;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Ko,classes:["face"],...t?{anchors:tn,meanRgb:en}:{anchors:Qo,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?on:rn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ha(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var xo=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof xo.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof xo.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var sn=.4,cn=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],mn=[117.001,114.697,97.404];var ne=class extends ee{constructor(){let t={withSeparableConvs:!0,iouThreshold:sn,classes:["face"],anchors:cn,meanRgb:mn,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 St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},pn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ba=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ga=(o,t)=>P.tinyYolov2.locateFaces(o,t),un=o=>P.faceLandmark68Net.detectLandmarks(o),xa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),va=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ya=o=>P.faceExpressionNet.predictExpressions(o),_a=o=>P.ageGenderNet.predictAgeAndGender(o),fn=o=>P.ssdMobilenetv1.load(o),Ta=o=>P.tinyFaceDetector.load(o),wa=o=>P.tinyYolov2.load(o),Pa=o=>P.faceLandmark68Net.load(o),Fa=o=>P.faceLandmark68TinyNet.load(o),Da=o=>P.faceRecognitionNet.load(o),Ea=o=>P.faceExpressionNet.load(o),Ma=o=>P.ageGenderNet.load(o),Ca=fn,Ia=pn,Na=un;var Sr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Sr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>yr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Sr{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return yr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Lr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Lr{async run(){let t=await this.parentTask,e=await oe(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 Cr(Ir(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Lr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Cr(Ir(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},Pt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Mr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Mr(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>Pe(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await de(this.input,[e]):await le(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),Pe(t,n)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=P.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=P.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=P.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>jt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ie(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(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 Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new se(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ce(this.runAndExtendWithFaceDetection(),this.input)}};function Sa(o,t=new X){return new Qe(o,t)}function Ar(o,t=new X){return new Ie(o,t)}async function ln(o,t){return Ar(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Ar(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Aa=ln;function vo(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*a,0))}var tr=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 mt)return s;if(s instanceof Float32Array)return new mt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new mt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>vo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new pe(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>mt.fromJSON(r));return new tr(e,t.distanceThreshold)}};function Wa(o){let t=new ne;return t.extractWeights(o),t}function dn(o,t){let{width:e,height:r}=new k(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=>dn(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 Pe(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof z||o instanceof M?o.forSize(e,r):o}var Ba=Lo;0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,fetchVideo,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); 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st=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 go=class extends A{constructor(e){super("TinyYolov2");ho(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):wt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?wt(n,r.conv6):n,n=r.conv7?wt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return a=this.config.meanRgb?rt(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,n):this.runTinyYolov2(a,n)})}async forward(e,r){return this.forwardInput(await C(e),r)}async detect(e,r={}){let{inputSize:n,scoreThreshold:a}=new st(r),s=await C(e),i=await this.forwardInput(s,n),c=N.tidy(()=>N.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),b=p.map(h=>this.config.classes[h.label]);return Yr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],b[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return an(e,this.config)}extractParams(e){let r=this.config.filterSizes||go.DEFAULT_FILTER_SIZES,n=r?r.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return nn(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,b]=N.tidy(()=>{let T=e.reshape([p,p,u,this.boxEncodingSize]),_=T.slice([0,0,0,0],[p,p,u,4]),E=T.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(T.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[_,E,W]}),y=[],F=await l.array(),h=await f.array();for(let T=0;Tn){let tt=(_+Ne(h[T][_][E][0]))/p*c,lt=(T+Ne(h[T][_][E][1]))/p*m,q=Math.exp(h[T][_][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[T][_][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:T,col:_,anchor:E},{classScore:yo,label:_o}=this.withClassScores?await this.extractPredictedClass(b,$t):{classScore:1,label:0};y.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*yo,label:_o,...$t})}}return f.dispose(),l.dispose(),b.dispose(),y}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=go;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Ko,classes:["face"],...t?{anchors:tn,meanRgb:en}:{anchors:Qo,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?on:rn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ha(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var xo=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof xo.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof xo.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var sn=.4,cn=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],mn=[117.001,114.697,97.404];var ne=class extends ee{constructor(){let t={withSeparableConvs:!0,iouThreshold:sn,classes:["face"],anchors:cn,meanRgb:mn,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 St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},pn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ba=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ga=(o,t)=>P.tinyYolov2.locateFaces(o,t),un=o=>P.faceLandmark68Net.detectLandmarks(o),xa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),va=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ya=o=>P.faceExpressionNet.predictExpressions(o),_a=o=>P.ageGenderNet.predictAgeAndGender(o),fn=o=>P.ssdMobilenetv1.load(o),Ta=o=>P.tinyFaceDetector.load(o),wa=o=>P.tinyYolov2.load(o),Pa=o=>P.faceLandmark68Net.load(o),Fa=o=>P.faceLandmark68TinyNet.load(o),Da=o=>P.faceRecognitionNet.load(o),Ea=o=>P.faceExpressionNet.load(o),Ma=o=>P.ageGenderNet.load(o),Ca=fn,Ia=pn,Na=un;var Sr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Sr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>yr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Sr{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return yr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Lr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Lr{async run(){let t=await this.parentTask,e=await oe(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 Cr(Ir(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Lr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Cr(Ir(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},Pt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Mr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Mr(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>Pe(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await de(this.input,[e]):await le(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),Pe(t,n)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=P.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=P.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=P.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>jt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ie(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(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 Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new se(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ce(this.runAndExtendWithFaceDetection(),this.input)}};function Sa(o,t=new X){return new Qe(o,t)}function Ar(o,t=new X){return new Ie(o,t)}async function ln(o,t){return Ar(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Ar(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Aa=ln;function vo(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*a,0))}var tr=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 mt)return s;if(s instanceof Float32Array)return new mt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new mt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>vo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new pe(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>mt.fromJSON(r));return new tr(e,t.distanceThreshold)}};function Wa(o){let t=new ne;return t.extractWeights(o),t}function dn(o,t){let{width:e,height:r}=new k(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=>dn(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 Pe(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof z||o instanceof M?o.forSize(e,r):o}var Ba=Lo;0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,fetchVideo,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); diff --git a/dist/face-api.node.js b/dist/face-api.node.js index d3118b5..2f76741 100644 --- a/dist/face-api.node.js +++ b/dist/face-api.node.js @@ -4,4 +4,4 @@ author: ' */ -"use strict";var dn=Object.create;var er=Object.defineProperty;var hn=Object.getOwnPropertyDescriptor;var bn=Object.getOwnPropertyNames;var gn=Object.getPrototypeOf,xn=Object.prototype.hasOwnProperty;var vn=(o,t)=>()=>(t||o((t={exports:{}}).exports,t),t.exports),Lr=(o,t)=>{for(var e in t)er(o,e,{get:t[e],enumerable:!0})},To=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of bn(t))!xn.call(o,n)&&n!==e&&er(o,n,{get:()=>t[n],enumerable:!(r=hn(t,n))||r.enumerable});return o};var v=(o,t,e)=>(e=o!=null?dn(gn(o)):{},To(t||!o||!o.__esModule?er(e,"default",{value:o,enumerable:!0}):e,o)),yn=o=>To(er({},"__esModule",{value:!0}),o);var x=vn((Va,Br)=>{"use strict";var Wr=Object.defineProperty,_n=Object.getOwnPropertyDescriptor,Tn=Object.getOwnPropertyNames,wn=Object.prototype.hasOwnProperty,Pn=(o,t)=>{for(var e in t)Wr(o,e,{get:t[e],enumerable:!0})},Ar=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of Tn(t))!wn.call(o,n)&&n!==e&&Wr(o,n,{get:()=>t[n],enumerable:!(r=_n(t,n))||r.enumerable});return o},Fn=(o,t,e)=>(Ar(o,t,"default"),e&&Ar(e,t,"default")),Dn=o=>Ar(Wr({},"__esModule",{value:!0}),o),kr={};Pn(kr,{version:()=>Nn});Br.exports=Dn(kr);Fn(kr,require("@tensorflow/tfjs-node"),Br.exports);var wo="4.3.0",En="4.3.0",Mn="4.3.0",Cn="4.3.0",In="4.3.0",Nn={tfjs:wo,"tfjs-core":wo,"tfjs-converter":En,"tfjs-backend-cpu":Mn,"tfjs-backend-webgl":Cn,"tfjs-backend-wasm":In}});var Ra={};Lr(Ra,{AgeGenderNet:()=>He,BoundingBox:()=>Vt,Box:()=>D,ComposableTask:()=>J,ComputeAllFaceDescriptorsTask:()=>Pt,ComputeFaceDescriptorsTaskBase:()=>Ue,ComputeSingleFaceDescriptorTask:()=>Ft,DetectAllFaceLandmarksTask:()=>qe,DetectAllFacesTask:()=>Ie,DetectFaceLandmarksTaskBase:()=>Je,DetectFacesTaskBase:()=>Ke,DetectSingleFaceLandmarksTask:()=>Ze,DetectSingleFaceTask:()=>Qe,Dimensions:()=>k,FACE_EXPRESSION_LABELS:()=>so,FaceDetection:()=>M,FaceDetectionNet:()=>lo,FaceExpressionNet:()=>Oe,FaceExpressions:()=>yt,FaceLandmark68Net:()=>Kt,FaceLandmark68TinyNet:()=>ze,FaceLandmarkNet:()=>po,FaceLandmarks:()=>z,FaceLandmarks5:()=>jr,FaceLandmarks68:()=>Gt,FaceMatch:()=>pe,FaceMatcher:()=>tr,FaceRecognitionNet:()=>Qt,Gender:()=>Tr,LabeledBox:()=>ue,LabeledFaceDescriptors:()=>mt,NetInput:()=>ut,NeuralNetwork:()=>A,ObjectDetection:()=>bt,Point:()=>g,PredictedBox:()=>Ur,Rect:()=>Yt,SsdMobilenetv1:()=>St,SsdMobilenetv1Options:()=>X,TinyFaceDetector:()=>ne,TinyFaceDetectorOptions:()=>je,TinyYolov2:()=>re,TinyYolov2Options:()=>st,allFaces:()=>Aa,allFacesSsdMobilenetv1:()=>fn,allFacesTinyYolov2:()=>La,awaitMediaLoaded:()=>to,bufferToImage:()=>eo,computeFaceDescriptor:()=>va,createCanvas:()=>Jt,createCanvasFromMedia:()=>We,createFaceDetectionNet:()=>fa,createFaceRecognitionNet:()=>ea,createSsdMobilenetv1:()=>qo,createTinyFaceDetector:()=>Wa,createTinyYolov2:()=>ha,detectAllFaces:()=>Sr,detectFaceLandmarks:()=>pn,detectFaceLandmarksTiny:()=>xa,detectLandmarks:()=>Na,detectSingleFace:()=>Sa,draw:()=>co,env:()=>w,euclideanDistance:()=>vo,extendWithAge:()=>Er,extendWithFaceDescriptor:()=>Dr,extendWithFaceDetection:()=>jt,extendWithFaceExpressions:()=>xr,extendWithFaceLandmarks:()=>Pe,extendWithGender:()=>Mr,extractFaceTensors:()=>de,extractFaces:()=>le,fetchImage:()=>Hn,fetchJson:()=>no,fetchNetWeights:()=>zn,fetchOrThrow:()=>xt,fetchVideo:()=>Vn,getContext2dOrThrow:()=>O,getMediaDimensions:()=>Xt,imageTensorToCanvas:()=>ro,imageToSquare:()=>oo,inverseSigmoid:()=>An,iou:()=>zr,isMediaElement:()=>ir,isMediaLoaded:()=>Ae,isWithAge:()=>ra,isWithFaceDetection:()=>pt,isWithFaceExpressions:()=>io,isWithFaceLandmarks:()=>Zt,isWithGender:()=>oa,loadAgeGenderModel:()=>Ma,loadFaceDetectionModel:()=>Ca,loadFaceExpressionModel:()=>Ea,loadFaceLandmarkModel:()=>Pa,loadFaceLandmarkTinyModel:()=>Fa,loadFaceRecognitionModel:()=>Da,loadSsdMobilenetv1Model:()=>un,loadTinyFaceDetectorModel:()=>Ta,loadTinyYolov2Model:()=>wa,loadWeightMap:()=>ao,locateFaces:()=>Ia,matchDimensions:()=>Yn,minBbox:()=>Vr,nets:()=>P,nonMaxSuppression:()=>Yr,normalize:()=>rt,padToSquare:()=>Gr,predictAgeAndGender:()=>_a,recognizeFaceExpressions:()=>ya,resizeResults:()=>ln,resolveInput:()=>Ut,shuffleArray:()=>Ln,sigmoid:()=>Ne,ssdMobilenetv1:()=>mn,tf:()=>ka,tinyFaceDetector:()=>ba,tinyYolov2:()=>ga,toNetInput:()=>C,utils:()=>Hr,validateConfig:()=>ho,version:()=>Ba});module.exports=yn(Ra);var 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st=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 go=class extends A{constructor(e){super("TinyYolov2");ho(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):wt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?wt(n,r.conv6):n,n=r.conv7?wt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return a=this.config.meanRgb?rt(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,n):this.runTinyYolov2(a,n)})}async forward(e,r){return this.forwardInput(await C(e),r)}async detect(e,r={}){let{inputSize:n,scoreThreshold:a}=new st(r),s=await C(e),i=await this.forwardInput(s,n),c=N.tidy(()=>N.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),b=p.map(h=>this.config.classes[h.label]);return Yr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],b[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return nn(e,this.config)}extractParams(e){let r=this.config.filterSizes||go.DEFAULT_FILTER_SIZES,n=r?r.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return on(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,b]=N.tidy(()=>{let T=e.reshape([p,p,u,this.boxEncodingSize]),_=T.slice([0,0,0,0],[p,p,u,4]),E=T.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(T.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[_,E,W]}),y=[],F=await l.array(),h=await f.array();for(let T=0;Tn){let tt=(_+Ne(h[T][_][E][0]))/p*c,lt=(T+Ne(h[T][_][E][1]))/p*m,q=Math.exp(h[T][_][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[T][_][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:T,col:_,anchor:E},{classScore:yo,label:_o}=this.withClassScores?await this.extractPredictedClass(b,$t):{classScore:1,label:0};y.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*yo,label:_o,...$t})}}return f.dispose(),l.dispose(),b.dispose(),y}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=go;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{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 ha(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var xo=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof xo.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof xo.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],cn=[117.001,114.697,97.404];var ne=class extends ee{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 St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ba=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ga=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),xa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),va=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ya=o=>P.faceExpressionNet.predictExpressions(o),_a=o=>P.ageGenderNet.predictAgeAndGender(o),un=o=>P.ssdMobilenetv1.load(o),Ta=o=>P.tinyFaceDetector.load(o),wa=o=>P.tinyYolov2.load(o),Pa=o=>P.faceLandmark68Net.load(o),Fa=o=>P.faceLandmark68TinyNet.load(o),Da=o=>P.faceRecognitionNet.load(o),Ea=o=>P.faceExpressionNet.load(o),Ma=o=>P.ageGenderNet.load(o),Ca=un,Ia=mn,Na=pn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Ir{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>xr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return xr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Nr{async run(){let t=await this.parentTask,e=await oe(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 Er(Mr(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Er(Mr(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},Pt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Dr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Dr(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>Pe(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await de(this.input,[e]):await le(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),Pe(t,n)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=P.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=P.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=P.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>jt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ie(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(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 Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new se(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ce(this.runAndExtendWithFaceDetection(),this.input)}};function Sa(o,t=new X){return new Qe(o,t)}function Sr(o,t=new X){return new Ie(o,t)}async function fn(o,t){return Sr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Sr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Aa=fn;function vo(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*a,0))}var tr=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 mt)return s;if(s instanceof Float32Array)return new mt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new mt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>vo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new pe(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>mt.fromJSON(r));return new tr(e,t.distanceThreshold)}};function Wa(o){let t=new ne;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new k(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 Pe(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof z||o instanceof M?o.forSize(e,r):o}var Ba=So;0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,fetchVideo,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); +"use strict";var dn=Object.create;var er=Object.defineProperty;var hn=Object.getOwnPropertyDescriptor;var bn=Object.getOwnPropertyNames;var gn=Object.getPrototypeOf,xn=Object.prototype.hasOwnProperty;var vn=(o,t)=>()=>(t||o((t={exports:{}}).exports,t),t.exports),Lr=(o,t)=>{for(var e in t)er(o,e,{get:t[e],enumerable:!0})},To=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of bn(t))!xn.call(o,n)&&n!==e&&er(o,n,{get:()=>t[n],enumerable:!(r=hn(t,n))||r.enumerable});return o};var v=(o,t,e)=>(e=o!=null?dn(gn(o)):{},To(t||!o||!o.__esModule?er(e,"default",{value:o,enumerable:!0}):e,o)),yn=o=>To(er({},"__esModule",{value:!0}),o);var x=vn((Va,Br)=>{"use strict";var Wr=Object.defineProperty,_n=Object.getOwnPropertyDescriptor,Tn=Object.getOwnPropertyNames,wn=Object.prototype.hasOwnProperty,Pn=(o,t)=>{for(var e in t)Wr(o,e,{get:t[e],enumerable:!0})},Ar=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of 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st=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 go=class extends A{constructor(e){super("TinyYolov2");ho(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let n=Tt(e,r.conv0);return 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a=this.config.meanRgb?rt(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,n):this.runTinyYolov2(a,n)})}async forward(e,r){return this.forwardInput(await C(e),r)}async detect(e,r={}){let{inputSize:n,scoreThreshold:a}=new st(r),s=await C(e),i=await this.forwardInput(s,n),c=N.tidy(()=>N.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),b=p.map(h=>this.config.classes[h.label]);return Yr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],b[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return nn(e,this.config)}extractParams(e){let r=this.config.filterSizes||go.DEFAULT_FILTER_SIZES,n=r?r.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return on(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,b]=N.tidy(()=>{let T=e.reshape([p,p,u,this.boxEncodingSize]),_=T.slice([0,0,0,0],[p,p,u,4]),E=T.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(T.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[_,E,W]}),y=[],F=await l.array(),h=await f.array();for(let T=0;Tn){let tt=(_+Ne(h[T][_][E][0]))/p*c,lt=(T+Ne(h[T][_][E][1]))/p*m,q=Math.exp(h[T][_][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[T][_][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:T,col:_,anchor:E},{classScore:yo,label:_o}=this.withClassScores?await this.extractPredictedClass(b,$t):{classScore:1,label:0};y.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*yo,label:_o,...$t})}}return f.dispose(),l.dispose(),b.dispose(),y}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=go;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{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 ha(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var xo=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof xo.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof xo.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],cn=[117.001,114.697,97.404];var ne=class extends ee{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 St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ba=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ga=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),xa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),va=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ya=o=>P.faceExpressionNet.predictExpressions(o),_a=o=>P.ageGenderNet.predictAgeAndGender(o),un=o=>P.ssdMobilenetv1.load(o),Ta=o=>P.tinyFaceDetector.load(o),wa=o=>P.tinyYolov2.load(o),Pa=o=>P.faceLandmark68Net.load(o),Fa=o=>P.faceLandmark68TinyNet.load(o),Da=o=>P.faceRecognitionNet.load(o),Ea=o=>P.faceExpressionNet.load(o),Ma=o=>P.ageGenderNet.load(o),Ca=un,Ia=mn,Na=pn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Ir{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>xr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return xr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Nr{async run(){let t=await this.parentTask,e=await oe(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 Er(Mr(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Er(Mr(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},Pt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Dr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Dr(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>Pe(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await de(this.input,[e]):await le(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),Pe(t,n)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=P.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=P.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=P.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>jt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ie(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(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 Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new se(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ce(this.runAndExtendWithFaceDetection(),this.input)}};function Sa(o,t=new X){return new Qe(o,t)}function Sr(o,t=new X){return new Ie(o,t)}async function fn(o,t){return Sr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Sr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Aa=fn;function vo(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*a,0))}var tr=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 mt)return s;if(s instanceof Float32Array)return new mt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new mt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>vo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new pe(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>mt.fromJSON(r));return new tr(e,t.distanceThreshold)}};function Wa(o){let t=new ne;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new k(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 Pe(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof z||o instanceof M?o.forSize(e,r):o}var Ba=So;0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,fetchVideo,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); 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ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let e=0;e{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){Kg(t).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let n=e.factory();if(n&&!(n instanceof Go)&&typeof n.then=="function"){let o=++this.pendingBackendInitId,s=n.then(i=>o(othis.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;ethis.startScope(n),()=>this.endScope(o),()=>(o=e(),o instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),o))}scopedRun(t,e,n){t();try{let o=n();return e(),o}catch(o){throw e(),o}}nextTensorId(){return gu.nextTensorId++}nextVariableId(){return gu.nextVariableId++}clone(t){let e=T.runKernel(go,{x:t}),n={x:t},o=i=>({x:()=>{let a="float32",u={x:i},l={dtype:a};return T.runKernel(fo,u,l)}}),s=[];return this.addTapeNode(this.state.activeScope.name,n,[e],o,s,{}),e}runKernel(t,e,n){if(this.backendName==null&&this.backend,!(oh(t,this.backendName)!=null))throw new Error(`Kernel '${t}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:t,inputs:e,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(t,e,n){let o=this.backend.numDataIds(),s=0;n.forEach(u=>{s+=u.dtype==="complex64"?3:1});let i=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=o-e-s-i;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${t}'`)}runKernelFunc(t){let e,n=[],o=this.isTapeOn(),s=this.state.numBytes,i=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let a;this.backendName==null&&this.backend;let u,l=b0(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(b0(t)){let{kernelName:d,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=oh(d,this.backendName);_(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),a=()=>{let b=this.backend.numDataIds();u=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let I=w.map(N=>N.rank!=null?N:this.makeTensorFromTensorInfo(N));if(o){let N=this.getTensorsForGradient(d,h,I);n=this.saveTensorsForBackwardMode(N)}return I}}else{let{forwardFunc:d}=t,h=g=>{o&&(n=g.map(x=>this.keep(this.clone(x))))};a=()=>{let g=this.backend.numDataIds();u=this.tidy(()=>d(this.backend,h));let x=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,g,x),x}}let{inputs:c,attrs:p}=t,m=b0(t)?null:t.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?e=a():(f=this.profiler.profileKernel(l,c,()=>a()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),e=f.outputs)}),o&&this.addTapeNode(l,c,e,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:e.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(t,e,n){let o=s0(t);if(o!=null){let s=o.inputsToSave||[],i=o.outputsToSave||[],a;o.saveAllInputs?(_(Array.isArray(e),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(e).map(l=>e[l])):a=s.map(l=>e[l]);let u=n.filter((l,c)=>i[c]);return a.concat(u)}return[]}makeTensor(t,e,n,o){if(t==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",o=o||this.backend;let s=t;n==="string"&&Wo(t[0])&&(s=t.map(u=>hu(u)));let i=o.write(s,e,n),a=new Pt(e,n,i,this.nextTensorId());if(this.trackTensor(a,o),n==="string"){let u=this.state.tensorInfo.get(i),l=e0(s);this.state.numBytes+=l-u.bytes,u.bytes=l}return a}makeTensorFromDataId(t,e,n,o){n=n||"float32";let s={dataId:t,shape:e,dtype:n};return this.makeTensorFromTensorInfo(s,o)}makeTensorFromTensorInfo(t,e){let{dataId:n,shape:o,dtype:s}=t,i=new Pt(o,s,n,this.nextTensorId());return this.trackTensor(i,e),i}makeVariable(t,e=!0,n,o){n=n||this.nextVariableId().toString(),o!=null&&o!==t.dtype&&(t=t.cast(o));let s=new dl(t,e,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(t,e){this.state.numTensors++,t.dtype==="string"&&this.state.numStringTensors++;let n=0;t.dtype!=="complex64"&&t.dtype!=="string"&&(n=t.size*Dp(t.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:n})),t instanceof dl||this.track(t)}incRef(t,e){this.trackTensor(t,e),this.backend.incRef(t.dataId)}removeDataId(t,e){this.state.tensorInfo.has(t)&&this.state.tensorInfo.get(t).backend===e&&(this.state.tensorInfo.delete(t),this.state.numDataBuffers--)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;let e=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,t.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=e.bytes),t.dtype!=="complex64"&&t.dtype!=="string"){let 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p=Q(p,a.dtype),u?R(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Cx=k({avgPool3d_:HK});function qK(r,t=0){_(r.length>=1,()=>"Pass at least one tensor to concat");let e=hl(r,"tensors","concat","string_or_numeric");if(e[0].dtype==="complex64"&&e.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor + with dtype ${s.dtype}. `)}),e.length===1)return un(e[0]);let n=e,o={axis:t};return T.runKernel(Ri,n,o)}var se=k({concat_:qK});function KK(r,t,e=!1,n=!1){let o=v(r,"a","matMul"),s=v(t,"b","matMul");[o,s]=jt(o,s);let i={a:o,b:s},a={transposeA:e,transposeB:n};return T.runKernel(ts,i,a)}var Bt=k({matMul_:KK});function jK(r){let e={x:v(r,"x","sigmoid","float32")};return T.runKernel(Xs,e)}var tn=k({sigmoid_:jK});function XK(r,t,e){let n=v(r,"x","slice","string_or_numeric");if(n.rank===0)throw new Error("Slicing scalar is not possible");let o={x:n},s={begin:t,size:e};return T.runKernel(Gi,o,s)}var Ot=k({slice_:XK});function YK(r){let e={x:v(r,"x","tanh","float32")};return T.runKernel(ni,e)}var ra=k({tanh_:YK});function ZK(r,t,e,n,o,s){let i=v(r,"forgetBias","basicLSTMCell"),a=v(t,"lstmKernel","basicLSTMCell"),u=v(e,"lstmBias","basicLSTMCell"),l=v(n,"data","basicLSTMCell"),c=v(o,"c","basicLSTMCell"),p=v(s,"h","basicLSTMCell"),m=se([l,p],1),f=Bt(m,a),d=Y(f,u),h=d.shape[0],g=d.shape[1]/4,x=[h,g],b=Ot(d,[0,0],x),w=Ot(d,[0,g],x),I=Ot(d,[0,g*2],x),N=Ot(d,[0,g*3],x),E=Y($(tn(b),ra(w)),$(c,tn(Y(i,I)))),A=$(ra(E),tn(N));return[E,A]}var gE=k({basicLSTMCell_:ZK});function JK(r,t,e){let n=v(r,"x","batchToSpaceND"),o=t.reduce((a,u)=>a*u);_(n.rank>=1+t.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${t.length}`),_(e.length===t.length,()=>`crops.length is ${e.length} but should be equal to blockShape.length ${t.length}`),_(n.shape[0]%o===0,()=>`input tensor batch is ${n.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${o}`);let s={x:n},i={blockShape:t,crops:e};return T.runKernel($i,s,i)}var wu=k({batchToSpaceND_:JK});function xE(r){let t;return r.rank===0||r.rank===1?t=R(r,[1,1,1,r.size]):r.rank===2?t=R(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?t=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]):t=r,t}function QK(r,t,e,n,o,s){s==null&&(s=.001);let i=v(r,"x","batchNorm"),a=v(t,"mean","batchNorm"),u=v(e,"variance","batchNorm"),l;o!=null&&(l=v(o,"scale","batchNorm"));let c;n!=null&&(c=v(n,"offset","batchNorm")),_(a.rank===u.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_(c==null||a.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_(l==null||a.rank===l.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let m={x:xE(i),scale:l,offset:c,mean:a,variance:u},f={varianceEpsilon:s},d=T.runKernel(gs,m,f);return R(d,i.shape)}var na=k({batchNorm_:QK});function tj(r,t,e,n,o,s){let i=v(r,"x","batchNorm"),a=v(t,"mean","batchNorm"),u=v(e,"variance","batchNorm"),l;o!=null&&(l=v(o,"scale","batchNorm"));let c;return n!=null&&(c=v(n,"offset","batchNorm")),_(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),_(a.rank===2||a.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${a.rank}.`),_(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`),l!=null&&_(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&_(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),na(i,a,u,c,l,s)}var vx=k({batchNorm2d_:tj});function ej(r,t,e,n,o,s){let i=v(r,"x","batchNorm"),a=v(t,"mean","batchNorm"),u=v(e,"variance","batchNorm"),l;o!=null&&(l=v(o,"scale","batchNorm"));let c;return n!=null&&(c=v(n,"offset","batchNorm")),_(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),_(a.rank===3||a.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${a.rank}.`),_(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),l!=null&&_(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&_(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),na(i,a,u,c,l,s)}var Sx=k({batchNorm3d_:ej});function rj(r,t,e,n,o,s){let i=v(r,"x","batchNorm"),a=v(t,"mean","batchNorm"),u=v(e,"variance","batchNorm"),l;o!=null&&(l=v(o,"scale","batchNorm"));let c;return n!=null&&(c=v(n,"offset","batchNorm")),_(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),_(a.rank===4||a.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${a.rank}.`),_(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&_(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&_(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),na(i,a,u,c,l,s)}var Nx=k({batchNorm4d_:rj});function nj(r,t,e){let n=v(r,"x","bincount"),o=v(t,"weights","bincount");_(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),_(e>=0,()=>`size must be non-negative, but got ${e}.`),_(o.size===n.size||o.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${n.shape}, weights shape: ${o.shape}.`);let s={x:n,weights:o},i={size:e};return T.runKernel(Da,s,i)}var kx=k({bincount_:nj});function oj(r,t){let e=v(r,"x","bitwiseAnd"),n=v(t,"y","bitwiseAnd");if(!sn(e.shape,n.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${e.shape}, y: 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S5(r,t,e,n,o,s="NHWC",i){let a=r;r.rank===3&&(a=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let u=t;u.rank===3&&(u=R(t,[1,t.shape[0],t.shape[1],t.shape[2]])),_(a.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${a.shape}.`),_(u.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${u.shape}.`),_(e.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${e}.`);let l=s==="NHWC"?a.shape[3]:a.shape[1],c=s==="NHWC"?u.shape[3]:u.shape[1];_(l===e[2],()=>`Error in conv2dDerFilter: depth of input ${l}) must match input depth in filter (${e[2]}.`),_(c===e[3],()=>`Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${e[3]}).`),Se("conv2dDerFilter",o,i);let p={x:a,dy:u},m={strides:n,pad:o,dataFormat:s,dimRoundingMode:i,filterShape:e};return T.runKernel(Pp,p,m)}var Dm=k({conv2DBackpropFilter_:S5});function vc(r,t,e){if(e==null||e==="linear")return r;if(e==="relu")return $(r,No(t));throw new 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p=v(r,"x","conv2d","float32"),m=v(t,"filter","conv2d","float32"),f=p,d=!1;p.rank===3&&(d=!0,f=R(p,[1,p.shape[0],p.shape[1],p.shape[2]])),_(f.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`),_(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),Se("fused conv2d",n,i);let h=o==="NHWC"?f.shape[3]:f.shape[1];_(m.shape[2]===h,()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`),_($r(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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setWeights(t){t=await this.extractIterations(t);let e=t.length/2,n=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var Tc=class extends qr{static get className(){return"Adagrad"}constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n];this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:B(()=>Co(s.shape,this.initialAccumulatorValue).variable(!1))});let i=Array.isArray(t)?t[o].tensor:t[n];if(i==null)return;let a=this.accumulatedGrads[o].variable;B(()=>{let 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e=!1;this.accumulations=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var Dc=class extends qr{static get className(){return"RMSProp"}constructor(t,e=.9,n=0,o=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=T.backend.epsilon()),t==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let 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nn{apply(t){return O$(t)}};fb.className="hardSigmoid";Q.registerClass(fb);var db=class extends nn{apply(t){return ii(t)}};db.className="softplus";Q.registerClass(db);var hb=class extends nn{apply(t){return F$(t)}};hb.className="softsign";Q.registerClass(hb);var gb=class extends nn{apply(t){return ea(t)}};gb.className="tanh";Q.registerClass(gb);var rf=class extends nn{apply(t,e=-1){return Eu(t,e)}};rf.className="softmax";Q.registerClass(rf);var xb=class extends nn{apply(t,e=-1){return dm(t,e)}};xb.className="logSoftmax";Q.registerClass(xb);var yb=class extends nn{apply(t,e=1){return B(()=>$(tn($(t,e)),t))}};yb.className="swish";Q.registerClass(yb);var bb=class extends nn{apply(t){return B(()=>$(t,ea(ii(t))))}};bb.className="mish";Q.registerClass(bb);function fi(r){return r.getClassName()}function FN(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,"activation")}function di(r){if(r==null){let t={};return t.className="linear",t.config={},FN(t)}if(typeof r=="string"){let t={};return t.className=r,t.config={},FN(t)}else return r instanceof nn?r:FN(r)}function ON(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var wb=class extends Q.Serializable{},Lu=class extends wb{constructor(t){super(),ON(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=Te([1]);return this.hasL1&&(e=Y(e,pt($(this.l1,Ee(t))))),this.hasL2&&(e=Y(e,pt($(this.l2,Oc(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Lu.className="L1L2";Q.registerClass(Lu);function gR(r){return ON(r),new Lu({l1:r!=null?r.l1:null,l2:0})}function xR(r){return ON(r),new Lu({l2:r!=null?r.l2:null,l1:0})}var dR={l1l2:"L1L2"};function me(r){return Rm(r)}function hR(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ce(r){if(r==null)return null;if(typeof r=="string"){let e={className:r in dR?dR[r]:r,config:{}};return hR(e)}else return r instanceof wb?r:hR(r)}var nf=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=Nt(t);let n=Pr(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};nf.className="ReLU";Q.registerClass(nf);var of=class extends Et{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=Nt(t);return vu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};of.className="LeakyReLU";Q.registerClass(of);var sf=class extends Et{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER="zeros",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=he(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ce(t.alphaRegularizer),this.alphaConstraint=Ve(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes=="number")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Wt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight("alpha",e,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o(Oe(t),t==="channelsFirst"?Vt(r,[0,2,3,1]):r))}function PN(r,t){return B(()=>(Oe(t),t==="channelsFirst"?Vt(r,[0,2,3,4,1]):r))}function d7(r,t,e,n=1,o="valid",s,i=1){return B(()=>{if(s==null&&(s=hn()),Oe(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.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(e!=null&&e.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(r=Vt(r,[0,2,1])),o==="causal")throw new St("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let a=um(r,t,n,o==="same"?"same":"valid","NWC",i);return e!=null&&(a=gn(a,e)),a})}function yR(r,t,e,n=[1,1],o="valid",s,i,a=null){return B(()=>{if(s==null&&(s=hn()),Oe(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(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Lh(r,s);if(o==="causal")throw new St("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Ru.conv2d({x:u,filter:t,strides:n,pad:o==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:e,activation:a}),s==="channelsFirst"&&(u=Vt(u,[0,3,1,2])),u})}function h7(r,t,e,n=[1,1,1],o="valid",s,i){return B(()=>{if(s==null&&(s=hn()),Oe(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(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=PN(r,s);if(o==="causal")throw new St("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=Tx(a,t,n,o==="same"?"same":"valid","NDHWC",i),e!=null&&(a=gn(a,e)),s==="channelsFirst"&&(a=Vt(a,[0,4,1,2,3])),a})}var qc=class extends Et{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",qc.verifyArgs(e),this.rank=t,Qe(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new St(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zu(e.kernelSize,t,"kernelSize"),this.strides=zu(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,fn(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Oe(this.dataFormat),this.activation=di(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=he(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ve(e.biasConstraint),this.biasRegularizer=Ce(e.biasRegularizer),this.activityRegularizer=Ce(e.activityRegularizer),this.dilationRate=zu(e.dilationRate==null?1:e.dilationRate,t,"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(t){if(uo("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!Ey(t.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(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:fi(this.activation),useBias:this.useBias,biasInitializer:_e(this.biasInitializer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},Bu=class extends qc{constructor(t,e){super(t,e),this.kernel=null,Bu.verifyArgs(e),this.filters=e.filters,Qe(this.filters,"filters"),this.kernelInitializer=he(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ve(e.kernelConstraint),this.kernelRegularizer=Ce(e.kernelRegularizer)}build(t){t=Wt(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,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:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=Nt(t);let n,o=this.bias==null?null:this.bias.read(),s=Ay(this.activation.getClassName());if(s!=null&&this.rank===2)n=yR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=d7(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=yR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=h7(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new St("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Wt(t);let e=[],n=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},El=class extends Bu{constructor(t){super(2,t),El.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Ey(t.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(t.kernelSize)}.`)}};El.className="Conv2D";Q.registerClass(El);var Al=class extends Bu{constructor(t){super(3,t),Al.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Al.className="Conv3D";Q.registerClass(Al);var cf=class extends El{constructor(t){if(super(t),this.inputSpec=[new Ie({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Wt(t),t.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=Nt(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat==="channelsFirst"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=hi(u,m,c,this.padding),h=hi(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,1]));let x=pm(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Vt(x,[0,3,1,2])),this.bias!=null&&(x=gn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=Wt(t);let e=t.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=hi(e[o],u,i,this.padding),e[s]=hi(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};cf.className="Conv2DTranspose";Q.registerClass(cf);var pf=class extends Al{constructor(t){if(super(t),this.inputSpec=[new Ie({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Wt(t),t.length!==5)throw new z("Input should have rank 5; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=Nt(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat==="channelsFirst"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=hi(l,h,m,this.padding),w=hi(c,g,f,this.padding),I=hi(p,x,d,this.padding),N=[s,b,w,I,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,4,1]));let E=Ex(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(E=Vt(E,[0,4,1,2,3])),this.bias!==null&&(E=gn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Wt(t);let e=t.slice(),n,o,s,i;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=hi(e[o],c,a,this.padding),e[s]=hi(e[s],p,u,this.padding),e[i]=hi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};pf.className="Conv3DTranspose";Q.registerClass(pf);var Ib=class extends Bu{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=he(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ce(e.depthwiseRegularizer),this.depthwiseConstraint=Ve(e.depthwiseConstraint),this.pointwiseInitializer=he(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ce(e.pointwiseRegularizer),this.pointwiseConstraint=Ve(e.pointwiseConstraint)}build(t){if(t=Wt(t),t.length{t=Nt(t);let n;if(this.rank===1)throw new St("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Vt(t,[0,2,3,1])),n=Im(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=gn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Vt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=_e(this.depthwiseInitializer),t.pointwiseInitializer=_e(this.pointwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.pointwiseRegularizer=me(this.pointwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseConstraint),t.pointwiseConstraint=Be(this.pointwiseConstraint),t}};Ib.className="SeparableConv";var mf=class extends Ib{constructor(t){super(2,t)}};mf.className="SeparableConv2D";Q.registerClass(mf);var Vu=class extends Bu{constructor(t){super(1,t),Vu.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Ey(t.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};Vu.className="Conv1D";Q.registerClass(Vu);var ff=class extends Et{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=Nt(t),this.dataFormat==="channelsLast"){let n=_h(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return _h(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=_h(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return _h(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};ff.className="Cropping2D";Q.registerClass(ff);var df=class extends Et{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,T$(this.interpolation)}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=Nt(t),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Vt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation==="nearest"?lo.resizeNearestNeighbor(n,[s,i]):lo.resizeBilinear(n,[s,i]);return Vt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation==="nearest"?lo.resizeNearestNeighbor(n,[s,i]):lo.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};df.className="UpSampling2D";Q.registerClass(df);function g7(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=hn()),Oe(o);let i=Lh(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=oa(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}var hf=class extends qc{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=he(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ve(t.depthwiseConstraint),this.depthwiseRegularizer=Ce(t.depthwiseRegularizer)}build(t){if(t=Wt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,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(t,e){return B(()=>{t=Nt(t);let n=g7(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=gn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Wt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=Tn(e,this.kernelSize[0],this.padding,this.strides[0]),i=Tn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=_e(this.depthwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseRegularizer),t}};hf.className="DepthwiseConv2D";Q.registerClass(hf);function MN(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function LN(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(dn(2,u));if(t=Vt(t,l),s!=null)throw new St("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=J(J(o,"bool"),"float32"),o.rank===u-1&&(o=ir(o,-1)),o=Vt(o,l)),n&&(t=hr(t,0),o!=null&&(o=hr(o,0)));let c=[],p,m=e,f=t.shape[0],d=xr(t),h;o!=null&&(h=xr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=lt(Ir(N),N),A=Y($(w[0],N),$(m[0],E)),D=m.map((F,P)=>Y($(w[1][P],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=qe(c,1)),[p,g,m]})}var _n=class extends Et{constructor(t){super(t);let e;if(t.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new Xc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ie({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return dn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){zy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ie({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new kn("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(o=>Te([n,o])):this.states_=[Te([n,this.cell.stateSize])];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Te([n,o])):this.states_[0]=Te([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let o=0;o$e(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=MN(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ie({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof rn){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=Nt(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} 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 a={training:o},l=LN((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=Te(t.shape);return e=pt(e,[1,2]),e=kl(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Py(e,[1,n]):e):this.cell.stateSize>1?[Py(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===_n.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=bn(o,n);return new t(Object.assign(e,{cell:s}))}};_n.className="RNN";Q.registerClass(_n);var Dl=class extends Et{},Kc=class extends Dl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,Qe(this.units,"units"),this.activation=di(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Fc([1,pi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Fc([1,pi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Wt(t),this.kernel=this.addWeight("kernel",[t[t.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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0Ir(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0Ir(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Do($(t,i),this.kernel.read()):s=Do(t,this.kernel.read()),this.bias!=null&&(s=gn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=Y(s,Do(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:fi(this.activation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),recurrentInitializer:_e(this.recurrentInitializer),biasInitializer:_e(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};Kc.className="SimpleRNNCell";Q.registerClass(Kc);var gf=class extends _n{constructor(t){t.cell=new Kc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};gf.className="SimpleRNN";Q.registerClass(gf);var jc=class extends Dl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=t.units,Qe(this.units,"units"),this.activation=di(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=di(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Fc([1,pi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Fc([1,pi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Wt(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0Ir(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0Ir(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};xf.className="GRU";Q.registerClass(xf);var $l=class extends Dl{constructor(t){super(t),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=t.units,Qe(this.units,"units"),this.activation=di(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=di(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Fc([1,pi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Fc([1,pi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Wt(t);let n=t[t.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 o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends xn{apply(u,l){let c=s.apply([i]),p=new Pu().apply([i]),m=s.apply([i*2]);return vN(vN(c,p),m)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0Ir(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0Ir(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};yf.className="LSTM";Q.registerClass(yf);var Xc=class extends Dl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{ci(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(bn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return Ah(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Ly(t(),e),a=()=>Ou(i,t,n);return!o||o<=1?$e(a().clone()):Array(o).fill(void 0).map(a).map(l=>$e(l.clone()))}var x7=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z("ConvRNN2D cell does not support constants");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=Te(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new kn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z("If an RNN is stateful, it needs to know its batch size. 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Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let a=0;a$e(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e==="channelsFirst",l=t[u?3:2],c=t[u?4:3],p=Tn(l,o[0],s,i[0],a[0]),m=Tn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};Cb.className="ConvRNN2D";var Yc=class extends $l{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,Qe(this.filters,"filters"),this.kernelSize=zu(n,2,"kernelSize"),this.kernelSize.forEach(u=>Qe(u,"kernelSize")),this.strides=zu(o||1,2,"strides"),this.strides.forEach(u=>Qe(u,"strides")),this.padding=s||"valid",fn(this.padding),this.dataFormat=i||"channelsLast",Oe(this.dataFormat),this.dilationRate=zu(a||1,2,"dilationRate"),this.dilationRate.forEach(u=>Qe(u,"dilationRate"))}build(t){var e;t=Wt(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends xn{apply(m,f){let d=l.apply([c]),h=dr([c]),g=l.apply([c*2]);return Om([d,h,g])}},e.className="CustomInit",e)}else u=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0Ir(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(nt,st,at)=>!st||!st[at]?nt:$(st[at],nt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0Ir(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[I,N,E,A]=gr(this.kernel.read(),a,w),[D,F,P,V]=this.useBias?gr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,I,D,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,P,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=gr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let K=this.recurrentActivation.apply(Y(c,h)),X=this.recurrentActivation.apply(Y(p,g)),Z=Y($(X,i),$(K,this.activation.apply(Y(m,x)))),et=$(this.recurrentActivation.apply(Y(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=x7(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=Sn(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?gn(s,n,this.dataFormat):s}recurrentConv(t,e){return Sn(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Yc.className="ConvLSTM2DCell";Q.registerClass(Yc);var bf=class extends Cb{constructor(t){let e=new Yc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};bf.className="ConvLSTM2D";Q.registerClass(bf);var Zc=class extends Et{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=Nt(t);if(0Ly(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Zc.className="Dropout";Q.registerClass(Zc);var wf=class extends Zc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};wf.className="SpatialDropout1D";Q.registerClass(wf);var If=class extends Et{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,Qe(this.units,"units"),this.activation=di(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ve(t.kernelConstraint),this.biasConstraint=Ve(t.biasConstraint),this.kernelRegularizer=Ce(t.kernelRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Wt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,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]:e}}],this.built=!0}computeOutputShape(t){t=Wt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Nt(t),o=Ay(this.activation.getClassName()),s;return o!=null?s=Do(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Do(n,this.kernel.read()),this.bias!=null&&(s=gn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:fi(this.activation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),biasInitializer:_e(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};If.className="Dense";Q.registerClass(If);var Cf=class extends Et{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Wt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). 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Et{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Nt(t);return Ou(()=>Y(Pm(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};Pf.className="GaussianNoise";Q.registerClass(Pf);var Mf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Nt(t);return this.rate>0&&this.rate<1?Ou(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,Pm(n.shape,1,s))},()=>n,e.training||!1):n})}};Mf.className="GaussianDropout";Q.registerClass(Mf);var Lf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return 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t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_e(this.betaInitializer),gammaInitializer:_e(this.gammaInitializer),movingMeanInitializer:_e(this.movingMeanInitializer),movingVarianceInitializer:_e(this.movingVarianceInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer),betaConstraint:Be(this.betaConstraint),gammaConstraint:Be(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};zf.className="BatchNormalization";Q.registerClass(zf);var Bf=class extends Et{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.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 e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new 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s==="max"?i=ku(r,t,e,a):i=bu(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}function bR(r,t,e,n,o,s){return B(()=>{Oe(o),bN(s),fn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=hn()),s==null&&(s="max"),r=PN(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=qx(r,t,e,a):i=xx(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,4,1,2,3])),i})}var vb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.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(t.poolSize)}`);if(Qe(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof 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e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(Nt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Uf=class extends Sb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),fn(o),_b(t,e,n,o,s,"max")}};Uf.className="MaxPooling2D";Q.registerClass(Uf);var Hf=class extends Sb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),fn(o),_b(t,e,n,o,s,"avg")}};Hf.className="AveragePooling2D";Q.registerClass(Hf);var Nb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.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 ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];Qe(this.poolSize,"poolSize"),Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),fn(this.padding),this.inputSpec=[new Ie({ndim:5})]}computeOutputShape(t){t=Wt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return 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nn{apply(t){return q$(t)}};bb.className="hardSigmoid";J.registerClass(bb);var wb=class extends nn{apply(t){return ai(t)}};wb.className="softplus";J.registerClass(wb);var Ib=class extends nn{apply(t){return H$(t)}};Ib.className="softsign";J.registerClass(Ib);var Cb=class extends nn{apply(t){return ra(t)}};Cb.className="tanh";J.registerClass(Cb);var rf=class extends nn{apply(t,e=-1){return Eu(t,e)}};rf.className="softmax";J.registerClass(rf);var vb=class extends nn{apply(t,e=-1){return dm(t,e)}};vb.className="logSoftmax";J.registerClass(vb);var Sb=class extends nn{apply(t,e=1){return B(()=>$(tn($(t,e)),t))}};Sb.className="swish";J.registerClass(Sb);var Nb=class extends nn{apply(t){return B(()=>$(t,ra(ai(t))))}};Nb.className="mish";J.registerClass(Nb);function di(r){return r.getClassName()}function VN(r,t={}){return fa(r,J.SerializationMap.getMap().classNameMap,t,"activation")}function hi(r){if(r==null){let t={};return t.className="linear",t.config={},VN(t)}if(typeof r=="string"){let t={};return t.className=r,t.config={},VN(t)}else return r instanceof nn?r:VN(r)}function GN(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var kb=class extends J.Serializable{},Lu=class extends kb{constructor(t){super(),GN(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=Te([1]);return this.hasL1&&(e=Y(e,pt($(this.l1,Ee(t))))),this.hasL2&&(e=Y(e,pt($(this.l2,Pc(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Lu.className="L1L2";J.registerClass(Lu);function TR(r){return GN(r),new Lu({l1:r!=null?r.l1:null,l2:0})}function _R(r){return GN(r),new Lu({l2:r!=null?r.l2:null,l1:0})}var NR={l1l2:"L1L2"};function me(r){return Rm(r)}function kR(r,t={}){return fa(r,J.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ce(r){if(r==null)return null;if(typeof r=="string"){let e={className:r in NR?NR[r]:r,config:{}};return kR(e)}else return r instanceof kb?r:kR(r)}var nf=class extends _t{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=St(t);let n=Pr(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};nf.className="ReLU";J.registerClass(nf);var of=class extends _t{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return vu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};of.className="LeakyReLU";J.registerClass(of);var sf=class extends _t{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER="zeros",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=he(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ce(t.alphaRegularizer),this.alphaConstraint=Ve(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes=="number")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Gt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight("alpha",e,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o(Oe(t),t==="channelsFirst"?Vt(r,[0,2,3,1]):r))}function WN(r,t){return B(()=>(Oe(t),t==="channelsFirst"?Vt(r,[0,2,3,4,1]):r))}function D7(r,t,e,n=1,o="valid",s,i=1){return B(()=>{if(s==null&&(s=xn()),Oe(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.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(e!=null&&e.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(r=Vt(r,[0,2,1])),o==="causal")throw new Nt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let a=um(r,t,n,o==="same"?"same":"valid","NWC",i);return e!=null&&(a=yn(a,e)),a})}function ER(r,t,e,n=[1,1],o="valid",s,i,a=null){return B(()=>{if(s==null&&(s=xn()),Oe(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(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Bh(r,s);if(o==="causal")throw new Nt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Ru.conv2d({x:u,filter:t,strides:n,pad:o==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:e,activation:a}),s==="channelsFirst"&&(u=Vt(u,[0,3,1,2])),u})}function $7(r,t,e,n=[1,1,1],o="valid",s,i){return B(()=>{if(s==null&&(s=xn()),Oe(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(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=WN(r,s);if(o==="causal")throw new Nt("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=$x(a,t,n,o==="same"?"same":"valid","NDHWC",i),e!=null&&(a=yn(a,e)),s==="channelsFirst"&&(a=Vt(a,[0,4,1,2,3])),a})}var Kc=class extends _t{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Kc.verifyArgs(e),this.rank=t,Qe(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Nt(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zu(e.kernelSize,t,"kernelSize"),this.strides=zu(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,hn(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Oe(this.dataFormat),this.activation=hi(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=he(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ve(e.biasConstraint),this.biasRegularizer=Ce(e.biasRegularizer),this.activityRegularizer=Ce(e.activityRegularizer),this.dilationRate=zu(e.dilationRate==null?1:e.dilationRate,t,"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(t){if(co("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!Oy(t.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(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:di(this.activation),useBias:this.useBias,biasInitializer:_e(this.biasInitializer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},Bu=class extends Kc{constructor(t,e){super(t,e),this.kernel=null,Bu.verifyArgs(e),this.filters=e.filters,Qe(this.filters,"filters"),this.kernelInitializer=he(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ve(e.kernelConstraint),this.kernelRegularizer=Ce(e.kernelRegularizer)}build(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,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:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=Py(this.activation.getClassName());if(s!=null&&this.rank===2)n=ER(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=D7(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=ER(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=$7(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Nt("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Gt(t);let e=[],n=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},El=class extends Bu{constructor(t){super(2,t),El.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Oy(t.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(t.kernelSize)}.`)}};El.className="Conv2D";J.registerClass(El);var Al=class extends Bu{constructor(t){super(3,t),Al.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Al.className="Conv3D";J.registerClass(Al);var cf=class extends El{constructor(t){if(super(t),this.inputSpec=[new Ie({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat==="channelsFirst"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=gi(u,m,c,this.padding),h=gi(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,1]));let x=pm(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Vt(x,[0,3,1,2])),this.bias!=null&&(x=yn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=gi(e[o],u,i,this.padding),e[s]=gi(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};cf.className="Conv2DTranspose";J.registerClass(cf);var pf=class extends Al{constructor(t){if(super(t),this.inputSpec=[new Ie({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==5)throw new z("Input should have rank 5; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat==="channelsFirst"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=gi(l,h,m,this.padding),w=gi(c,g,f,this.padding),I=gi(p,x,d,this.padding),N=[s,b,w,I,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,4,1]));let E=Fx(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(E=Vt(E,[0,4,1,2,3])),this.bias!==null&&(E=yn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s,i;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=gi(e[o],c,a,this.padding),e[s]=gi(e[s],p,u,this.padding),e[i]=gi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};pf.className="Conv3DTranspose";J.registerClass(pf);var Tb=class extends Bu{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=he(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ce(e.depthwiseRegularizer),this.depthwiseConstraint=Ve(e.depthwiseConstraint),this.pointwiseInitializer=he(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ce(e.pointwiseRegularizer),this.pointwiseConstraint=Ve(e.pointwiseConstraint)}build(t){if(t=Gt(t),t.length{t=St(t);let n;if(this.rank===1)throw new Nt("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Vt(t,[0,2,3,1])),n=Im(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Vt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=_e(this.depthwiseInitializer),t.pointwiseInitializer=_e(this.pointwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.pointwiseRegularizer=me(this.pointwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseConstraint),t.pointwiseConstraint=Be(this.pointwiseConstraint),t}};Tb.className="SeparableConv";var mf=class extends Tb{constructor(t){super(2,t)}};mf.className="SeparableConv2D";J.registerClass(mf);var Vu=class extends Bu{constructor(t){super(1,t),Vu.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Oy(t.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};Vu.className="Conv1D";J.registerClass(Vu);var ff=class extends _t{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat==="channelsLast"){let n=Ah(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ah(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ah(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ah(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};ff.className="Cropping2D";J.registerClass(ff);var df=class extends _t{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,L$(this.interpolation)}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Vt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation==="nearest"?dn.resizeNearestNeighbor(n,[s,i]):dn.resizeBilinear(n,[s,i]);return Vt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation==="nearest"?dn.resizeNearestNeighbor(n,[s,i]):dn.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};df.className="UpSampling2D";J.registerClass(df);function R7(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=xn()),Oe(o);let i=Bh(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=sa(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}var hf=class extends Kc{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=he(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ve(t.depthwiseConstraint),this.depthwiseRegularizer=Ce(t.depthwiseRegularizer)}build(t){if(t=Gt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,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(t,e){return B(()=>{t=St(t);let n=R7(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=En(e,this.kernelSize[0],this.padding,this.strides[0]),i=En(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=_e(this.depthwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseRegularizer),t}};hf.className="DepthwiseConv2D";J.registerClass(hf);function UN(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function HN(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(gn(2,u));if(t=Vt(t,l),s!=null)throw new Nt("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=Q(Q(o,"bool"),"float32"),o.rank===u-1&&(o=ar(o,-1)),o=Vt(o,l)),n&&(t=hr(t,0),o!=null&&(o=hr(o,0)));let c=[],p,m=e,f=t.shape[0],d=xr(t),h;o!=null&&(h=xr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=lt(Ir(N),N),A=Y($(w[0],N),$(m[0],E)),D=m.map((F,P)=>Y($(w[1][P],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=qe(c,1)),[p,g,m]})}var An=class extends _t{constructor(t){super(t);let e;if(t.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new Yc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ie({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return gn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Hy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ie({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new _n("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(o=>Te([n,o])):this.states_=[Te([n,this.cell.stateSize])];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Te([n,o])):this.states_[0]=Te([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let o=0;o$e(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=UN(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ie({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof rn){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} 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 a={training:o},l=HN((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=Te(t.shape);return e=pt(e,[1,2]),e=kl(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Gy(e,[1,n]):e):this.cell.stateSize>1?[Gy(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===An.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=In(o,n);return new t(Object.assign(e,{cell:s}))}};An.className="RNN";J.registerClass(An);var Dl=class extends _t{},jc=class extends Dl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,Qe(this.units,"units"),this.activation=hi(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Oc([1,mi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Oc([1,mi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t),this.kernel=this.addWeight("kernel",[t[t.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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0Ir(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0Ir(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=$o($(t,i),this.kernel.read()):s=$o(t,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=Y(s,$o(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:di(this.activation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),recurrentInitializer:_e(this.recurrentInitializer),biasInitializer:_e(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};jc.className="SimpleRNNCell";J.registerClass(jc);var gf=class extends An{constructor(t){t.cell=new jc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};gf.className="SimpleRNN";J.registerClass(gf);var Xc=class extends Dl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=t.units,Qe(this.units,"units"),this.activation=hi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=hi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Oc([1,mi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Oc([1,mi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0Ir(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0Ir(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};xf.className="GRU";J.registerClass(xf);var $l=class extends Dl{constructor(t){super(t),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=t.units,Qe(this.units,"units"),this.activation=hi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=hi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Oc([1,mi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Oc([1,mi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Gt(t);let n=t[t.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 o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends bn{apply(u,l){let c=s.apply([i]),p=new Pu().apply([i]),m=s.apply([i*2]);return AN(AN(c,p),m)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0Ir(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0Ir(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};yf.className="LSTM";J.registerClass(yf);var Yc=class extends Dl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{pi(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(In(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return $h(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Uy(t(),e),a=()=>Ou(i,t,n);return!o||o<=1?$e(a().clone()):Array(o).fill(void 0).map(a).map(l=>$e(l.clone()))}var F7=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z("ConvRNN2D cell does not support constants");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=Te(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new _n("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.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(()=>Te(s)):this.states_=[Te(s)];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Te(s)):this.states_[0]=Te(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let a=0;a$e(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e==="channelsFirst",l=t[u?3:2],c=t[u?4:3],p=En(l,o[0],s,i[0],a[0]),m=En(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};_b.className="ConvRNN2D";var Zc=class extends $l{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,Qe(this.filters,"filters"),this.kernelSize=zu(n,2,"kernelSize"),this.kernelSize.forEach(u=>Qe(u,"kernelSize")),this.strides=zu(o||1,2,"strides"),this.strides.forEach(u=>Qe(u,"strides")),this.padding=s||"valid",hn(this.padding),this.dataFormat=i||"channelsLast",Oe(this.dataFormat),this.dilationRate=zu(a||1,2,"dilationRate"),this.dilationRate.forEach(u=>Qe(u,"dilationRate"))}build(t){var e;t=Gt(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends bn{apply(m,f){let d=l.apply([c]),h=dr([c]),g=l.apply([c*2]);return Om([d,h,g])}},e.className="CustomInit",e)}else u=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0Ir(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(nt,st,at)=>!st||!st[at]?nt:$(st[at],nt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0Ir(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[I,N,E,A]=gr(this.kernel.read(),a,w),[D,F,P,V]=this.useBias?gr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,I,D,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,P,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=gr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let K=this.recurrentActivation.apply(Y(c,h)),X=this.recurrentActivation.apply(Y(p,g)),Z=Y($(X,i),$(K,this.activation.apply(Y(m,x)))),et=$(this.recurrentActivation.apply(Y(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=F7(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=kn(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?yn(s,n,this.dataFormat):s}recurrentConv(t,e){return kn(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Zc.className="ConvLSTM2DCell";J.registerClass(Zc);var bf=class extends _b{constructor(t){let e=new Zc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};bf.className="ConvLSTM2D";J.registerClass(bf);var Jc=class extends _t{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);if(0Uy(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Jc.className="Dropout";J.registerClass(Jc);var wf=class extends Jc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};wf.className="SpatialDropout1D";J.registerClass(wf);var If=class extends _t{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,Qe(this.units,"units"),this.activation=hi(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ve(t.kernelConstraint),this.biasConstraint=Ve(t.biasConstraint),this.kernelRegularizer=Ce(t.kernelRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Gt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,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]:e}}],this.built=!0}computeOutputShape(t){t=Gt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=Py(this.activation.getClassName()),s;return o!=null?s=$o(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=$o(n,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:di(this.activation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),biasInitializer:_e(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};If.className="Dense";J.registerClass(If);var Cf=class extends _t{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Gt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],Do(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat==="channelsFirst"&&n.rank>1){let o=[0];for(let s=2;s{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:di(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};vf.className="Activation";J.registerClass(vf);var Sf=class extends _t{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),V$(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};Sf.className="RepeatVector";J.registerClass(Sf);var Nf=class extends _t{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e{this.invokeCallHook(t,e);let n=St(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};Nf.className="Reshape";J.registerClass(Nf);var kf=class extends _t{constructor(t){if(super(t),t.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);let e=gn(1,t.dims.length+1);if(!y.arraysEqual(t.dims.slice().sort(),e))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ie({ndim:this.dims.length+1})]}computeOutputShape(t){t=Gt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Vt(St(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};kf.className="Permute";J.registerClass(kf);var Tf=class extends _t{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=St(t),o=-1;return dc(li(n,this.maskValue),o)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=-1,s=!0,i=dc(li(n,this.maskValue),o,s);return $(n,Q(i,n.dtype))})}};Tf.className="Masking";J.registerClass(Tf);var _f=class extends _t{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(we(t.inputLength))}this.inputDim=t.inputDim,Qe(this.inputDim,"inputDim"),this.outputDim=t.outputDim,Qe(this.outputDim,"outputDim"),this.embeddingsInitializer=he(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ce(t.embeddingsRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.embeddingsConstraint=Ve(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=St(t),li(t,vt(t))):null)}computeOutputShape(t){if(t=Gt(t),this.inputLength==null)return[...t,this.outputDim];let e=we(this.inputLength);if(e.length!==t.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);n.dtype!=="int32"&&(n=en(n,"int32"));let o=Wy(this.embeddings.read(),R(n,[n.size]));return R(o,Gt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:_e(this.embeddingsInitializer),embeddingsRegularizer:me(this.embeddingsRegularizer),activityRegularizer:me(this.activityRegularizer),embeddingsConstraint:Be(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};_f.className="Embedding";J.registerClass(_f);var Fl=class extends _t{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new Nt}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(t)}.`);let n=t[0]==null?null:t[0].slice(1);for(let s=1;ss.length);t.indexOf(null)===-1&&Ao(o).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(t,e){return B(()=>{if(t=t,this.reshapeRequired){let n=[],o=t.map(s=>s.rank);if(o.indexOf(null)===-1){let s=mi(o);for(let i of t){let a=i.rank;for(let u=0;u1){let c=gn(1,l).concat([0]);n.push(Vt(u,c)),s=!0}else n.push(u)}let i=this.mergeFunction(n),a=i.rank;if(s){if(a==null){let u=i.shape,l=u.length,c=u[l-1],p=[c].concat(u.slice(0,u.length-1));i=R(Vt(R(i,[-1,c]),[1,0]),p)}else if(a>1){let u=[a-1].concat(gn(0,a-1));i=Vt(i,u)}}return i}}else return this.mergeFunction(t)})}computeOutputShape(t){t=t;let e;t[0]==null?e=null:e=t[0].slice(1);for(let o=1;o{if(e==null)return null;if(!Array.isArray(e))throw new z("`mask` should be an Array");if(!Array.isArray(t))throw new z("`inputs` should be an Array");if(e.length!==t.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${t.length} vs ${e.length})`);if(e.every(o=>o==null))return null;e=e.map(o=>o==null?o:ar(o,0));let n=e[0];for(let o=1;o{let e=t[0].clone();for(let n=1;n{let e=t[0].clone();for(let n=1;n{let e=t[0].clone();for(let n=1;n{let e=t[0];for(let n=1;n{let e=t[0];for(let n=1;n1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(t))}mergeFunction(t){return B(()=>Om(t,this.axis))}computeOutputShape(t){if(!(Array.isArray(t)&&Array.isArray(t[0])))throw new z("A `Concatenate` layer should be called on a list of inputs.");let e=t,n=e[0].slice(),o=this.axis<0?n.length+this.axis:this.axis;for(let s of e.slice(1)){if(n[o]==null||s[o]==null){n[o]=null;break}n[o]+=s[o]}return n}computeMask(t,e){if(e==null)return null;if(!Array.isArray(e))throw new z("`mask` should be an array for Concatenate");if(!Array.isArray(t))throw new z("`inputs` should be an array for Concatenate");if(e.length!==t.length)throw new z(`Mismatch in the length of mask (${e.length}) and the legnth of inputs (${t.length})`);return B(()=>{let n=!0;if(e.forEach(i=>{if(i!=null){n=!1;return}}),n)return null;let o=[];for(let i=0;i3||t.shape.length>3)throw new Nt("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 ${t.shape.length}`),typeof e=="number"&&(e=[e,e]),r.dtype==="complex64"||t.dtype==="complex64")throw new Nt("batchDot is not implemented for complex64-type Tensors yet.");let n=r.shape.length,o=t.shape.length;e==null&&(e=[n-1,o-2]);let s=e;return B(()=>{let i;if(n>o){i=n-o;let u=[];for(let l=0;ln){i=o-n;let u=[];for(let l=0;l0){let u;n>o?u=n+o-3:u=n-1;let l=[];for(let c=u;c"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0],n=t[1];if(e.length>3||n.length>3)throw new Nt("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);if(e[o[0]]!==n[o[1]])throw new z(`Dimension incompatibility: ${e[o[0]]} !== ${n[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],n=t[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,i)=>Vh(s,t[i].shape.length)):o=[Vh(this.axes,e.shape.length),Vh(this.axes,n.shape.length)],this.normalize&&(e=Rh(e,o[0]),n=Rh(n,o[1])),O7(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Vh(this.axes,t.length),Vh(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new Nt("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Of.className="Dot";J.registerClass(Of);var Pf=class extends _t{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return Ou(()=>Y(Pm(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};Pf.className="GaussianNoise";J.registerClass(Pf);var Mf=class extends _t{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?Ou(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,Pm(n.shape,1,s))},()=>n,e.training||!1):n})}};Mf.className="GaussianDropout";J.registerClass(Mf);var Lf=class extends _t{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return 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e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ie({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=gn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=_o(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,gn(0,i).slice(0,i-1)),m=()=>{if(p){let b=R(this.movingMean.read(),l),w=R(this.movingVariance.read(),l),I=this.center?R(this.beta.read(),l):null,N=this.scale?R(this.gamma.read(),l):null;return Gh(o,b,w,I,N,this.epsilon)}else return Gh(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=L7(o,this.gamma.read(),this.beta.read(),a,this.epsilon),g=(b,w,I)=>{B(()=>{let N=1-I,E=b.read(),A=$(lt(E,w),N);b.write(lt(E,A))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_e(this.betaInitializer),gammaInitializer:_e(this.gammaInitializer),movingMeanInitializer:_e(this.movingMeanInitializer),movingVarianceInitializer:_e(this.movingVarianceInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer),betaConstraint:Be(this.betaConstraint),gammaConstraint:Be(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};zf.className="BatchNormalization";J.registerClass(zf);var Bf=class extends _t{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.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 e of this.axis)if(!Number.isInteger(e))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=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=he(t.betaInitializer||"zeros"),this.gammaInitializer=he(t.gammaInitializer||"ones"),this.betaRegularizer=Ce(t.betaRegularizer),this.gammaRegularizer=Ce(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Gt(t);let e=t.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==Ao(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=yc(n,this.axis,!0),l=_o(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.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(e==null&&(e=xn()),e!=="channelsLast"&&e!=="channelsFirst")throw new z(`Unknown data format: ${e}. 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length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){t=Gt(t);let e,n;return this.dataFormat==="channelsFirst"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?n=t[3]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],t[1],e,n]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?n=t[2]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],e,n,t[3]])}call(t,e){return B(()=>z7(St(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};Vf.className="ZeroPadding2D";J.registerClass(Vf);function Fb(r,t,e,n,o,s){return B(()=>{Oe(o),kN(s),hn(n),e==null&&(e=[1,1]),n==null&&(n="valid"),o==null&&(o=xn()),s==null&&(s="max"),r=Bh(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=ku(r,t,e,a):i=bu(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}function AR(r,t,e,n,o,s){return B(()=>{Oe(o),kN(s),hn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=xn()),s==null&&(s="max"),r=WN(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=Zx(r,t,e,a):i=Cx(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,4,1,2,3])),i})}var Eb=class extends _t{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.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(t.poolSize)}`);if(Qe(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]=="number")this.strides=t.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(t.strides)}`);Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,hn(this.padding),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){t=Gt(t);let e=En(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=kl(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Un(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Gf=class extends Eb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),Fb(t,e,n,o,s,"max")}};Gf.className="MaxPooling1D";J.registerClass(Gf);var Wf=class extends Eb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),Fb(t,e,n,o,s,"avg")}};Wf.className="AveragePooling1D";J.registerClass(Wf);var Ab=class extends _t{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.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 ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];Qe(this.poolSize,"poolSize"),Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),hn(this.padding),this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=En(e,this.poolSize[0],this.padding,this.strides[0]),n=En(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Uf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),Fb(t,e,n,o,s,"max")}};Uf.className="MaxPooling2D";J.registerClass(Uf);var Hf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),Fb(t,e,n,o,s,"avg")}};Hf.className="AveragePooling2D";J.registerClass(Hf);var Db=class extends _t{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.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 ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];Qe(this.poolSize,"poolSize"),Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),hn(this.padding),this.inputSpec=[new Ie({ndim:5})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=En(e,this.poolSize[0],this.padding,this.strides[0]),n=En(n,this.poolSize[1],this.padding,this.strides[1]),o=En(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},qf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),AR(t,e,n,o,s,"max")}};qf.className="MaxPooling3D";J.registerClass(qf);var Kf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),AR(t,e,n,o,s,"avg")}};Kf.className="AveragePooling3D";J.registerClass(Kf);var $b=class extends _t{constructor(t){super(t),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new Nt}},jf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return ke(n,1)})}};jf.className="GlobalAveragePooling1D";J.registerClass(jf);var Xf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Sr(n,1)})}};Xf.className="GlobalMaxPooling1D";J.registerClass(Xf);var Rb=class extends _t{constructor(t){super(t),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat==="channelsLast"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new Nt}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Yf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat==="channelsLast"?ke(n,[1,2]):ke(n,[2,3])})}};Yf.className="GlobalAveragePooling2D";J.registerClass(Yf);var Zf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return 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t(i)}},Jf=class extends Ob{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Gt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Gt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),HN((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Jf.className="TimeDistributed";J.registerClass(Jf);function B7(r){da(P$,"BidirectionalMergeMode",r)}var V7="concat",Qf=class extends Ob{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=In(n),e.goBackwards=e.goBackwards!==!0;let 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TypeError(`Node type ${r.op} is not implemented`)}};var YR=(r,t,e,n=se)=>{switch(r.op){case"ResizeBilinear":{let o=C("images",r,t,e),s=C("size",r,t,e),i=C("alignCorners",r,t,e),a=C("halfPixelCenters",r,t,e);return[n.image.resizeBilinear(o,[s[0],s[1]],i,a)]}case"ResizeNearestNeighbor":{let o=C("images",r,t,e),s=C("size",r,t,e),i=C("alignCorners",r,t,e),a=C("halfPixelCenters",r,t,e);return[n.image.resizeNearestNeighbor(o,[s[0],s[1]],i,a)]}case"CropAndResize":{let o=C("image",r,t,e),s=C("boxes",r,t,e),i=C("boxInd",r,t,e),a=C("cropSize",r,t,e),u=C("method",r,t,e),l=C("extrapolationValue",r,t,e);return[n.image.cropAndResize(o,s,i,a,u,l)]}case"ImageProjectiveTransformV3":{let o=C("images",r,t,e),s=C("transforms",r,t,e),i=C("outputShape",r,t,e),a=C("fillValue",r,t,e),u=C("interpolation",r,t,e),l=C("fillMode",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZR=(r,t,e,n=se)=>{switch(r.op){case"Equal":return[n.equal(C("a",r,t,e),C("b",r,t,e))];case"NotEqual":return[n.notEqual(C("a",r,t,e),C("b",r,t,e))];case"Greater":return[n.greater(C("a",r,t,e),C("b",r,t,e))];case"GreaterEqual":return[n.greaterEqual(C("a",r,t,e),C("b",r,t,e))];case"Less":return[n.less(C("a",r,t,e),C("b",r,t,e))];case"LessEqual":return[n.lessEqual(C("a",r,t,e),C("b",r,t,e))];case"LogicalAnd":return[n.logicalAnd(C("a",r,t,e),C("b",r,t,e))];case"LogicalNot":return[n.logicalNot(C("a",r,t,e))];case"LogicalOr":return[n.logicalOr(C("a",r,t,e),C("b",r,t,e))];case"Select":case"SelectV2":return[n.where(C("condition",r,t,e),C("a",r,t,e),C("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var JR=(r,t,e,n=se)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(C("a",r,t,e),C("b",r,t,e),C("transposeA",r,t,e),C("transposeB",r,t,e))];case"Einsum":return[n.einsum(C("equation",r,t,e),...C("tensors",r,t,e))];case"Transpose":return[n.transpose(C("x",r,t,e),C("perm",r,t,e))];case"_FusedMatMul":let[o,s]=C("fusedOps",r,t,e),i=o==="biasadd",a=s==="prelu",u=C("numArgs",r,t,e),l=C("leakyreluAlpha",r,t,e);if(i){if(a&&u!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&u!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,p]=C("args",r,t,e);return[n.fused.matMul({a:C("a",r,t,e),b:C("b",r,t,e),transposeA:C("transposeA",r,t,e),transposeB:C("transposeB",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case"MatrixBandPart":return[n.linalg.bandPart(C("a",r,t,e),C("numLower",r,t,e),C("numUpper",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QR=(r,t,e,n=se)=>{switch(r.op){case"EuclideanNorm":return[n.euclideanNorm(C("x",r,t,e),C("axis",r,t,e),C("keepDims",r,t,e))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(C("x",r,t,e),C("mean",r,t,e),C("variance",r,t,e),C("offset",r,t,e),C("scale",r,t,e),C("epsilon",r,t,e))];case"FusedBatchNormV3":return[n.batchNorm(C("x",r,t,e),C("mean",r,t,e),C("variance",r,t,e),C("offset",r,t,e),C("scale",r,t,e),C("epsilon",r,t,e))];case"LRN":return[n.localResponseNormalization(C("x",r,t,e),C("radius",r,t,e),C("bias",r,t,e),C("alpha",r,t,e),C("beta",r,t,e))];case"Softmax":return[n.softmax(C("x",r,t,e))];case"LogSoftmax":return[n.logSoftmax(C("x",r,t,e))];case"SparseToDense":return[n.sparseToDense(C("sparseIndices",r,t,e),C("outputShape",r,t,e),C("sparseValues",r,t,e),C("defaultValue",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var tF=(r,t,e,n=se)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(C("paramsNestedSplits",r,t,e),C("paramsDenseValues",r,t,e),C("indices",r,t,e),C("outputRaggedRank",r,t,e));return o.concat(s)}case"RaggedRange":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(C("starts",r,t,e),C("limits",r,t,e),C("splits",r,t,e));return[o,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(C("shape",r,t,e),C("values",r,t,e),C("defaultValue",r,t,e),C("rowPartitionTensors",r,t,e),C("rowPartitionTypes",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var eF=(r,t,e,n=se)=>{switch(r.op){case"Max":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.max(C("x",r,t,e),a,u)]}case"Mean":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.mean(C("x",r,t,e),a,u)]}case"Min":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.min(C("x",r,t,e),a,u)]}case"Sum":{let 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nF=(r,t,e,n=se)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(C("indices",r,t,e),C("values",r,t,e),C("denseShape",r,t,e),C("defaultValue",r,t,e));return[o,s,i,a]}case"SparseReshape":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(C("inputIndices",r,t,e),C("inputShape",r,t,e),C("newShape",r,t,e));return[o,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(C("data",r,t,e),C("indices",r,t,e),C("segmentIds",r,t,e))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(C("data",r,t,e),C("indices",r,t,e),C("segmentIds",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var oF=(r,t,e,n=se)=>{switch(r.op){case"FFT":return[n.fft(C("x",r,t,e))];case"IFFT":return[n.ifft(C("x",r,t,e))];case"RFFT":return[n.rfft(C("x",r,t,e))];case"IRFFT":return[n.irfft(C("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var sF=(r,t,e,n=se)=>{switch(r.op){case"StaticRegexReplace":return[n.string.staticRegexReplace(C("input",r,t,e),C("pattern",r,t,e),C("rewrite",r,t,e),C("replaceGlobal",r,t,e))];case"StringNGrams":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(C("data",r,t,e),C("dataSplits",r,t,e),C("separator",r,t,e),C("nGramWidths",r,t,e),C("leftPad",r,t,e),C("rightPad",r,t,e),C("padWidth",r,t,e),C("preserveShortSequences",r,t,e));return[o,s]}case"StringSplit":{let{indices:o,values:s,shape:i}=n.string.stringSplit(C("input",r,t,e),C("delimiter",r,t,e),C("skipEmpty",r,t,e));return[o,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(C("input",r,t,e),C("numBuckets",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var iF=(r,t,e,n=se)=>{switch(r.op){case"Cast":return[n.cast(C("x",r,t,e),C("dtype",r,t,e))];case"ExpandDims":{let o=C("axis",r,t,e);return[n.expandDims(C("x",r,t,e),o)]}case"Squeeze":{let o=C("axis",r,t,e);return[n.squeeze(C("x",r,t,e),o)]}case"Reshape":return[n.reshape(C("x",r,t,e),C("shape",r,t,e))];case"MirrorPad":return[n.mirrorPad(C("x",r,t,e),C("padding",r,t,e),C("mode",r,t,e))];case"PadV2":case"Pad":return[n.pad(C("x",r,t,e),C("padding",r,t,e),C("constantValue",r,t,e))];case"SpaceToBatchND":{let o=C("blockShape",r,t,e),s=C("paddings",r,t,e);return[n.spaceToBatchND(C("x",r,t,e),o,s)]}case"BatchToSpaceND":{let o=C("blockShape",r,t,e),s=C("crops",r,t,e);return[n.batchToSpaceND(C("x",r,t,e),o,s)]}case"DepthToSpace":{let o=C("blockSize",r,t,e),s=C("dataFormat",r,t,e).toUpperCase();return[n.depthToSpace(C("x",r,t,e),o,s)]}case"BroadcastTo":return[n.broadcastTo(C("x",r,t,e),C("shape",r,t,e))];case"BroadcastArgs":return[n.broadcastArgs(C("s0",r,t,e),C("s1",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function pk(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case"arithmetic":return o(()=>OR(i,a,u));case"basic_math":return o(()=>PR(i,a,u));case"control":return GR(i,a,u);case"convolution":return o(()=>UR(i,a,u));case"creation":return o(()=>HR(i,a,u));case"dynamic":return qR(i,a,u);case"evaluation":return o(()=>KR(i,a,u));case"image":return o(()=>YR(i,a,u));case"graph":return o(()=>jR(i,a,u));case"logical":return o(()=>ZR(i,a,u));case"matrices":return o(()=>JR(i,a,u));case"normalization":return o(()=>QR(i,a,u));case"ragged":return o(()=>tF(i,a,u));case"reduction":return o(()=>eF(i,a,u));case"slice_join":return o(()=>rF(i,a,u));case"sparse":return o(()=>nF(i,a,u));case"spectral":return o(()=>oF(i,a,u));case"string":return o(()=>sF(i,a,u));case"transformation":return o(()=>iF(i,a,u));case"hash_table":return XR(i,a,u,n);case"custom":let l=Rb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Hb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Wh=class{constructor(t={},e={},n={},o={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),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 t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function mk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>wn(m)[0]));n=n||[];let c=new Set(n.map(m=>wn(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Jc(m)||o9(m)||s9(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&!l.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function aF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>wn(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],i=g=>e.has(typeof g=="string"?g:g.name);function a(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let u=a([...o,...r.weights,...s]).filter(i),l=a([...u,...Object.values(r.nodes)]).filter(i),c=new Map(l.map(g=>[g.name,g])),p={};for(let g of l){p[g.name]=p[g.name]||0;for(let x of g.children)i(x)||(p[x.name]=Number.POSITIVE_INFINITY),p[x.name]=(p[x.name]||0)+1}let m=Object.entries(p).filter(([,g])=>g===0).map(([g])=>g),f=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(i))--p[b.name]===0&&(f.push(b.name),m.push(b.name))}let d=f.map(g=>c.get(g)),h=QQ(d,u);return t9(h,u),h}function QQ(r,t){let e=new Map(r.map(i=>[i.name,i])),n=t.map(i=>i.name),o=new Set(n);for(;n.length>0;){let i=n.pop(),a=e.get(i);for(let u of a.children)!e.has(u.name)||o.has(u.name)||(o.add(u.name),n.push(u.name))}return r.filter(i=>o.has(i.name))}var sd=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function t9(r,t){let e=new Map(r.map((a,u)=>[a.name,u])),n=new Set(t.map(a=>a.name)),o=a=>n.has(typeof a=="string"?a:a.name),s=new Set(r.map(a=>a.name)),i=a=>s.has(typeof a=="string"?a:a.name);for(let a of r){for(let u of a.children.filter(i)){if(!e.has(u.name))throw new sd(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new sd(`Node ${a.name} is scheduled to run after its child ${u.name}.`)}if(!o(a))for(let u of a.inputs){if(!e.has(u.name))throw new sd(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new sd(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function lF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Jc(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;at[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new Qc(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+"--"+o.join(this.SEPARATOR)}compile(t,e){let n=mk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let l=e.map(p=>p.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${c}]. Missing the following inputs: [${o}]`)}let a=aF(this.graph,n),u=lF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return $e(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[wn(m)[0]]),s=e.map(m=>wn(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Wh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=wn(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=pk(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);f[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,f,m,d,i,g.get(x.name))}return this.parent==null&&m.dispose(d),e.map(x=>cr(x,f,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){if(!(Jc(e)||i.has(t))){for(let u of n[t])u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length);for(let u of e.inputs){if(Jc(u))continue;let l=WN(u.name,n,o);if(l!=null)for(let c of l){if(!c||c.kept||s.has(c.id))continue;let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,n,o,s,i){if(!(Jc(t)||s.has(t.name))&&i!=null)for(let a of i){let u=WN(a,e,n);for(let l of u)!l||l.kept||o.has(l.id)||l.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Wh(this.weightMap,o,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>cr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(I=>this.graph.nodes[wn(I)[0]]),a=n.map(I=>wn(I)[0]),u=new Set(a),l=a.map(I=>this.graph.nodes[I]);l.length===0&&(l=this._outputs);let{usedNodes:c,missingInputs:p,dynamicNode:m,syncInputs:f}=mk(t,l,this.weightMap,this._initNodes),d=[...i,...this.graph.weights,...this._initNodes||[]].map(I=>({node:I,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(I=>{let[N,E]=wn(I),A=[];A[E]=t[I],h[N]=A});let g={},x=this.getFrozenTensorIds(h),b={};for(;d.length>0;){let I=this.processStack(i,d,e,h,b,x,u,g,c);await Promise.all(I)}m==null&&!o&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let w=l.filter(I=>!Jc(I)&&!cr(I.name,h,e)).map(I=>I.name);if(w.length>0){let I="";throw m!=null&&(I=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${f}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${p}]. ${I}`)}return h}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&C("isConstant",p.node,o,n)&&([m]=gi(p.node.name,n)),o[p.node.name]==null){let f=pk(p.node,o,n,this._resourceManager);m||([m]=gi(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=gi(a.name,n);s[u]||!i.has(a.name)||(a.op==="Merge"?a.inputNames.some(l=>!!cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=wn(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=wn(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=wn(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var Xb=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var i9="?tfjs-format=file",a9="model.json",Uh=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},n=Mr){this.modelUrl=t,this.loadOptions=e,this.version="n/a",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new Xb}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let t=this.handler.load();return y.isPromise(t)?t.then(e=>this.loadSync(e)):this.loadSync(t)}loadSync(t){this.artifacts=t;let e=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=n,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let 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c=(l=(u=(a=this.signature)===null||a===void 0?void 0:a.inputs)===null||u===void 0?void 0:u[i])===null||l===void 0?void 0:l.resourceId;return c!=null?s[i]=this.resourceIdToCapturedInput[c]:s[i]=t[o++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(t){if(this.resourceIdToCapturedInput={},this.initializerSignature){let e=this.initializerSignature.outputs,n=Object.keys(e);for(let o=0;o1?n:n[0]}async executeAsync(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await 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r=="object"&&r instanceof Pt||y.isTypedArray(r)}function p9(r){return r===null||typeof r!="object"&&typeof r!="function"}function dF(r){return cF(r,m9)}function m9(r){return r instanceof Pt?{value:r.clone(),recurse:!1}:Gu(r)?{value:null,recurse:!0}:{value:r,recurse:!1}}var id=class{constructor(t){if(this.capacity=t,this.begin=0,this.end=0,t==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(t<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(t),this.doubledCapacity=2*t}wrap(t){for(;t<0;)t+=this.doubledCapacity;return t%this.doubledCapacity}get(t){if(t<0)throw new RangeError("Can't get item at a negative index.");return this.data[t%this.capacity]}set(t,e){if(t<0)throw new RangeError("Can't set item at a negative index.");this.data[t%this.capacity]=e}length(){let t=this.end-this.begin;return t<0&&(t=this.doubledCapacity+t),t}isFull(){return this.length()===this.capacity}isEmpty(){return 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id{constructor(){super(tp.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),n=this.length();for(let o=0;oe===!0)}rowMajorBatch(t,e=!0){return new wk(this,t,e)}columnMajorBatch(t,e=!0,n=dk){return this.rowMajorBatch(t,e).map(s=>pF(s,n))}concatenate(t,e){return new Qb(Tk([this,t]),e)}take(t){return t<0||t==null?this:new bk(this,t)}skip(t){return t<0||t==null?this:new yk(this,t)}prefetch(t){return new tw(this,t)}shuffle(t,e){return new kk(this,t,e)}serial(){return new xk(this)}},hk=class extends tr{constructor(t){super(),this.items=t,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 t=this.items[this.trav];return this.trav++,{value:dF(t),done:!1}}},gk=class extends tr{constructor(t){super(),this.nextFn=t}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}},xk=class extends tr{constructor(t){super(),this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},yk=class extends tr{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},wk=class extends tr{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},Ik=class extends tr{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},Ck=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},vk=class extends tr{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},Jb=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=await this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},ep=class extends tr{constructor(){super(),this.outputQueue=new tp,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}}},Sk=class extends ep{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return!0}},Qb=class extends tr{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return"TODO: fill in upstream of chained summaries 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Zb(this.iterators,o);if(e===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Pl.FAIL:throw new Error(`Zipped streams should have the same length. 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At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!L().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let e=new ud(t);return await e.start(),e}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 t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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 e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.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 t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},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(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),or(n,e)}};var cd=class extends tr{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ke([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=li([i,s,u,a],[1,4])}else this.cropBox=li([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(!L().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new cd(t,e);return await n.start(),n}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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=Sy.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=ir(J(t,"float32"),0),n;n=lo.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var pd=class{};var Kh=class extends tr{split(t){return new Ek(this,t)}},Ek=class extends Kh{constructor(t,e){super(),this.upstream=t,this.impl=new Ak(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Ak=class extends ep{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var nw=class extends tr{decodeUTF8(){return new Dk(this)}},Dk=class extends Kh{constructor(t){super(),this.upstream=t,this.impl=new $k(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},$k=class extends ep{constructor(t){if(super(),this.upstream=t,L().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=fk();this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let n;return L().get("IS_BROWSER")?n=this.decoder.decode(e,{stream:!0}):n=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(n),!0}};var md=class extends nw{constructor(t,e={}){super(),this.file=t,this.options=e,y.assert(t instanceof Uint8Array||(L().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=a=>{let u=s.result;if(u instanceof ArrayBuffer&&(u=new Uint8Array(u)),!(u instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));e(u)},s.onabort=a=>n(new Error("Aborted")),s.onerror=a=>n(new Error(a.type));let i=this.file.slice(this.offset,o);s.readAsArrayBuffer(i)}this.offset=o}),done:!1}}};async function vF(r,t={},e){let n,o;typeof r=="string"?n=r:(n=r.url,o=h9(r));let s=await(e||y.fetch)(n,o);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new md(i,t)}else throw new Error(s.statusText)}var h9=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function ow(r){return typeof r=="string"&&r.slice(0,7)==="file://"}var fd=class extends pd{constructor(t,e={}){super(),this.input=t,this.options=e}async iterator(){if(ow(this.input)&&L().get("IS_NODE")){let t=sw();this.input=t.readFileSync(this.input.slice(7))}return new md(this.input,this.options)}};var dd=class extends pd{constructor(t,e={}){super(),this.url=t,this.fileOptions=e}async iterator(){return ow(this.url)?new fd(this.url,this.fileOptions).iterator():vF(this.url,this.fileOptions)}};function SF(r,t={}){return new ld(new dd(r),t)}function NF(r){let t=Hh(r);return En(async()=>t)}function kF(r){return En(async()=>{let t=await r();return Hh(()=>t.next())})}async function TF(r,t){return cd.create(r,t)}async function _F(r){return ud.create(r)}var Rk="4.3.0";function tt(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var g9=Kr.whereImpl,Wu=class extends Go{nextDataId(){return Wu.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Ta(this,Bn())}write(t,e,n){this.firstUse&&(this.firstUse=!1,L().get("IS_NODE")&&S.warn(` + ${n}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),i=Jb(s,e),a=n===0?0:r.size/n,u=B(()=>{let c=[];r=R(r,[1,n,a]);for(let p=0;p{switch(r.op){case"If":case"StatelessIf":{let n=C("thenBranch",r,t,e),o=C("elseBranch",r,t,e),s=C("cond",r,t,e),i=C("args",r,t,e);return(await s.data())[0]?e.functionMap[n].executeFunctionAsync(i,e.tensorArrayMap,e.tensorListMap):e.functionMap[o].executeFunctionAsync(i,e.tensorArrayMap,e.tensorListMap)}case"While":case"StatelessWhile":{let n=C("body",r,t,e),o=C("cond",r,t,e),s=C("args",r,t,e),i=await e.functionMap[o].executeFunctionAsync(s,e.tensorArrayMap,e.tensorListMap),a=s.map(c=>c.id),u=await i[0].data();i.forEach(c=>{!c.kept&&a.indexOf(c.id)===-1&&c.dispose()});let l=s;for(;u[0];){let c=l;l=await e.functionMap[n].executeFunctionAsync(l,e.tensorArrayMap,e.tensorListMap);let p=l.map(f=>f.id);c.forEach(f=>{!f.kept&&a.indexOf(f.id)===-1&&p.indexOf(f.id)===-1&&f.dispose()});let m=await e.functionMap[o].executeFunctionAsync(l,e.tensorArrayMap,e.tensorListMap);u=await m[0].data(),m.forEach(f=>{!f.kept&&a.indexOf(f.id)===-1&&p.indexOf(f.id)===-1&&f.dispose()})}return 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performance."),console.log(u);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ft(this.size(),"int32")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),B(()=>{let o=xr(e),s=n.length,i=o.length;y.assert(s===i,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${i} elements.`);for(let a=0;a{let o=[];for(let s=0;s{switch(r.op){case"HashTable":case"HashTableV2":{let o=n.getHashTableHandleByName(r.name);if(o!=null)return[o];{let s=C("keyDType",r,t,e),i=C("valueDType",r,t,e),a=new tw(s,i);return n.addHashTable(r.name,a),[a.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let o=C("tableHandle",r,t,e,n),s=C("keys",r,t,e),i=C("values",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let 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o=C("images",r,t,e),s=C("transforms",r,t,e),i=C("outputShape",r,t,e),a=C("fillValue",r,t,e),u=C("interpolation",r,t,e),l=C("fillMode",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var uF=(r,t,e,n=ie)=>{switch(r.op){case"Equal":return[n.equal(C("a",r,t,e),C("b",r,t,e))];case"NotEqual":return[n.notEqual(C("a",r,t,e),C("b",r,t,e))];case"Greater":return[n.greater(C("a",r,t,e),C("b",r,t,e))];case"GreaterEqual":return[n.greaterEqual(C("a",r,t,e),C("b",r,t,e))];case"Less":return[n.less(C("a",r,t,e),C("b",r,t,e))];case"LessEqual":return[n.lessEqual(C("a",r,t,e),C("b",r,t,e))];case"LogicalAnd":return[n.logicalAnd(C("a",r,t,e),C("b",r,t,e))];case"LogicalNot":return[n.logicalNot(C("a",r,t,e))];case"LogicalOr":return[n.logicalOr(C("a",r,t,e),C("b",r,t,e))];case"Select":case"SelectV2":return[n.where(C("condition",r,t,e),C("a",r,t,e),C("b",r,t,e))];case"BitwiseAnd":return[n.bitwiseAnd(C("a",r,t,e),C("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var cF=(r,t,e,n=ie)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(C("a",r,t,e),C("b",r,t,e),C("transposeA",r,t,e),C("transposeB",r,t,e))];case"Einsum":return[n.einsum(C("equation",r,t,e),...C("tensors",r,t,e))];case"Transpose":return[n.transpose(C("x",r,t,e),C("perm",r,t,e))];case"_FusedMatMul":let[o,s]=C("fusedOps",r,t,e),i=o==="biasadd",a=s==="prelu",u=C("numArgs",r,t,e),l=C("leakyreluAlpha",r,t,e);if(i){if(a&&u!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&u!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,p]=C("args",r,t,e);return[n.fused.matMul({a:C("a",r,t,e),b:C("b",r,t,e),transposeA:C("transposeA",r,t,e),transposeB:C("transposeB",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case"MatrixBandPart":return[n.linalg.bandPart(C("a",r,t,e),C("numLower",r,t,e),C("numUpper",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var pF=(r,t,e,n=ie)=>{switch(r.op){case"EuclideanNorm":return[n.euclideanNorm(C("x",r,t,e),C("axis",r,t,e),C("keepDims",r,t,e))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(C("x",r,t,e),C("mean",r,t,e),C("variance",r,t,e),C("offset",r,t,e),C("scale",r,t,e),C("epsilon",r,t,e))];case"FusedBatchNormV3":return[n.batchNorm(C("x",r,t,e),C("mean",r,t,e),C("variance",r,t,e),C("offset",r,t,e),C("scale",r,t,e),C("epsilon",r,t,e))];case"LRN":return[n.localResponseNormalization(C("x",r,t,e),C("radius",r,t,e),C("bias",r,t,e),C("alpha",r,t,e),C("beta",r,t,e))];case"Softmax":return[n.softmax(C("x",r,t,e))];case"LogSoftmax":return[n.logSoftmax(C("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var mF=(r,t,e,n=ie)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(C("paramsNestedSplits",r,t,e),C("paramsDenseValues",r,t,e),C("indices",r,t,e),C("outputRaggedRank",r,t,e));return o.concat(s)}case"RaggedRange":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(C("starts",r,t,e),C("limits",r,t,e),C("splits",r,t,e));return[o,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(C("shape",r,t,e),C("values",r,t,e),C("defaultValue",r,t,e),C("rowPartitionTensors",r,t,e),C("rowPartitionTypes",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var fF=(r,t,e,n=ie)=>{switch(r.op){case"Max":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.max(C("x",r,t,e),a,u)]}case"Mean":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.mean(C("x",r,t,e),a,u)]}case"Min":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.min(C("x",r,t,e),a,u)]}case"Sum":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.sum(C("x",r,t,e),a,u)]}case"All":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.all(C("x",r,t,e),a,u)]}case"Any":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.any(C("x",r,t,e),a,u)]}case"ArgMax":{let a=C("axis",r,t,e);return[n.argMax(C("x",r,t,e),a)]}case"ArgMin":{let a=C("axis",r,t,e);return[n.argMin(C("x",r,t,e),a)]}case"Prod":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.prod(C("x",r,t,e),a,u)]}case"Cumprod":{let a=C("axis",r,t,e),u=C("exclusive",r,t,e),l=C("reverse",r,t,e);return[n.cumprod(C("x",r,t,e),a,u,l)]}case"Cumsum":{let a=C("axis",r,t,e),u=C("exclusive",r,t,e),l=C("reverse",r,t,e);return[n.cumsum(C("x",r,t,e),a,u,l)]}case"Bincount":let o=C("x",r,t,e),s=C("weights",r,t,e),i=C("size",r,t,e);return[n.bincount(o,s,i)];case"DenseBincount":{let a=C("x",r,t,e),u=C("weights",r,t,e),l=C("size",r,t,e),c=C("binaryOutput",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var dF=(r,t,e,n=ie)=>{switch(r.op){case"ConcatV2":case"Concat":{let o=C("n",r,t,e),s=C("axis",r,t,e),i=C("tensors",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case"Gather":{let o=C("x",r,t,e),s=C("indices",r,t,e);return[n.gather(o,n.cast(s,"int32"),0)]}case"GatherV2":{let o=C("axis",r,t,e),s=C("batchDims",r,t,e),i=C("x",r,t,e),a=C("indices",r,t,e);return[n.gather(i,n.cast(a,"int32"),o,s)]}case"Reverse":{let o=C("dims",r,t,e),s=[];for(let a=0;a{let o=C("axis",r,t,e),s=C("tensors",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error("the input tensors shape does not match");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case"Unpack":{let o=C("axis",r,t,e),s=C("tensor",r,t,e);return n.unstack(s,o)}case"Tile":{let o=C("reps",r,t,e);return[n.tile(C("x",r,t,e),o)]}case"Split":case"SplitV":{let o=C("axis",r,t,e),s=C("numOrSizeSplits",r,t,e),i=C("x",r,t,e);return n.split(i,s,o)}case"ScatterNd":{let o=C("indices",r,t,e),s=C("values",r,t,e),i=C("shape",r,t,e);return[n.scatterND(o,s,i)]}case"GatherNd":{let o=C("x",r,t,e),s=C("indices",r,t,e);return[n.gatherND(o,s)]}case"SparseToDense":{let o=C("sparseIndices",r,t,e),s=C("outputShape",r,t,e),i=C("sparseValues",r,t,e),a=C("defaultValue",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case"TensorScatterUpdate":{let o=C("indices",r,t,e),s=C("values",r,t,e),i=C("tensor",r,t,e);return[n.tensorScatterUpdate(i,o,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var hF=(r,t,e,n=ie)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(C("indices",r,t,e),C("values",r,t,e),C("denseShape",r,t,e),C("defaultValue",r,t,e));return[o,s,i,a]}case"SparseReshape":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(C("inputIndices",r,t,e),C("inputShape",r,t,e),C("newShape",r,t,e));return[o,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(C("data",r,t,e),C("indices",r,t,e),C("segmentIds",r,t,e))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(C("data",r,t,e),C("indices",r,t,e),C("segmentIds",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var gF=(r,t,e,n=ie)=>{switch(r.op){case"FFT":return[n.fft(C("x",r,t,e))];case"IFFT":return[n.ifft(C("x",r,t,e))];case"RFFT":return[n.rfft(C("x",r,t,e))];case"IRFFT":return[n.irfft(C("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var xF=(r,t,e,n=ie)=>{switch(r.op){case"StaticRegexReplace":return[n.string.staticRegexReplace(C("input",r,t,e),C("pattern",r,t,e),C("rewrite",r,t,e),C("replaceGlobal",r,t,e))];case"StringNGrams":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(C("data",r,t,e),C("dataSplits",r,t,e),C("separator",r,t,e),C("nGramWidths",r,t,e),C("leftPad",r,t,e),C("rightPad",r,t,e),C("padWidth",r,t,e),C("preserveShortSequences",r,t,e));return[o,s]}case"StringSplit":{let{indices:o,values:s,shape:i}=n.string.stringSplit(C("input",r,t,e),C("delimiter",r,t,e),C("skipEmpty",r,t,e));return[o,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(C("input",r,t,e),C("numBuckets",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var yF=(r,t,e,n=ie)=>{switch(r.op){case"Cast":return[n.cast(C("x",r,t,e),C("dtype",r,t,e))];case"ExpandDims":{let o=C("axis",r,t,e);return[n.expandDims(C("x",r,t,e),o)]}case"Squeeze":{let o=C("axis",r,t,e);return[n.squeeze(C("x",r,t,e),o)]}case"Reshape":return[n.reshape(C("x",r,t,e),C("shape",r,t,e))];case"EnsureShape":return[n.ensureShape(C("x",r,t,e),C("shape",r,t,e))];case"MirrorPad":return[n.mirrorPad(C("x",r,t,e),C("padding",r,t,e),C("mode",r,t,e))];case"PadV2":case"Pad":return[n.pad(C("x",r,t,e),C("padding",r,t,e),C("constantValue",r,t,e))];case"SpaceToBatchND":{let o=C("blockShape",r,t,e),s=C("paddings",r,t,e);return[n.spaceToBatchND(C("x",r,t,e),o,s)]}case"BatchToSpaceND":{let o=C("blockShape",r,t,e),s=C("crops",r,t,e);return[n.batchToSpaceND(C("x",r,t,e),o,s)]}case"DepthToSpace":{let o=C("blockSize",r,t,e),s=C("dataFormat",r,t,e).toUpperCase();return[n.depthToSpace(C("x",r,t,e),o,s)]}case"BroadcastTo":return[n.broadcastTo(C("x",r,t,e),C("shape",r,t,e))];case"BroadcastArgs":return[n.broadcastArgs(C("s0",r,t,e),C("s1",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function yk(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case"arithmetic":return o(()=>KR(i,a,u));case"basic_math":return o(()=>jR(i,a,u));case"control":return tF(i,a,u);case"convolution":return o(()=>rF(i,a,u));case"creation":return o(()=>nF(i,a,u));case"dynamic":return oF(i,a,u);case"evaluation":return o(()=>sF(i,a,u));case"image":return o(()=>lF(i,a,u));case"graph":return o(()=>iF(i,a,u));case"logical":return o(()=>uF(i,a,u));case"matrices":return o(()=>cF(i,a,u));case"normalization":return o(()=>pF(i,a,u));case"ragged":return o(()=>mF(i,a,u));case"reduction":return o(()=>fF(i,a,u));case"slice_join":return o(()=>dF(i,a,u));case"sparse":return o(()=>hF(i,a,u));case"spectral":return o(()=>gF(i,a,u));case"string":return o(()=>xF(i,a,u));case"transformation":return o(()=>yF(i,a,u));case"hash_table":return aF(i,a,u,n);case"custom":let l=zb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Zb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Kh=class{constructor(t={},e={},n={},o={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),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 t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function bk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>Cn(m)[0]));n=n||[];let c=new Set(n.map(m=>Cn(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Gu(m)||vQ(m)||SQ(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&!l.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function bF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>Cn(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],i=g=>e.has(typeof g=="string"?g:g.name);function a(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let u=a([...o,...r.weights,...s]).filter(i),l=a([...u,...Object.values(r.nodes)]).filter(i),c=new Map(l.map(g=>[g.name,g])),p={};for(let g of l){p[g.name]=p[g.name]||0;for(let x of g.children)i(x)||(p[x.name]=Number.POSITIVE_INFINITY),p[x.name]=(p[x.name]||0)+1}let m=Object.entries(p).filter(([,g])=>g===0).map(([g])=>g),f=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(i))--p[b.name]===0&&(f.push(b.name),m.push(b.name))}let d=f.map(g=>c.get(g)),h=yQ(d,u);return bQ(h,u),h}function yQ(r,t){let e=new Map(r.map(i=>[i.name,i])),n=t.map(i=>i.name),o=new Set(n);for(;n.length>0;){let i=n.pop(),a=e.get(i);for(let u of a.children)!e.has(u.name)||o.has(u.name)||(o.add(u.name),n.push(u.name))}return r.filter(i=>o.has(i.name))}var id=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function bQ(r,t){let e=new Map(r.map((a,u)=>[a.name,u])),n=new Set(t.map(a=>a.name)),o=a=>n.has(typeof a=="string"?a:a.name),s=new Set(r.map(a=>a.name)),i=a=>s.has(typeof a=="string"?a:a.name);for(let a of r){for(let u of a.children.filter(i)){if(!e.has(u.name))throw new id(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new id(`Node ${a.name} is scheduled to run after its child ${u.name}.`)}if(!o(a))for(let u of a.inputs){if(!e.has(u.name))throw new id(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new id(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function wF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Gu(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;at[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new Qc(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+"--"+o.join(this.SEPARATOR)}compile(t,e){let n=bk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let l=e.map(p=>p.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${c}]. Missing the following inputs: [${o}]`)}let a=bF(this.graph,n),u=wF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return $e(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[Cn(m)[0]]),s=e.map(m=>Cn(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Kh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=Cn(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=yk(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. 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c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=xi(a.name,n);s[u]||!i.has(a.name)||(a.op==="Merge"?a.inputNames.some(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=Cn(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=Cn(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=Cn(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var ew=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in 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t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof Pt?[t]:t,n={};return e.forEach((o,s)=>n[this.structuredOutputKeys[s]]=o),n}return t}predict(t,e){let n=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(t,e){let n=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(t){var e;if(!(t instanceof Pt)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let i in s){let a=s[i];a.resourceId!=null&&(t[i]=this.resourceIdToCapturedInput[a.resourceId])}return t}t=Array.isArray(t)?t:[t];let n=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${t.length} input tensors provided.`);let o=0;return this.inputNodes.reduce((s,i)=>{var a,u,l;let 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this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}},Sk=class extends tr{constructor(t){super(),this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Nk=class extends tr{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Tk=class extends tr{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},_k=class extends tr{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},Ek=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Co.getTensorsInContainer(t.value),n=this.transform(t.value),o=Co.getTensorsInContainer(n);for(let s of e)Co.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Ak=class extends tr{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},ow=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Co.getTensorsInContainer(t.value),n=await this.transform(t.value),o=Co.getTensorsInContainer(n);for(let s of e)Co.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},ep=class extends tr{constructor(){super(),this.outputQueue=new tp,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}}},Dk=class extends ep{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=Co.getTensorsInContainer(t.value),n=this.transform(t.value),o=Co.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)Co.isTensorInList(s,o)||s.dispose();return!0}},sw=class extends tr{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(t){if(await t,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 e=await this.iterator.next();return e.done?(this.iterator=null,this.readFromChain(t)):e}},Pl;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Pl||(Pl={}));var $k=class extends tr{constructor(t,e=Pl.FAIL){super(),this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(t){await t;let e=0,n=0;function o(i){return i instanceof tr?{value:i.next().then(u=>(e++,u.done&&n++,u.value)),recurse:!1}:{value:null,recurse:!0}}let s=await nw(this.iterators,o);if(e===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Pl.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Pl.SHORTEST:return{value:null,done:!0};case Pl.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},iw=class extends tr{constructor(t,e){super(),this.upstream=t,this.bufferSize=e,this.buffer=new ad(e)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let t=this.upstream.next();this.buffer.push(t)}}next(){return this.refill(),this.buffer.shift()}},Rk=class extends iw{constructor(t,e,n){super(t,e),this.upstream=t,this.windowSize=e,this.upstreamExhausted=!1,this.random=TF.alea(n||y.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(t){return Math.floor(this.random()*t)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let t=this.chooseIndex(),e=await this.buffer.shuffleExcise(t);if(e.done)this.upstreamExhausted=!0;else return this.refill(),e}return{value:null,done:!0}}};var bi=class{constructor(){this.size=null}batch(t,e=!0){let n=this;y.assert(t>0,()=>`batchSize needs to be positive, but it is + ${t}`);let o;return this.size===1/0||this.size==null?o=this.size:e?o=Math.ceil(this.size/t):o=Math.floor(this.size/t),Dn(async()=>(await n.iterator()).columnMajorBatch(t,e,$Q),o)}concatenate(t){let e=this,n;return this.size===1/0||t.size===1/0?n=1/0:this.size!=null&&t.size!=null?n=this.size+t.size:n=null,Dn(async()=>(await e.iterator()).concatenate(await t.iterator()),n)}filter(t){let e=this,n;return this.size===1/0?n=1/0:n=null,Dn(async()=>(await e.iterator()).filter(o=>B(()=>t(o))),n)}async forEachAsync(t){return(await this.iterator()).forEachAsync(t)}map(t){let e=this;return Dn(async()=>(await e.iterator()).map(n=>B(()=>t(n))),this.size)}mapAsync(t){let e=this;return Dn(async()=>(await e.iterator()).mapAsync(t),this.size)}prefetch(t){if(t==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let e=this;return Dn(async()=>(await e.iterator()).prefetch(t),this.size)}repeat(t){let e=this,n;return this.size!=null&&t>0?n=this.size*t:t===0?n=0:this.size!=null&&(t===void 0||t<0)?n=1/0:n=null,Dn(async()=>{let o=Xh(async()=>({value:await e.iterator(),done:!1}));return _F(o.take(t))},n)}skip(t){let e=this,n;return this.size!=null&&t>=0&&this.size>=t?n=this.size-t:this.size!=null&&(this.size(await e.iterator()).skip(t),n)}shuffle(t,e,n=!0){if(t==null||t<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 o=this,s=AF.alea(e||y.now().toString());return Dn(async()=>{let i=s.int32();return n&&(i+=s.int32()),(await o.iterator()).shuffle(t,i.toString())},this.size)}take(t){let e=this,n;return this.size!=null&&this.size>t?n=t:this.size!=null&&this.size<=t?n=this.size:n=null,Dn(async()=>(await e.iterator()).take(t),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};bi.MAX_BUFFER_SIZE=1e4;function Dn(r,t=null){return new class extends bi{constructor(){super(...arguments),this.size=t}async iterator(){return r()}}}function DF(r){return Dn(async()=>Fk(r),r.length)}function $F(r){if(!Wu(r))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(r))for(let e=0;e{let e=await nw(r,n=>{if(n instanceof bi)return{value:n.iterator(),recurse:!1};if(Wu(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return EF(e,Pl.SHORTEST)},t)}function $Q(r){if(r===null)return null;let t=r[0];return NF(t)?{value:RQ(r),recurse:!1}:{value:null,recurse:!0}}function RQ(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Pt?qe(r):sr(r)}var ld=class extends bi{constructor(t){super(),this.input=t}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` +`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o))}};var aw='"',Yh=Symbol("out"),RF=Symbol("field"),lw=Symbol("quote"),Ok=Symbol("quoteafterquote"),FF=Symbol("quoteinquote"),ud=class extends bi{async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let t=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!t)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&t&&y.assert(t.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 ("+t.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=t);let e=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),n=Object.keys(e).filter(o=>e[o]>1);if(y.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let o of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(o)===-1)throw new Error('The key "'+o+'" 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 n=e.value;return this.parseRow(n,!1)}else return null}constructor(t,e){super(),this.input=t,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new ld(t),e||(e={}),this.hasHeader=e.hasHeader!==!1,this.fullColumnNames=e.columnNames,this.columnConfigs=e.columnConfigs,this.configuredColumnsOnly=e.configuredColumnsOnly,e.delimWhitespace?(y.assert(e.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=e.delimiter?e.delimiter:","}async iterator(){this.columnNamesValidated||await this.setColumnNames();let t=await this.base.iterator();return this.hasHeader&&(t=t.skip(1)),t.map(e=>this.makeDataElement(e))}makeDataElement(t){let e=this.parseRow(t),n={},o={};for(let s=0;s14||!Number.isInteger(e))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=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.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(t={}){if(!L().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let e=new cd(t);return await e.start(),e}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 t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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 e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.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 t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},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(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),sr(n,e)}};var pd=class extends tr{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ke([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=ui([i,s,u,a],[1,4])}else this.cropBox=ui([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(!L().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new pd(t,e);return await n.start(),n}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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=Ay.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=ar(Q(t,"float32"),0),n;n=dn.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var md=class{};var Zh=class extends tr{split(t){return new Pk(this,t)}},Pk=class extends Zh{constructor(t,e){super(),this.upstream=t,this.impl=new Mk(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Mk=class extends ep{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var uw=class extends tr{decodeUTF8(){return new Lk(this)}},Lk=class extends Zh{constructor(t){super(),this.upstream=t,this.impl=new zk(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},zk=class extends ep{constructor(t){if(super(),this.upstream=t,L().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=wk();this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let n;return L().get("IS_BROWSER")?n=this.decoder.decode(e,{stream:!0}):n=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(n),!0}};var fd=class extends uw{constructor(t,e={}){super(),this.file=t,this.options=e,y.assert(t instanceof Uint8Array||(L().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.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,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=a=>{let u=s.result;if(u instanceof 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c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,I=c.effectiveFilterDepth,N=c.effectiveFilterHeight,E=c.effectiveFilterWidth,A=I-1-c.padInfo.front,D=E-1-c.padInfo.left,F=N-1-c.padInfo.top,P=bt(s.shape,"float32"),V=1/(d*h*g),G=e.bufferSync(o);for(let W=0;W=c.outDepth||Math.floor(ot)!==ot))for(let it=0;it=c.outHeight||Math.floor(mt)!==mt))for(let gt=0;gt=c.outWidth||Math.floor(It)!==It)continue;let Rt=G.get(W,ot,mt,It,q);st+=Rt}}}P.set(st*V,W,H,K,X,q)}return e.makeTensorInfo(P.shape,P.dtype,P.values)}var LO={kernelName:Xl,backendName:"cpu",kernelFunc:utt};function ctt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;tt([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,I=x-1-c.padInfo.top,N=bt(i.shape,"float32"),E=1/(f*d),A=e.data.get(o.dataId).values,D=bt(o.shape,"float32",A);for(let F=0;F=c.outHeight||Math.floor(X)!==X))for(let Z=0;Z=c.outWidth||Math.floor(et)!==et)continue;let nt=D.get(F,X,et,P);H+=nt}}N.set(H*E,F,V,G,P)}return e.makeTensorInfo(N.shape,N.dtype,N.values)}var zO={kernelName:Fp,backendName:"cpu",kernelFunc:ctt};function ptt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;y.assert(a.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(s==null||a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),tt([o,a,u,s,i],"batchNorm");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=e.data.get(o.dataId).values,p=e.data.get(a.dataId).values,m=e.data.get(u.dataId).values,f=s?e.data.get(s.dataId).values:new Float32Array([1]),d=i?e.data.get(i.dataId).values:new Float32Array([0]),h=new Float32Array(c.length),g=d.length,x=f.length,b=m.length,w=p.length,I=0,N=0,E=0,A=0;for(let D=0;D=g&&(I=0),N>=w&&(N=0),E>=x&&(E=0),A>=b&&(A=0);return e.makeTensorInfo(o.shape,o.dtype,h)}var BO={kernelName:hs,backendName:"cpu",kernelFunc:ptt};function mtt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;tt([o],"batchToSpaceND");let a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=Jt({inputs:{x:o},backend:e,attrs:{shape:u}}),d=Ge({inputs:{x:f},backend:e,attrs:{perm:l}}),h=Jt({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Mo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var VO={kernelName:Di,backendName:"cpu",kernelFunc:mtt};function ftt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=xd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var GO={kernelName:Da,backendName:"cpu",kernelFunc:ftt};function dtt(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var WO={kernelName:Yl,backendName:"cpu",kernelFunc:dtt};var htt=At(ho,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Yr({inputs:{x:u[0]},backend:e});if(u[0].dtype==="complex64"){let h=u.map(I=>Fo({inputs:{input:I},backend:e})),g=u.map(I=>ba({inputs:{input:I},backend:e})),x=Uu({inputs:h,backend:e,attrs:{axis:s}}),b=Uu({inputs:g,backend:e,attrs:{axis:s}}),w=Cr({inputs:{real:x,imag:b},backend:e});return h.forEach(I=>e.disposeIntermediateTensorInfo(I)),g.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Jt({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=rp(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var KO={kernelName:$i,backendName:"cpu",kernelFunc:Uu};function fT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],"conv2d");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",I=new le(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],D=w?N[1]:N[2],F=w?N[2]:1,P=w?1:N[1],V=I.strides[0],G=w?I.strides[1]:I.strides[2],W=w?I.strides[2]:1,q=w?1:I.strides[1],H=e.data.get(o.dataId).values,K=e.data.get(s.dataId).values,X=I.values;for(let Z=0;Z=m.inHeight)continue;let gt=it*E[0],It=et+mt*D;for(let Rt=0;Rt=m.inWidth)continue;let ge=gt+qt*E[1],ee=It+ce*F,xe=ge;for(let fe=0;fe=l.inDepth)continue;let Z=K*F[0],et=V+X*D[1];for(let nt=0;nt=l.inHeight)continue;let mt=Z+ot*F[1],gt=et+it*D[2];for(let It=0;It=l.inWidth)continue;let ce=mt+Ht*F[2],ge=gt+qt*l.inChannels,ee=ce;for(let xe=0;xeMath.cos(r)),tP={kernelName:os,backendName:"cpu",kernelFunc:Ctt};var vtt=At(ss,r=>Math.cosh(r)),eP={kernelName:ss,backendName:"cpu",kernelFunc:vtt};function Stt(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=bt([d,h,g,f],"float32"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,I=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-P)*(m-1)/(g-1):0;for(let K=0;K1?F*(p-1)+K*q:.5*(F+V)*(p-1);if(X<0||X>p-1){for(let Z=0;Z1?P*(m-1)+st*H:.5*(P+G)*(m-1);if(at<0||at>m-1){for(let gt=0;gt1?P*(m-1)+Z*H:.5*(P+G)*(m-1);if(et<0||et>m-1){for(let at=0;atx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=S.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,w=b.left,I=b.top,N=f.outChannels/f.inChannels,E=new le(f.outShape,o.dtype),A=e.data.get(o.dataId).values,D=e.data.get(s.dataId).values,F=E.values;for(let P=0;P=f.inHeight)continue;let Z=K*p[0],et=V+X*c[1];for(let nt=0;nt=f.inWidth)continue;let mt=Z+ot*p[1],gt=et+it*f.inChannels,It=st,Rt=mt;for(let Dt=0;Dt{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:I,strideWidth:N,filterHeight:E,filterWidth:A,dilationHeight:D,dilationWidth:F,outShape:P}=S.computeDilation2DInfo(n.shape,o.shape,s,i,"NHWC",a),V=y.sizeFromShape(P),G=P.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H=0&&it=0&>st&&(st=Dt)}}}let at=y.locToIndex([H,K,Z,nt],G,y.computeStrides(P));W[at]=st}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),P,n.dtype),shape:P,dtype:n.dtype}}};var mP={kernelName:eu,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${eu}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W=0&&ot=0&&mtet&&(et=gt,nt=at,st=it)}}}V[nt][st][Z]+=P[W][q][K][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var fP={kernelName:tu,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${tu}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W=0&&ot=0&&mtet&&(et=gt,nt=ot,st=mt)}}}V[W][nt][st][Z]+=P[W][q][K][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function Ml(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"sum");let a;o.dtype==="bool"?a=Oo({inputs:{x:o},backend:e,attrs:{dtype:"int32"}}):a=Yr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=S.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=Ge({inputs:{x:a},backend:e,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,u)),S.assertAxesAreInnerMostDims("sum",p,m.shape.length);let[f,d]=S.computeOutAndReduceShapes(m.shape,p),h=S.upcastType(m.dtype,"int32"),g=hd(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let I=0;I=0&&(m=Ml({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var hP={kernelName:zp,backendName:"cpu",kernelFunc:$tt};function Rtt(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],"eluGrad");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=0?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,"float32",s)}var gP={kernelName:Ma,backendName:"cpu",kernelFunc:Rtt};var Ftt=S.ERF_P,Ott=S.ERF_A1,Ptt=S.ERF_A2,Mtt=S.ERF_A3,Ltt=S.ERF_A4,ztt=S.ERF_A5,Btt=At(La,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+Ftt*e);return t*(1-((((ztt*n+Ltt)*n+Mtt)*n+Ptt)*n+Ott)*n*Math.exp(-e*e))}),xP={kernelName:La,backendName:"cpu",kernelFunc:Btt};function Cd(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),Jt({inputs:{x:o},backend:e,attrs:{shape:a}})}var yP={kernelName:Ri,backendName:"cpu",kernelFunc:Cd};var Vtt=te((r,t)=>r/t),Zh=ie(us,Vtt),Jh={kernelName:us,backendName:"cpu",kernelFunc:Zh};function Cw(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType("float32",c),m=y.getTypedArrayFromDType("float32",c);for(let g=0;g{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m=0&&w=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=S.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=Jt({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=Jt({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=lw(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var NP={kernelName:Fi,backendName:"cpu",kernelFunc:Ytt};function Ztt(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=Jt({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=Cw(a,!0,e),l=Jt({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var kP={kernelName:Vp,backendName:"cpu",kernelFunc:Ztt};var Jtt=At(xs,r=>Number.isFinite(r)?1:0,"bool"),TP={kernelName:xs,backendName:"cpu",kernelFunc:Jtt};var Qtt=At(ys,r=>Math.abs(r)===1/0?1:0,"bool"),_P={kernelName:ys,backendName:"cpu",kernelFunc:Qtt};var tet=At(bs,r=>Number.isNaN(r)?1:0,"bool"),EP={kernelName:bs,backendName:"cpu",kernelFunc:tet};function eet(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=uw(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var AP={kernelName:Ha,backendName:"cpu",kernelFunc:eet};var ret=At(Cs,r=>Math.log1p(r)),DP={kernelName:Cs,backendName:"cpu",kernelFunc:ret};var net=te((r,t)=>r&&t),oet=ie(qa,net,null,"bool"),$P={kernelName:qa,backendName:"cpu",kernelFunc:oet};var set=At(Ka,r=>r?0:1,"bool"),RP={kernelName:Ka,backendName:"cpu",kernelFunc:set};var iet=te((r,t)=>r||t),aet=ie(ja,iet,null,"bool"),FP={kernelName:ja,backendName:"cpu",kernelFunc:aet};function uet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;tt(o,"LRN");let l=o.shape[3],c=l-1,p=e.data.get(o.dataId).values,m=y.sizeFromShape(o.shape),f=new Float32Array(m);function d(h){let g=h%l,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),w=0;for(;x<=b;x++){let I=p[x];w+=I*I}return w}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. 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Att(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;tt([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,I=x-1-c.padInfo.top,N=bt(i.shape,"float32"),E=1/(f*d),A=e.data.get(o.dataId).values,D=bt(o.shape,"float32",A);for(let F=0;F=c.outHeight||Math.floor(X)!==X))for(let Z=0;Z=c.outWidth||Math.floor(et)!==et)continue;let nt=D.get(F,X,et,P);H+=nt}}N.set(H*E,F,V,G,P)}return e.makeTensorInfo(N.shape,N.dtype,N.values)}var JO={kernelName:Fp,backendName:"cpu",kernelFunc:Att};function Dtt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;y.assert(a.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization 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a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=Qt({inputs:{x:o},backend:e,attrs:{shape:u}}),d=Ge({inputs:{x:f},backend:e,attrs:{perm:l}}),h=Qt({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Lo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var tP={kernelName:$i,backendName:"cpu",kernelFunc:$tt};function Rtt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=yd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var eP={kernelName:Da,backendName:"cpu",kernelFunc:Rtt};function Ftt(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var rP={kernelName:Yl,backendName:"cpu",kernelFunc:Ftt};var Ott=At(go,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Yr({inputs:{x:u[0]},backend:e});if(u[0].dtype==="complex64"){let h=u.map(I=>Oo({inputs:{input:I},backend:e})),g=u.map(I=>ba({inputs:{input:I},backend:e})),x=Hu({inputs:h,backend:e,attrs:{axis:s}}),b=Hu({inputs:g,backend:e,attrs:{axis:s}}),w=Cr({inputs:{real:x,imag:b},backend:e});return h.forEach(I=>e.disposeIntermediateTensorInfo(I)),g.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Qt({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=rp(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var iP={kernelName:Ri,backendName:"cpu",kernelFunc:Hu};function IT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],"conv2d");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",I=new le(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],D=w?N[1]:N[2],F=w?N[2]:1,P=w?1:N[1],V=I.strides[0],G=w?I.strides[1]:I.strides[2],W=w?I.strides[2]:1,q=w?1:I.strides[1],H=e.data.get(o.dataId).values,K=e.data.get(s.dataId).values,X=I.values;for(let Z=0;Z=m.inHeight)continue;let gt=it*E[0],It=et+mt*D;for(let Rt=0;Rt=m.inWidth)continue;let ge=gt+qt*E[1],ee=It+ce*F,xe=ge;for(let fe=0;fe=l.inDepth)continue;let Z=K*F[0],et=V+X*D[1];for(let nt=0;nt=l.inHeight)continue;let mt=Z+ot*F[1],gt=et+it*D[2];for(let It=0;It=l.inWidth)continue;let ce=mt+Ht*F[2],ge=gt+qt*l.inChannels,ee=ce;for(let xe=0;xeMath.cos(r)),fP={kernelName:ss,backendName:"cpu",kernelFunc:Gtt};var Wtt=At(is,r=>Math.cosh(r)),dP={kernelName:is,backendName:"cpu",kernelFunc:Wtt};function Utt(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=bt([d,h,g,f],"float32"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,I=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-P)*(m-1)/(g-1):0;for(let K=0;K1?F*(p-1)+K*q:.5*(F+V)*(p-1);if(X<0||X>p-1){for(let Z=0;Z1?P*(m-1)+st*H:.5*(P+G)*(m-1);if(at<0||at>m-1){for(let gt=0;gt1?P*(m-1)+Z*H:.5*(P+G)*(m-1);if(et<0||et>m-1){for(let at=0;atx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=S.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,w=b.left,I=b.top,N=f.outChannels/f.inChannels,E=new le(f.outShape,o.dtype),A=e.data.get(o.dataId).values,D=e.data.get(s.dataId).values,F=E.values;for(let P=0;P=f.inHeight)continue;let Z=K*p[0],et=V+X*c[1];for(let nt=0;nt=f.inWidth)continue;let mt=Z+ot*p[1],gt=et+it*f.inChannels,It=st,Rt=mt;for(let Dt=0;Dt{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:I,strideWidth:N,filterHeight:E,filterWidth:A,dilationHeight:D,dilationWidth:F,outShape:P}=S.computeDilation2DInfo(n.shape,o.shape,s,i,"NHWC",a),V=y.sizeFromShape(P),G=P.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H=0&&it=0&>st&&(st=Dt)}}}let at=y.locToIndex([H,K,Z,nt],G,y.computeStrides(P));W[at]=st}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),P,n.dtype),shape:P,dtype:n.dtype}}};var NP={kernelName:eu,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${eu}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W=0&&ot=0&&mtet&&(et=gt,nt=at,st=it)}}}V[nt][st][Z]+=P[W][q][K][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var kP={kernelName:tu,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${tu}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W=0&&ot=0&&mtet&&(et=gt,nt=ot,st=mt)}}}V[W][nt][st][Z]+=P[W][q][K][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function Ml(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"sum");let a;o.dtype==="bool"?a=Po({inputs:{x:o},backend:e,attrs:{dtype:"int32"}}):a=Yr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=S.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=Ge({inputs:{x:a},backend:e,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,u)),S.assertAxesAreInnerMostDims("sum",p,m.shape.length);let[f,d]=S.computeOutAndReduceShapes(m.shape,p),h=S.upcastType(m.dtype,"int32"),g=gd(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let I=0;I=0&&(m=Ml({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var _P={kernelName:zp,backendName:"cpu",kernelFunc:Jtt};function Qtt(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],"eluGrad");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=0?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,"float32",s)}var EP={kernelName:Ma,backendName:"cpu",kernelFunc:Qtt};var tet=S.ERF_P,eet=S.ERF_A1,ret=S.ERF_A2,net=S.ERF_A3,oet=S.ERF_A4,set=S.ERF_A5,iet=At(La,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+tet*e);return t*(1-((((set*n+oet)*n+net)*n+ret)*n+eet)*n*Math.exp(-e*e))}),AP={kernelName:La,backendName:"cpu",kernelFunc:iet};function vd(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),Qt({inputs:{x:o},backend:e,attrs:{shape:a}})}var DP={kernelName:Fi,backendName:"cpu",kernelFunc:vd};var aet=Jt((r,t)=>r/t),eg=ne(cs,aet),rg={kernelName:cs,backendName:"cpu",kernelFunc:eg};function _w(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType("float32",c),m=y.getTypedArrayFromDType("float32",c);for(let g=0;g{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m=0&&w=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=S.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=Qt({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=Qt({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=dw(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var LP={kernelName:Oi,backendName:"cpu",kernelFunc:xet};function yet(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=Qt({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=_w(a,!0,e),l=Qt({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var zP={kernelName:Vp,backendName:"cpu",kernelFunc:yet};var bet=At(ys,r=>Number.isFinite(r)?1:0,"bool"),BP={kernelName:ys,backendName:"cpu",kernelFunc:bet};var wet=At(bs,r=>Math.abs(r)===1/0?1:0,"bool"),VP={kernelName:bs,backendName:"cpu",kernelFunc:wet};var Iet=At(ws,r=>Number.isNaN(r)?1:0,"bool"),GP={kernelName:ws,backendName:"cpu",kernelFunc:Iet};function Cet(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=hw(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var WP={kernelName:Ha,backendName:"cpu",kernelFunc:Cet};var vet=At(vs,r=>Math.log1p(r)),UP={kernelName:vs,backendName:"cpu",kernelFunc:vet};var Net=Jt((r,t)=>r&&t),ket=ne(qa,Net,null,"bool"),HP={kernelName:qa,backendName:"cpu",kernelFunc:ket};var Tet=At(Ka,r=>r?0:1,"bool"),qP={kernelName:Ka,backendName:"cpu",kernelFunc:Tet};var _et=Jt((r,t)=>r||t),Eet=ne(ja,_et,null,"bool"),KP={kernelName:ja,backendName:"cpu",kernelFunc:Eet};function Aet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;tt(o,"LRN");let l=o.shape[3],c=l-1,p=e.data.get(o.dataId).values,m=y.sizeFromShape(o.shape),f=new Float32Array(m);function d(h){let g=h%l,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),w=0;for(;x<=b;x++){let I=p[x];w+=I*I}return w}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. 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To speed things up dram ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function Ii(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join("")}function fp(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join("")}function Grt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function nL(r,t,e="index"){let n=r.map((s,i)=>i),o=Grt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join("")}function Td(r){let t=y.computeStrides(r).map(e=>e.toString());return` + `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function Ci(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join("")}function fp(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join("")}function lnt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function gL(r,t,e="index"){let n=r.map((s,i)=>i),o=lnt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join("")}function _d(r){let t=y.computeStrides(r).map(e=>e.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; } -`}function _d(){return` +`}function Ed(){return` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } -`}var $w=` +`}var Lw=` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -155,22 +159,22 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return c / 255.0; } -`;var{getBroadcastDims:oL}=S;function sL(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:""};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=Rw(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:""};`)});let o=n.join(` -`),s=r.map(f=>Wrt(f,t,e.packedInputs,e.enableShapeUniforms)).join(` -`),i=t.texShape,a=We(),u=qrt(a),l,c,p=Xrt(a);return t.isPacked?(l=Urt(t.logicalShape,i,e.enableShapeUniforms),c=jrt(a)):(l=Hrt(t.logicalShape,i,e.enableShapeUniforms),c=Krt(a)),e.packedInputs&&(p+=Qrt),[p,u,c,o,l,s,e.userCode].join(` -`)}function Ad(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return pnt(r,t);case 1:return fnt(r,t);case 2:return hnt(r,t);case 3:return xnt(r,t);case 4:return bnt(r,t);case 5:return wnt(r);case 6:return Int(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function iL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return cnt(r);case 1:return mnt(r,t);case 2:return dnt(r,t);case 3:return gnt(r,t);default:return ynt(r,t)}}function Wrt(r,t,e=!1,n){let o="";e?o+=iL(r,n):o+=Ad(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=Cnt(r,t):o+=vnt(r,t)),o}function Urt(r,t,e){switch(r.length){case 0:return aL();case 1:return tnt(r,t,e);case 2:return lnt(r,t,e);case 3:return rnt(r,t,e);default:return ont(r,t,e)}}function Hrt(r,t,e){switch(r.length){case 0:return aL();case 1:return ent(r,t,e);case 2:return unt(r,t,e);case 3:return nnt(r,t,e);case 4:return snt(r,t,e);case 5:return int(r,t);case 6:return ant(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function qrt(r){return` +`;var{getBroadcastDims:xL}=S;function yL(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:""};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=zw(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:""};`)});let o=n.join(` +`),s=r.map(f=>unt(f,t,e.packedInputs,e.enableShapeUniforms)).join(` +`),i=t.texShape,a=We(),u=mnt(a),l,c,p=hnt(a);return t.isPacked?(l=cnt(t.logicalShape,i,e.enableShapeUniforms),c=dnt(a)):(l=pnt(t.logicalShape,i,e.enableShapeUniforms),c=fnt(a)),e.packedInputs&&(p+=bnt),[p,u,c,o,l,s,e.userCode].join(` +`)}function Dd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return Dnt(r,t);case 1:return Rnt(r,t);case 2:return Ont(r,t);case 3:return Mnt(r,t);case 4:return znt(r,t);case 5:return Bnt(r);case 6:return Vnt(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function bL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return Ant(r);case 1:return $nt(r,t);case 2:return Fnt(r,t);case 3:return Pnt(r,t);default:return Lnt(r,t)}}function unt(r,t,e=!1,n){let o="";e?o+=bL(r,n):o+=Dd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=Gnt(r,t):o+=Wnt(r,t)),o}function cnt(r,t,e){switch(r.length){case 0:return wL();case 1:return wnt(r,t,e);case 2:return _nt(r,t,e);case 3:return Cnt(r,t,e);default:return Snt(r,t,e)}}function pnt(r,t,e){switch(r.length){case 0:return wL();case 1:return Int(r,t,e);case 2:return Ent(r,t,e);case 3:return vnt(r,t,e);case 4:return Nnt(r,t,e);case 5:return knt(r,t);case 6:return Tnt(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function mnt(r){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${r.texture2D}(textureSampler, uv).r; } - `}function Krt(r){return` + `}function fnt(r){return` void setOutput(float val) { ${r.output} = vec4(val, 0, 0, 0); } - `}function jrt(r){return` + `}function dnt(r){return` void setOutput(vec4 val) { ${r.output} = val; } - `}function Xrt(r){return`${r.version} + `}function hnt(r){return`${r.version} precision highp float; precision highp int; precision highp sampler2D; @@ -225,10 +229,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return fract((p3.x + p3.y) * p3.z); } - ${Yrt} - ${Zrt} - ${Jrt} - `}var Yrt=` + ${gnt} + ${xnt} + ${ynt} + `}var gnt=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -240,7 +244,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,Zrt=` +`,xnt=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -248,7 +252,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,Jrt=` +`,ynt=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -257,7 +261,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,Qrt=` +`,bnt=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -268,11 +272,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function aL(){return` +`;function wL(){return` int getOutputCoords() { return 0; } - `}function tnt(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?` + `}function wnt(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } @@ -301,7 +305,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${n[0]}, ${n[1]})); return 2 * (resTexRC.x * ${n[1]} + resTexRC.y); } - `}function ent(r,t,e){return t[0]===1?e?` + `}function Int(r,t,e){return t[0]===1?e?` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } @@ -329,7 +333,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${t[0]}, ${t[1]})); return resTexRC.x * ${t[1]} + resTexRC.y; } - `}function rnt(r,t,e){if(e)return` + `}function Cnt(r,t,e){if(e)return` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); @@ -360,7 +364,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `}function nnt(r,t,e){if(e)return` + `}function vnt(r,t,e){if(e)return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -368,7 +372,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${fp(["r","c","d"],r)} return ivec3(r, c, d); } -`;let n=Ii(["r","c","d"],r);return` +`;let n=Ci(["r","c","d"],r);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -376,7 +380,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n} return ivec3(r, c, d); } - `}function ont(r,t,e){if(e)return` + `}function Snt(r,t,e){if(e)return` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -417,7 +421,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec${r.length}(${u}); } - `}function snt(r,t,e){if(e)return` + `}function Nnt(r,t,e){if(e)return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -425,7 +429,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${fp(["r","c","d","d2"],r)} return ivec4(r, c, d, d2); } - `;let n=Ii(["r","c","d","d2"],r);return` + `;let n=Ci(["r","c","d","d2"],r);return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -433,7 +437,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n} return ivec4(r, c, d, d2); } - `}function int(r,t){let e=Ii(["r","c","d","d2","d3"],r);return` + `}function knt(r,t){let e=Ci(["r","c","d","d2","d3"],r);return` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -445,7 +449,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } - `}function ant(r,t){let e=Ii(["r","c","d","d2","d3","d4"],r);return` + `}function Tnt(r,t){let e=Ci(["r","c","d","d2","d3","d4"],r);return` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -456,7 +460,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec6 result = ivec6(r, c, d, d2, d3, d4); return result; } - `}function lnt(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(y.arraysEqual(r,t))return e?` + `}function _nt(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(y.arraysEqual(r,t))return e?` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); @@ -489,7 +493,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec2(r, c); } - `}function unt(r,t,e){return y.arraysEqual(r,t)?e?` + `}function Ent(r,t,e){return y.arraysEqual(r,t)?e?` ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); } @@ -543,11 +547,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${r[1]}; return ivec2(r, c); } - `}function dp(r){return`offset${r}`}function cnt(r){let t=r.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),n=We();return` + `}function dp(r){return`offset${r}`}function Ant(r){let t=r.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),n=We();return` vec4 ${e}() { return ${n.texture2D}(${t}, halfCR); } - `}function pnt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${n}() {return ${e};}`;let[o,s]=r.shapeInfo.texShape;if(o===1&&s===1)return` + `}function Dnt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${n}() {return ${e};}`;let[o,s]=r.shapeInfo.texShape;if(o===1&&s===1)return` float ${n}() { return sampleTexture(${e}, halfCR); } @@ -561,7 +565,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${a}, ${u}, ${i}); return sampleTexture(${e}, uv); } - `}function mnt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,s=We();if(t)return` + `}function $nt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,s=We();if(t)return` vec4 ${n}(int index) { ivec2 packedTexShape = ivec2(ceil(float(${e}TexShape[0]) / 2.0), ceil(float(${e}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( @@ -574,9 +578,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${i[0]}, ${i[1]}, index); return ${s.texture2D}(${e}, uv); } - `}function fnt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return` + `}function Rnt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return` float ${n}(int index) { - ${Dd(r)} + ${$d(r)} } `;let o=r.shapeInfo.texShape,s=o[0],i=o[1];if(i===1&&s===1)return` float ${n}(int index) { @@ -612,7 +616,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${s}, ${i}, index + ${a}); return sampleTexture(${e}, uv); } - `}function dnt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=s[0],a=s[1],u=We();if(s!=null&&y.arraysEqual(e,s))return t?` + `}function Fnt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=s[0],a=s[1],u=We();if(s!=null&&y.arraysEqual(e,s))return t?` vec4 ${o}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]); @@ -636,7 +640,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col); return ${u.texture2D}(${n}, uv); } - `}function hnt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape;if(s!=null&&y.arraysEqual(e,s)){if(t)return` + `}function Ont(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape;if(s!=null&&y.arraysEqual(e,s)){if(t)return` float ${o}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]); return sampleTexture(${n}, uv); @@ -646,15 +650,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = (vec2(col, row) + halfCR) / vec2(${f}.0, ${m}.0); return sampleTexture(${n}, uv); } - `}let{newShape:i,keptDims:a}=y.squeezeShape(e),u=i;if(u.length=1?c="coords = 0;":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(` + `}function Gnt(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=r.shapeInfo.logicalShape.length,i=t.logicalShape.length,a=xL(r.shapeInfo.logicalShape,t.logicalShape),u=zt(i),l=i-s,c,p=["x","y","z","w","u","v"];s===0?c="":i<2&&a.length>=1?c="coords = 0;":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(` `);let m="";i<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+l]}`).join(", ");let f="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(t.logicalShape)===1;if(s===1&&!h&&!x)f=` return vec4(outputValue.xy, outputValue.xy); `;else if(h&&!x)i===1?f=` @@ -973,20 +977,20 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 outputValue = get${n}(${m}); ${f} } - `}function vnt(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=t.texShape,i=r.shapeInfo.texShape,a=r.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!r.shapeInfo.isUniform&&a===u&&r.shapeInfo.flatOffset==null&&y.arraysEqual(i,s))return` + `}function Wnt(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=t.texShape,i=r.shapeInfo.texShape,a=r.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!r.shapeInfo.isUniform&&a===u&&r.shapeInfo.flatOffset==null&&y.arraysEqual(i,s))return` float ${o}() { return sampleTexture(${e}, resultUV); } - `;let l=zt(u),c=oL(r.shapeInfo.logicalShape,t.logicalShape),p=u-a,m,f=["x","y","z","w","u","v"];a===0?m="":u<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(` + `;let l=zt(u),c=xL(r.shapeInfo.logicalShape,t.logicalShape),p=u-a,m,f=["x","y","z","w","u","v"];a===0?m="":u<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(` `);let d="";return u<2&&a>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),` float ${o}() { ${l} coords = getOutputCoords(); 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Shape ${a} and ${u} must match`)})}function cL(r,t,e,n,o){t.program.enableShapeUniforms||(lL(t.inShapeInfos,e),lL([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):r.setOutputMatrixTexture(s.texture,i[0],i[1]),r.setProgram(t.webGLProgram),L().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN);for(let u=0;u{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=Rw(r.packedInputs,i.shape,u),m="",f="",d="";if(c.length===1&&r.packedInputs){let N=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${N[0]>1}_${N[1]>1}`}else if(c.length===2&&!r.packedInputs)f=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let N=y.computeStrides(c);d=`${N[0]===u[1]}_${N[N.length-1]===u[1]}`}let h=i.shape.length,g=c.length===2&&y.arraysEqual(i.shape,u),x=y.sizeFromShape(i.shape)===1,b=S.getBroadcastDims(i.shape,e.shape),w=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(u,e.texData.texShape),I=r.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;n+=`${h}_${w}_${l?p:""}_${c.length}_${x}_${b}_${g}_${m}_${f}_${d}_${I}_${a}`}else{let u=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${u}_${a}`}});let o=r.userCode,s=r.constructor.name;return s+="_"+n+"_"+o+`${L().getNumber("WEBGL_VERSION")}`,s}function de(r){return L().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&r<=4}var Fw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Hu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=` + `}function zt(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 ${r} is not yet supported`)}function zw(r,t,e){let{newShape:n,keptDims:o}=y.squeezeShape(t),s=t.length,i=r&&s===3&&t[0]===1,a=i?t.slice(1):n,u=!r&&s>1&&!y.arraysEqual(t,e)&&n.lengthr[e]).join(", ")}function CL(r,t,e,n){let o=e.map((c,p)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:m}}),s=o.map(c=>c.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},a=yL(o,i,t),u=$T(r.gl,a),l=r.createProgram(u);return L().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(l),Object.assign({program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i},YT(r,t,l)))}function YT(r,t,e){let n=[],o=[],s,i,a,u=null,l=null;l=r.getUniformLocation(e,"NAN",!1),L().getNumber("WEBGL_VERSION")===1&&(u=r.getUniformLocation(e,"INFINITY",!1));let c=!1;for(let p of t.variableNames){let m={name:p,uniform:r.getUniformLocation(e,p,c),offset:r.getUniformLocation(e,`offset${p}`,c)};t.enableShapeUniforms&&(m.shape=r.getUniformLocation(e,`${p}Shape`,c),m.texShape=r.getUniformLocation(e,`${p}TexShape`,c)),n.push(m)}if(t.enableShapeUniforms&&(s=r.getUniformLocation(e,"outShape",c),a=r.getUniformLocation(e,"outShapeStrides",c),i=r.getUniformLocation(e,"outTexShape",c)),t.customUniforms)for(let p of t.customUniforms)o.push(r.getUniformLocation(e,p.name,c));return{variablesLocations:n,customUniformLocations:o,infLoc:u,nanLoc:l,outShapeLocation:s,outShapeStridesLocation:a,outTexShapeLocation:i}}function IL(r,t){if(r.length!==t.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t.length} inputs`);r.forEach((e,n)=>{let o=e.logicalShape,s=t[n],i=s.shape;if(!y.arraysEqual(o,i))throw Error(`Binary was compiled with different shapes than the current args. 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Shape ${a} and ${u} must match`)})}function vL(r,t,e,n,o){t.program.enableShapeUniforms||(IL(t.inShapeInfos,e),IL([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):r.setOutputMatrixTexture(s.texture,i[0],i[1]),r.setProgram(t.webGLProgram),r.bindVertexArray(t.webGLProgram.vao),L().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN);for(let u=0;u{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=zw(r.packedInputs,i.shape,u),m="",f="",d="";if(c.length===1&&r.packedInputs){let N=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${N[0]>1}_${N[1]>1}`}else if(c.length===2&&!r.packedInputs)f=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let N=y.computeStrides(c);d=`${N[0]===u[1]}_${N[N.length-1]===u[1]}`}let h=i.shape.length,g=c.length===2&&y.arraysEqual(i.shape,u),x=y.sizeFromShape(i.shape)===1,b=S.getBroadcastDims(i.shape,e.shape),w=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(u,e.texData.texShape),I=r.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;n+=`${h}_${w}_${l?p:""}_${c.length}_${x}_${b}_${g}_${m}_${f}_${d}_${I}_${a}`}else{let u=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${u}_${a}`}});let o=r.userCode,s=r.constructor.name;return s+="_"+n+"_"+o+`${L().getNumber("WEBGL_VERSION")}`,s}function de(r){return L().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&r<=4}var Bw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=qu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?fp(["r","c","d"],t):Ii(["r","c","d"],t)} + ${this.enableShapeUniforms?fp(["r","c","d"],t):Ci(["r","c","d"],t)} return ivec3(r, c, d); } @@ -1004,9 +1008,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${e.output} = result; } - `}};var Ow=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Hu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=` + `}};var Vw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=qu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?fp(["r","c","d"],t):Ii(["r","c","d"],t)} + ${this.enableShapeUniforms?fp(["r","c","d"],t):Ci(["r","c","d"],t)} return ivec3(r, c, d); } @@ -1024,26 +1028,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${e.output} = result; } - `}};var Pw=class{constructor(t){this.variableNames=["A"],this.outTexUsage=Zr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=` - ${$w} + `}};var Gw=class{constructor(t){this.variableNames=["A"],this.outTexUsage=Zr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=` + ${Lw} void main() { float x = getAAtOutCoords(); ${e.output} = encode_float(x); } - `}};var Mw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Zr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=` - ${$w} + `}};var Ww=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Zr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=` + ${Lw} void main() { ivec3 coords = getOutputCoords(); float x = 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f1={};Kt(f1,{bindVertexProgramAttributeStreams:()=>s1,createBufferFromOutputTexture:()=>l1,createFloat16MatrixTexture:()=>e1,createFloat16PackedMatrixTexture:()=>o1,createFloat32MatrixTexture:()=>t1,createIndexBuffer:()=>QT,createPackedMatrixTexture:()=>n1,createUnsignedBytesMatrixTexture:()=>r1,createVertexBuffer:()=>JT,createVertexShader:()=>ZT,downloadByteEncodedFloatMatrixFromOutputTexture:()=>c1,downloadFloat32MatrixFromBuffer:()=>u1,downloadMatrixFromPackedOutputTexture:()=>m1,downloadPackedMatrixFromBuffer:()=>p1,getInternalFormatForFloat16MatrixTexture:()=>qw,getInternalFormatForFloat16PackedMatrixTexture:()=>Xw,getInternalFormatForFloat32MatrixTexture:()=>Hw,getInternalFormatForPackedMatrixTexture:()=>jw,getInternalFormatForUnsignedBytesMatrixTexture:()=>Kw,uploadDenseMatrixToTexture:()=>i1,uploadPixelDataToTexture:()=>a1});function ZT(r){let t=We(),e=`${t.version} precision highp float; ${t.attribute} vec3 clipSpacePos; ${t.attribute} vec2 uv; @@ -1115,7 +1119,7 @@ vec2 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if(L().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",Xn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Xn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=JT(this.gl),this.indexBuffer=QT(this.gl),this.framebuffer=zT(this.gl),this.textureConfig=ag(this.gl,this.textureHalfFloatExtension)}get debug(){return L().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 t=this.gl;ht(t,()=>t.finish()),ht(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ht(t,()=>t.deleteFramebuffer(this.framebuffer)),ht(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ht(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ht(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),t1(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),e1(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),r1(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),a1(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),i1(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),o1(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),n1(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(Pw(this.gl,this.framebuffer),this.outputTexture=null),ht(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,n){return this.downloadMatrixDriver(t,()=>c1(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return p1(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return u1(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=l1(this.gl,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,n;if(L().getBool("WEBGL_FENCE_API_ENABLED")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=()=>{let i=o.clientWaitSync(s,0,0);return i===o.ALREADY_SIGNALED||i===o.CONDITION_SATISFIED},e=s}else L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(e=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(e,L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:e,isFencePassed:n}}downloadMatrixFromPackedTexture(t,e,n){return this.downloadMatrixDriver(t,()=>m1(this.gl,e,n))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=ZT(e));let n=RT(e);ht(e,()=>e.attachShader(n,this.vertexShader)),ht(e,()=>e.attachShader(n,t)),FT(e,n);let o=Object.assign(n,{vao:this.createVertexArray()});return this.debug&&lg(e,o),o}buildVao(t){this.setProgram(t),this.bindVertexArray(t.vao);let e=this.gl;ht(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),s1(e,t,this.vertexBuffer)}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ht(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&lg(this.gl,this.program),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?BT(this.gl,t,e):VT(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ht(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),GT(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=wa(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&lg(this.gl,this.program),Nd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}ht(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ht(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Sd(this.gl,L().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(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(t,L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(t,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(t,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=Knt(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let n;"setTimeoutCustom"in L().platform&&(n=L().platform.setTimeoutCustom.bind(L().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),ug(this.gl,t,this.framebuffer),this.debug&&Nd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ug(this.gl,this.outputTexture,this.framebuffer),this.debug&&Nd(this.gl)):Pw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;ug(o,t,this.framebuffer),this.debug&&Nd(o),this.outputTexture=t,ht(o,()=>o.viewport(0,0,e,n)),ht(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),ht(this.gl,()=>this.gl.scissor(t,e,n,o))}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 Knt(r){let t=0;for(;t`${r}.${e}`)}function er(r,t){return t===1?[r]:d1(r,t)}function hz(r,t){if(r===1)return"rc";let e="";for(let n=0;n= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}), cEdge ? 0. : getA(${e[1]}), rEdge ? 0. : getA(${e[2]}), - rEdge || cEdge ? 0. : getA(${e[3]})`}};var Fd=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let n="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2===1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),n+=` + rEdge || cEdge ? 0. : getA(${e[3]})`}};var Od=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let n="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2===1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),n+=` ${s} ${o>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); @@ -1154,8 +1158,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${o>0?"}":""} `}this.userCode=` - ${_nt(e,this.enableShapeUniforms)} - ${this.enableShapeUniforms?_d():Td(t)} + ${jnt(e,this.enableShapeUniforms)} + ${this.enableShapeUniforms?Ed():_d(t)} void main() { ivec3 rc = getOutputCoords(); @@ -1170,12 +1174,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function _nt(r,t){return` + `}};function jnt(r,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?nL(["r","c","d"],"inputShape"):Ii(["r","c","d"],r)} + ${t?gL(["r","c","d"],"inputShape"):Ci(["r","c","d"],r)} return ivec3(r, c, d); } - `}var jw=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=oz(e,n),s=sz(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=nz(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===Lr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Lr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Lr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=oz(n,o),i=sz(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=nz(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().get("WEBGL_DELETE_TEXTURE_THRESHOLD");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}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 t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Ent(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function nz(r,t,e,n,o){let s=Ant(t,n),i;if(o){let[u,l]=wa(r[0],r[1]);i=u*l}else{let[u,l]=mp(r[0],r[1]);i=u*l}let a=Ent(e,s);return i*a}function Ant(r,t){switch(r){case Lr.PACKED_2X2_FLOAT32:return Gw(t);case Lr.PACKED_2X2_FLOAT16:return Ww(t);case Lr.UNPACKED_FLOAT32:return zw(t);case Lr.UNPACKED_FLOAT16:return Bw(t);case Lr.PACKED_4X1_UNSIGNED_BYTE:return Vw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function Dnt(r){return L().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Lr.PACKED_2X2_FLOAT32:Lr.UNPACKED_FLOAT32:r?Lr.PACKED_2X2_FLOAT16:Lr.UNPACKED_FLOAT16}function oz(r,t){if(r===Zr.UPLOAD)return Lr.PACKED_2X2_FLOAT32;if(r===Zr.RENDER||r==null)return Dnt(t);if(r===Zr.DOWNLOAD||r===Zr.PIXELS)return Lr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function sz(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var zr=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=` + `}var tI=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=xz(e,n),s=yz(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=gz(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===Lr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Lr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Lr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=xz(n,o),i=yz(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=gz(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().get("WEBGL_DELETE_TEXTURE_THRESHOLD");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}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 t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Xnt(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function gz(r,t,e,n,o){let s=Ynt(t,n),i;if(o){let[u,l]=wa(r[0],r[1]);i=u*l}else{let[u,l]=mp(r[0],r[1]);i=u*l}let a=Xnt(e,s);return i*a}function Ynt(r,t){switch(r){case Lr.PACKED_2X2_FLOAT32:return jw(t);case Lr.PACKED_2X2_FLOAT16:return Xw(t);case Lr.UNPACKED_FLOAT32:return Hw(t);case Lr.UNPACKED_FLOAT16:return qw(t);case Lr.PACKED_4X1_UNSIGNED_BYTE:return Kw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function Znt(r){return L().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Lr.PACKED_2X2_FLOAT32:Lr.UNPACKED_FLOAT32:r?Lr.PACKED_2X2_FLOAT16:Lr.UNPACKED_FLOAT16}function xz(r,t){if(r===Zr.UPLOAD)return Lr.PACKED_2X2_FLOAT32;if(r===Zr.RENDER||r==null)return Znt(t);if(r===Zr.DOWNLOAD||r===Zr.PIXELS)return Lr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function yz(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var zr=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${e} } @@ -1186,11 +1190,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},yr="if (isnan(x)) return x;",iz="return x;",a1="return abs(x);";var az="return (x >= 0.0) ? x : (exp(x) - 1.0);",lz=yr+` + `}},yr="if (isnan(x)) return x;",bz="return x;",h1="return abs(x);";var wz="return (x >= 0.0) ? x : (exp(x) - 1.0);",Iz=yr+` return (x < 0.0) ? 0.0 : x; -`,uz=yr+` +`,Cz=yr+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Ia="return x;",cz="return 1.0 / (1.0 + exp(-1.0 * x));";var mz="return x;",fz=` +`,Ia="return x;",vz="return 1.0 / (1.0 + exp(-1.0 * x));";var Nz="return x;",kz=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1199,7 +1203,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,dz=` +`,Tz=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1209,7 +1213,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,hz=` +`,_z=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1219,7 +1223,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,gz="return 1.0 / (1.0 + exp(-1.0 * x));",Dn=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=` +`,Ez="return 1.0 / (1.0 + exp(-1.0 * x));",Rn=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${e} } @@ -1230,17 +1234,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}};var Xw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let e=t.length,n=er("rc",e),o=zt(e),s=rz(e,n),i=n.slice(-2),a=e<=1?"rc":`vec2(${i.join(",")})`;this.userCode=` + `}};var eI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let e=t.length,n=er("rc",e),o=zt(e),s=hz(e,n),i=n.slice(-2),a=e<=1?"rc":`vec2(${i.join(",")})`;this.userCode=` void main() { ${o} rc = getOutputCoords(); vec4 packedInput = getA(${s}); setOutput(getChannel(packedInput, ${a})); } - `}};var Rnt=Kr.whereImpl,Fnt=1e-7,Ont=1e-4,Yw={};function Pnt(r){return r in Yw||(Yw[r]={}),Yw[r]}var Mnt=L().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Lnt=600;function znt(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*Lnt/1024/1024}var Ku=class extends Go{nextDataId(){return Ku.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!L().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof hp)e=t;else{let n=qn(L().getNumber("WEBGL_VERSION"),t);e=new hp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=qn(L().getNumber("WEBGL_VERSION"));e=new hp(n),this.binaryCache=Pnt(L().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new jw(this.gpgpu),this.numMBBeforeWarning=znt(),this.texData=new Ta(this,Bn())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=Nd(e),c=new ig(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((L().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||L().getBool("DEBUG"))&&this.checkNumericalProblems(t),n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Zr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(L().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Zr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new Dn(a,Ia):m=new zr(a,Ia);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new Dn(o,Ia):d=new zr(o,Ia);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(L().getBool("DEBUG")&&!L().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&L().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(i!=="complex64"&&L().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...rg(o))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(i==="complex64"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Bn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let f;u?f=new Dn(s,Ia):f=new zr(s,Ia);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),p=Bn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>y.decodeString(o));return bt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return bt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=Mnt){return L().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Bn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new Xw(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new Kw(t.shape),n=!0;return this.runWebGLProgram(e,[t],t.dtype,null,n)}packedReshape(t,e){let n=[zl(t.shape),...Bl(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[zl(e),...Bl(e)],i=new Fd(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let a=Nd(s),u;o?u=new Ow(a):u=new Fw(a);let l=!0,c=[e!=null?e:rg(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Hu.DENSE){let x=i!=null?i:rg(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=L().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!qu(b.shape,x.shape)){let w=x,I=x.shape;x.shape=b.shape,x=this.packedReshape(x,I),l.push(x),b=this.texData.get(x.dataId),w.shape=I}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=pL(t,c,p),f=this.getAndSaveBinary(m,()=>uL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get("ENGINE_COMPILE_ONLY")||cL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=L().get("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!L().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(L().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=B(()=>{if(!L().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=L().getBool("DEBUG");L().set("DEBUG",!1);let e=this.abs(ft(1e-8)).dataSync()[0];if(L().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Fnt:Ont}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=OT(n,u),e.texShape=p),s!=null){let m=Nd(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=wa(p[0],p[1])),u?f=new Lw(m,g):f=new ig(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Zr.PIXELS:w.usage=Zr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let I=[[h,d]],N=!0,E=this.runWebGLProgram(f,[b],o,I,N),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,L().get("ENGINE_COMPILE_ONLY")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=Bnt(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!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(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Sh(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(_w(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=GT(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=a,t.outTexShapeLocation=u}}createTensorFromGPUData(t,e,n){t.channels=t.channels||"RGBA";let{texture:o,height:s,width:i,channels:a}=t,u=Bn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=u.writeTexture(o,e,n,s,i,a);return Bn().makeTensorFromDataId(l,e,n,u)}};Ku.nextDataId=0;function Bnt(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew Ku,2);var n2e={forceHalfFloat:yz};var Od=` + `}};var Qnt=Kr.whereImpl,tot=1e-7,eot=1e-4,rI={};function rot(r){return r in rI||(rI[r]={}),rI[r]}var not=L().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),oot=600;function sot(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*oot/1024/1024}var ju=class extends Wo{nextDataId(){return ju.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!L().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof hp)e=t;else{let n=jn(L().getNumber("WEBGL_VERSION"),t);e=new hp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=jn(L().getNumber("WEBGL_VERSION"));e=new hp(n),this.binaryCache=rot(L().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new tI(this.gpgpu),this.numMBBeforeWarning=sot(),this.texData=new Ta(this,Vn())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=kd(e),c=new cg(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((L().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||L().getBool("DEBUG"))&&this.checkNumericalProblems(t),n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Zr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(L().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Zr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new Rn(a,Ia):m=new zr(a,Ia);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new Rn(o,Ia):d=new zr(o,Ia);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(L().getBool("DEBUG")&&!L().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&L().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(i!=="complex64"&&L().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...ig(o))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(i==="complex64"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Vn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let f;u?f=new Rn(s,Ia):f=new zr(s,Ia);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),p=Vn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>y.decodeString(o));return bt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return bt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=not){return L().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Vn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new eI(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new Qw(t.shape),n=!0;return this.runWebGLProgram(e,[t],t.dtype,null,n)}packedReshape(t,e){let n=[zl(t.shape),...Bl(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[zl(e),...Bl(e)],i=new Od(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let a=kd(s),u;o?u=new Vw(a):u=new Bw(a);let l=!0,c=[e!=null?e:ig(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===qu.DENSE){let x=i!=null?i:ig(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=L().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Ku(b.shape,x.shape)){let w=x,I=x.shape;x.shape=b.shape,x=this.packedReshape(x,I),l.push(x),b=this.texData.get(x.dataId),w.shape=I}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=SL(t,c,p),f=this.getAndSaveBinary(m,()=>CL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get("ENGINE_COMPILE_ONLY")||vL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=L().get("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!L().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(L().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=B(()=>{if(!L().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=L().getBool("DEBUG");L().set("DEBUG",!1);let e=this.abs(ft(1e-8)).dataSync()[0];if(L().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?tot:eot}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=WT(n,u),e.texShape=p),s!=null){let m=kd(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=wa(p[0],p[1])),u?f=new Uw(m,g):f=new cg(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Zr.PIXELS:w.usage=Zr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let I=[[h,d]],N=!0,E=this.runWebGLProgram(f,[b],o,I,N),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,L().get("ENGINE_COMPILE_ONLY")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=iot(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!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(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await kh(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Fw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=YT(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=a,t.outTexShapeLocation=u}}createTensorFromGPUData(t,e,n){t.channels=t.channels||"RGBA";let{texture:o,height:s,width:i,channels:a}=t,u=Vn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=u.writeTexture(o,e,n,s,i,a);return Vn().makeTensorFromDataId(l,e,n,u)}};ju.nextDataId=0;function iot(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew ju,2);var aDe={forceHalfFloat:Dz};var Pd=` if (isnan(a)) return a; if (isnan(b)) return b; -`;var po=class{constructor(t,e,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=de(this.outputShape.length),this.userCode=` +`;var mo=class{constructor(t,e,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=de(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${t} } @@ -1250,12 +1254,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}};var jn=` + `}};var Yn=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; -`;var Lo=class{constructor(t,e,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=de(s);let i="";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=` +`;var zo=class{constructor(t,e,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=de(s);let i="";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=` result.y = 0.; result.z = 0.; result.w = 0.; @@ -1299,13 +1303,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function rr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var bz={kernelName:go,backendName:"webgl",kernelFunc:rr};function $n(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,"complex64"),i=e.texData.get(s.dataId),a=rr({inputs:{x:n},backend:e}),u=rr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var wz={kernelName:Op,backendName:"webgl",kernelFunc:$n};var l1="return (a < 0.) ? b * a : a;",u1=` + `}};function rr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var $z={kernelName:xo,backendName:"webgl",kernelFunc:rr};function Fn(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,"complex64"),i=e.texData.get(s.dataId),a=rr({inputs:{x:n},backend:e}),u=rr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var Rz={kernelName:Op,backendName:"webgl",kernelFunc:Fn};var g1="return (a < 0.) ? b * a : a;",x1=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function Vnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),a=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Lo(u1,o.shape,i.shape):new po(l1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],"float32");return e.disposeIntermediateTensorInfo(i),u}var Iz={kernelName:ws,backendName:"webgl",kernelFunc:Vnt};var c1="return (a < 0.) ? b * a : a;",p1=` +`;function aot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),a=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new zo(x1,o.shape,i.shape):new mo(g1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],"float32");return e.disposeIntermediateTensorInfo(i),u}var Fz={kernelName:Is,backendName:"webgl",kernelFunc:aot};var y1="return (a < 0.) ? b * a : a;",b1=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function Gnt(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Lo(p1,n.shape,o.shape):new po(c1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],"float32")}var Cz={kernelName:Os,backendName:"webgl",kernelFunc:Gnt};var zo="if (isnan(x)) return x;";function wt({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=L().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new Dn(i.shape,t):c=new zr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function ue({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype==="complex64"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[I,N]=w,E={dataId:I.dataId,dtype:I.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},D=new po(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],lr(I.dtype,N.dtype))}),b=$n({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||lr(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype==="string"?S.fromUint8ToStringArray(d):d,x=u.dtype==="string"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),I=c.makeTensorInfo(w,p),N=c.texData.get(I.dataId);return N.values=b,I}let m=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,f;return m?f=new Lo(t,u.shape,l.shape,e):f=new po(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Vl(r,t=!1){if(r==="linear")return t?mz:iz;if(r==="relu")return t?dz:lz;if(r==="elu")return t?fz:az;if(r==="relu6")return t?hz:uz;if(r==="prelu")return t?p1:c1;if(r==="leakyrelu")return t?u1:l1;if(r==="sigmoid")return t?gz:cz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Pd=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=de(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";a&&(u?g=`vec4 activation(vec4 a) { +`;function lot(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new zo(b1,n.shape,o.shape):new mo(y1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],"float32")}var Oz={kernelName:Ps,backendName:"webgl",kernelFunc:lot};var Bo="if (isnan(x)) return x;";function wt({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=L().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new Rn(i.shape,t):c=new zr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function ue({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype==="complex64"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[I,N]=w,E={dataId:I.dataId,dtype:I.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},D=new mo(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],ur(I.dtype,N.dtype))}),b=Fn({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||ur(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype==="string"?S.fromUint8ToStringArray(d):d,x=u.dtype==="string"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),I=c.makeTensorInfo(w,p),N=c.texData.get(I.dataId);return N.values=b,I}let m=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,f;return m?f=new zo(t,u.shape,l.shape,e):f=new mo(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Vl(r,t=!1){if(r==="linear")return t?Nz:bz;if(r==="relu")return t?Tz:Iz;if(r==="elu")return t?kz:wz;if(r==="relu6")return t?_z:Cz;if(r==="prelu")return t?b1:y1;if(r==="leakyrelu")return t?x1:g1;if(r==="sigmoid")return t?Ez:vz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Md=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=de(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";a&&(u?g=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${a} }`:l?g=`vec4 activation(vec4 a) { @@ -1344,7 +1348,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};var m1={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},lg=class{constructor(t,e,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.userCode=` + `}};var w1={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},mg=class{constructor(t,e,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.userCode=` float binaryOpComplex( float areal, float aimag, float breal, float bimag) { ${t} @@ -1357,7 +1361,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float bimag = getBImagAtOutCoords(); setOutput(binaryOpComplex(areal, aimag, breal, bimag)); } - `}};var vz="return a * b;";function ug(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=S.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),l=new lg(m1.REAL,n.shape,o.shape),c=new lg(m1.IMAG,n.shape,o.shape),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:n.shape},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:o.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:o.shape}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=$n({inputs:{real:m,imag:f},backend:e});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}if(e.shouldExecuteOnCPU([n,o])){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),[l,c]=$L(n.shape,o.shape,a.values,u.values,s),p=e.makeTensorInfo(c,s),m=e.texData.get(p.dataId);return m.values=l,p}let i;return L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Lo(vz,n.shape,o.shape):i=new po(vz,n.shape,o.shape),e.runWebGLProgram(i,[n,o],s)}var Sz={kernelName:Ds,backendName:"webgl",kernelFunc:ug};function Nz(r,t,e){let n=[zl(r.shape),...Bl(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[zl(t),...Bl(t)],i=new Fd(s,n),a=!0,u=[n],l=e.runWebGLProgram(i,[o],r.dtype,u,a);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{shape:s}=n,i=e,a=y.sizeFromShape(o.shape),u=y.inferFromImplicitShape(s,a),l=y.sizeFromShape(u);y.assert(a===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!qu(o.shape,u)&&!(c.texture!==null&&qu(c.shape,u))?Nz(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var kz={kernelName:zi,backendName:"webgl",kernelFunc:rt};var cg=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l="sumValue += dot(values, ones);";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=` + `}};var Pz="return a * b;";function fg(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=S.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),l=new mg(w1.REAL,n.shape,o.shape),c=new mg(w1.IMAG,n.shape,o.shape),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:n.shape},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:o.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:o.shape}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Fn({inputs:{real:m,imag:f},backend:e});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}if(e.shouldExecuteOnCPU([n,o])){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),[l,c]=HL(n.shape,o.shape,a.values,u.values,s),p=e.makeTensorInfo(c,s),m=e.texData.get(p.dataId);return m.values=l,p}let i;return L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new zo(Pz,n.shape,o.shape):i=new mo(Pz,n.shape,o.shape),e.runWebGLProgram(i,[n,o],s)}var Mz={kernelName:$s,backendName:"webgl",kernelFunc:fg};function Lz(r,t,e){let n=[zl(r.shape),...Bl(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[zl(t),...Bl(t)],i=new Od(s,n),a=!0,u=[n],l=e.runWebGLProgram(i,[o],r.dtype,u,a);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{shape:s}=n,i=e,a=y.sizeFromShape(o.shape),u=y.inferFromImplicitShape(s,a),l=y.sizeFromShape(u);y.assert(a===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!Ku(o.shape,u)&&!(c.texture!==null&&Ku(c.shape,u))?Lz(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var zz={kernelName:Bi,backendName:"webgl",kernelFunc:rt};var dg=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l="sumValue += dot(values, ones);";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=` if (inIdx < 0 || inIdx >= ${s}) { return 0.0; } @@ -1410,7 +1414,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(sumValue); } - `}};var Zw=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a="0.0",u="";e==="prod"?a="1.0":e==="min"?(a="1.0 / 1e-20",u="min"):e==="max"&&(a="-1.0 / 1e-20",u="max");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?l="sumValue":e==="prod"?l="prodValue":e==="all"?l="allValue":e==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=` + `}};var nI=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a="0.0",u="";e==="prod"?a="1.0":e==="min"?(a="1.0 / 1e-20",u="min"):e==="max"&&(a="-1.0 / 1e-20",u="max");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?l="sumValue":e==="prod"?l="prodValue":e==="all"?l="allValue":e==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=` if (${e==="sum"}) { sumValue += dot(values, ones); } else if (${e==="prod"}) { @@ -1502,12 +1506,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function Unt(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function Xn(r,t,e,n){let o=Unt(r.shape),s=r;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=zt(this.rank),s=i1("rc",this.rank),i=new Array(this.rank);for(let c=0;c6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=zt(this.rank),s=d1("rc",this.rank),i=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],E=n?[b,d,m]:[b,m,d],A=rt({inputs:{x:r},backend:o,attrs:{shape:N}}),D=rt({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[A,D],P=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u==="leakyrelu",H=u!=null?Vl(u,!0):null,K=G||W||q||H!=null,X;if((f===1||d===1)&&V>f1&&K===!1){let et=A,nt=D;e&&(et=Pe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(nt=Pe({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),F.push(nt));let st=d!==1,at=d===1,ot=et;st&&(ot=rt({inputs:{x:et},backend:o,attrs:{shape:[P,V,1]}}),F.push(ot));let it=d===1?2:1,mt=nt;at&&(mt=rt({inputs:{x:nt},backend:o,attrs:{shape:[P,1,V]}}),F.push(mt));let gt=ug({inputs:{a:ot,b:mt},backend:o});X=xp({inputs:{x:gt},backend:o,attrs:{axis:it,keepDims:!0}}),F.push(gt)}else{let et=lr(r.dtype,t.dtype),nt=new Pd(N,E,[P,f,d],e,n,G,H,W,q),st=[A,D];if(s!=null&&st.push(s),W&&st.push(i),q){let at=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));st.push(at),F.push(at)}X=o.runWebGLProgram(nt,st,et)}let Z=rt({inputs:{x:X},backend:o,attrs:{shape:I}});F.push(X);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function qnt(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return yp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var Az={kernelName:qi,backendName:"webgl",kernelFunc:qnt};var Dz="return abs(x);";function Knt(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=e.texData.get(n.dataId),i=Hw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Dn(n.shape,Dz):o=new zr(n.shape,Dz),e.runWebGLProgram(o,[n],n.dtype)}var $z={kernelName:Ti,backendName:"webgl",kernelFunc:Knt};var jnt=yr+` + `}};function Xu(r,t,e){let n=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sI(r.shape,t):new oI(r.shape,t);return e.runWebGLProgram(n,[r],r.dtype)}function Bz(r,t,e,n){let o=t,s=r.shape.length,i=y.parseAxisParam(o,r.shape),a=i,u=S.getAxesPermutation(a,s),l=u!=null,c=r;l&&(c=Xu(r,u,n),a=S.getInnerMostAxes(a.length,s)),S.assertAxesAreInnerMostDims("sum",a,s);let[p,m]=S.computeOutAndReduceShapes(c.shape,a),f=p;e&&(f=S.expandShapeToKeepDim(p,i));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=rt({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),b=mc(r.dtype),w=Zn(x,b,"sum",n),I=rt({inputs:{x:w},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),l&&n.disposeIntermediateTensorInfo(c),I}function xp(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;return Bz(o,s,i,e)}var Vz={kernelName:Js,backendName:"webgl",kernelFunc:xp};function Pe(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{perm:s}=n,i=e,a=o.shape.length,u=new Array(a);for(let c=0;c`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],E=n?[b,d,m]:[b,m,d],A=rt({inputs:{x:r},backend:o,attrs:{shape:N}}),D=rt({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[A,D],P=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u==="leakyrelu",H=u!=null?Vl(u,!0):null,K=G||W||q||H!=null,X;if((f===1||d===1)&&V>I1&&K===!1){let et=A,nt=D;e&&(et=Pe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(nt=Pe({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),F.push(nt));let st=d!==1,at=d===1,ot=et;st&&(ot=rt({inputs:{x:et},backend:o,attrs:{shape:[P,V,1]}}),F.push(ot));let it=d===1?2:1,mt=nt;at&&(mt=rt({inputs:{x:nt},backend:o,attrs:{shape:[P,1,V]}}),F.push(mt));let gt=fg({inputs:{a:ot,b:mt},backend:o});X=xp({inputs:{x:gt},backend:o,attrs:{axis:it,keepDims:!0}}),F.push(gt)}else{let et=ur(r.dtype,t.dtype),nt=new Md(N,E,[P,f,d],e,n,G,H,W,q),st=[A,D];if(s!=null&&st.push(s),W&&st.push(i),q){let at=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));st.push(at),F.push(at)}X=o.runWebGLProgram(nt,st,et)}let Z=rt({inputs:{x:X},backend:o,attrs:{shape:I}});F.push(X);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function mot(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return yp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var Wz={kernelName:Ki,backendName:"webgl",kernelFunc:mot};var Uz="return abs(x);";function fot(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=e.texData.get(n.dataId),i=Zw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Rn(n.shape,Uz):o=new zr(n.shape,Uz),e.runWebGLProgram(o,[n],n.dtype)}var Hz={kernelName:_i,backendName:"webgl",kernelFunc:fot};var dot=yr+` if (abs(x) > 1.) { return NAN; } return acos(x); -`,Xnt=wt({opSnippet:jnt}),Rz={kernelName:Uo,backendName:"webgl",kernelFunc:Xnt};var Ynt=yr+` +`,hot=wt({opSnippet:dot}),qz={kernelName:Ho,backendName:"webgl",kernelFunc:hot};var got=yr+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,Znt=wt({opSnippet:Ynt}),Fz={kernelName:Ho,backendName:"webgl",kernelFunc:Znt};var Oz="return a + b;",Jnt=ue({opSnippet:Oz,packedOpSnippet:Oz,supportsComplex:!0,cpuKernelImpl:mL}),Pz={kernelName:ro,backendName:"webgl",kernelFunc:Jnt};var tI=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` +return log(x + sqrt(x * x - 1.0));`,xot=wt({opSnippet:got}),Kz={kernelName:qo,backendName:"webgl",kernelFunc:xot};var jz="return a + b;",yot=ue({opSnippet:jz,packedOpSnippet:jz,supportsComplex:!0,cpuKernelImpl:NL}),Xz={kernelName:oo,backendName:"webgl",kernelFunc:yot};var iI=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} @@ -1539,7 +1543,7 @@ return log(x + sqrt(x * x - 1.0));`,Znt=wt({opSnippet:Ynt}),Fz={kernelName:Ho,ba float result = ${o}; setOutput(result); } - `}};var eI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` + `}};var aI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} @@ -1547,7 +1551,7 @@ return log(x + sqrt(x * x - 1.0));`,Znt=wt({opSnippet:Ynt}),Fz={kernelName:Ho,ba vec4 result = ${o}; setOutput(result); } - `}};function rI(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return rr({inputs:{x:n[0]},backend:e});if(n.length>L().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(n.length/2),l=rI({inputs:n.slice(0,u),backend:e}),c=rI({inputs:n.slice(u),backend:e});return rI({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>lr(u,l)),s=n.map(u=>u.shape),a=L().getBool("WEBGL_PACK")?new eI(n[0].shape,s):new tI(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var Mz={kernelName:qo,backendName:"webgl",kernelFunc:rI};function Qnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("all",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Xn(h,h.dtype,"all",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var Lz={kernelName:Ea,backendName:"webgl",kernelFunc:Qnt};function tot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("any",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Xn(h,h.dtype,"any",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var zz={kernelName:Aa,backendName:"webgl",kernelFunc:tot};var nI=class{constructor(t,e,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,i];let a=e==="max"?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + `}};function lI(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return rr({inputs:{x:n[0]},backend:e});if(n.length>L().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(n.length/2),l=lI({inputs:n.slice(0,u),backend:e}),c=lI({inputs:n.slice(u),backend:e});return lI({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>ur(u,l)),s=n.map(u=>u.shape),a=L().getBool("WEBGL_PACK")?new aI(n[0].shape,s):new iI(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var Yz={kernelName:Ko,backendName:"webgl",kernelFunc:lI};function bot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("all",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Zn(h,h.dtype,"all",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var Zz={kernelName:Ea,backendName:"webgl",kernelFunc:bot};function wot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("any",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Zn(h,h.dtype,"any",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var Jz={kernelName:Aa,backendName:"webgl",kernelFunc:wot};var uI=class{constructor(t,e,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,i];let a=e==="max"?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1567,7 +1571,7 @@ return log(x + sqrt(x * x - 1.0));`,Znt=wt({opSnippet:Ynt}),Fz={kernelName:Ho,ba } setOutput(float(bestIndex)); } - `}};var oI=class{constructor(t,e,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push("bestIndicesA");let a=this.outputShape,u=a.length,l=zt(u),c=er("coords",u),p,m;if(i===1){m=u+1;let D=zt(m);p=` + `}};var cI=class{constructor(t,e,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push("bestIndicesA");let a=this.outputShape,u=a.length,l=zt(u),c=er("coords",u),p,m;if(i===1){m=u+1;let D=zt(m);p=` ${D} sourceLocR = ${D}(${c.join()}, 0); ++${c[u-1]}; ${D} sourceLocG = ${D}(${c.join()}, 0); @@ -1629,25 +1633,25 @@ return log(x + sqrt(x * x - 1.0));`,Znt=wt({opSnippet:Ynt}),Fz={kernelName:Ho,ba } setOutput(bestIndex); } - `}};function Bz(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new nI(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=Bz(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function Vz(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new oI(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,"int32");if(l.shape.length===t.shape.length){let c=Vz(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function sI(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!L().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=rt({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=Bz(r,m,n);s.push(f);let d=rt({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return Vz(r,t,n)}function eot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=sI(e,u,i[0],"max");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var Gz={kernelName:_i,backendName:"webgl",kernelFunc:eot};function rot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=sI(e,u,i[0],"min");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var Wz={kernelName:Ei,backendName:"webgl",kernelFunc:rot};var not=yr+` + `}};function Qz(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new uI(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=Qz(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function t3(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new 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Iot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=pI(e,u,i[0],"max");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var e3={kernelName:Ei,backendName:"webgl",kernelFunc:Iot};function Cot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=pI(e,u,i[0],"min");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var r3={kernelName:Ai,backendName:"webgl",kernelFunc:Cot};var vot=yr+` if (abs(x) > 1.) { return NAN; } return asin(x); -`,oot=wt({opSnippet:not}),Uz={kernelName:Ko,backendName:"webgl",kernelFunc:oot};var sot=yr+"return log(x + sqrt(x * x + 1.0));",iot=wt({opSnippet:sot}),Hz={kernelName:jo,backendName:"webgl",kernelFunc:iot};var aot=yr+` +`,Sot=wt({opSnippet:vot}),n3={kernelName:jo,backendName:"webgl",kernelFunc:Sot};var Not=yr+"return log(x + sqrt(x * x + 1.0));",kot=wt({opSnippet:Not}),o3={kernelName:Xo,backendName:"webgl",kernelFunc:kot};var Tot=yr+` return atan(x); -`,lot=wt({opSnippet:aot}),qz={kernelName:Xo,backendName:"webgl",kernelFunc:lot};var uot=Od+` +`,_ot=wt({opSnippet:Tot}),s3={kernelName:Yo,backendName:"webgl",kernelFunc:_ot};var Eot=Pd+` return atan(a, b); -`,cot=` +`,Aot=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - `+jn+` + `+Yn+` return result; 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2.0;`,Rot=wt({opSnippet:$ot}),a3={kernelName:Zo,backendName:"webgl",kernelFunc:Rot};var vi=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e==="avg",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),n){let D=">=";this.userCode=` const ivec2 strides = ivec2(${a}, ${u}); const ivec2 pads = ivec2(${f}, ${d}); @@ -1788,7 +1792,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(${I}); } - `}},Xu=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e==="avg",I="0.0";if(w||(I="-1.0 / 1e-20"),n){let P=">=";this.userCode=` + `}},Yu=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e==="avg",I="0.0";if(w||(I="-1.0 / 1e-20"),n){let P=">=";this.userCode=` const ivec3 strides = ivec3(${a}, ${u}, ${l}); const ivec3 pads = ivec3(${g}, ${x}, ${b}); @@ -1951,7 +1955,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(${E}); } - `}};function dot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;wi(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new Ci(c,"avg",!1);return e.runWebGLProgram(p,[o],"float32")}var Xz={kernelName:Jo,backendName:"webgl",kernelFunc:dot};function hot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new Xu(p,"avg",!1);return e.runWebGLProgram(m,[o],"float32")}var Yz={kernelName:Ai,backendName:"webgl",kernelFunc:hot};var iI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=` + `}};function Fot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;Ii(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new vi(c,"avg",!1);return e.runWebGLProgram(p,[o],"float32")}var l3={kernelName:Qo,backendName:"webgl",kernelFunc:Fot};function Oot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new Yu(p,"avg",!1);return e.runWebGLProgram(m,[o],"float32")}var u3={kernelName:Di,backendName:"webgl",kernelFunc:Oot};var mI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=` const ivec2 pads = ivec2(${c}, ${p}); const float avgMultiplier = float(${m}); @@ -1993,7 +1997,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(dotProd); } - `}},aI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=` + `}},fI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=` const ivec3 pads = ivec3(${d}, ${h}, ${g}); const float avgMultiplier = float(${x}); @@ -2049,7 +2053,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(dotProd); } - `}};function got(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new aI(m);return e.runWebGLProgram(f,[o],i.dtype)}var Zz={kernelName:Xl,backendName:"webgl",kernelFunc:got};function xot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;wi([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new iI(c);return e.runWebGLProgram(p,[o],i.dtype)}var Jz={kernelName:Fp,backendName:"webgl",kernelFunc:xot};function yot(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return yp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var Qz={kernelName:Qo,backendName:"webgl",kernelFunc:yot};var lI=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="0.0";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="1.0";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` + `}};function Pot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new fI(m);return e.runWebGLProgram(f,[o],i.dtype)}var c3={kernelName:Xl,backendName:"webgl",kernelFunc:Pot};function Mot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;Ii([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new mI(c);return e.runWebGLProgram(p,[o],i.dtype)}var p3={kernelName:Fp,backendName:"webgl",kernelFunc:Mot};function Lot(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return yp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var m3={kernelName:ts,backendName:"webgl",kernelFunc:Lot};var dI=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="0.0";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="1.0";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2059,7 +2063,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN float inv = scale * inversesqrt(variance + float(${i})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}};var uI=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="vec4(0.0)";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="vec4(1.0)";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` + `}};var hI=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="vec4(0.0)";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="vec4(1.0)";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` void main() { vec4 offset = ${a}; vec4 scale = ${u}; @@ -2072,7 +2076,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN setOutput((x - mean) * inv + offset); } - `}};var bot=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=L().getBool("WEBGL_PACK_NORMALIZATION")?new uI(n.shape,o.shape,s.shape,c,p,u):new lI(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},t3={kernelName:hs,backendName:"webgl",kernelFunc:bot};var cI=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=zt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=wot(this.rank),o,s=t.map((i,a)=>`sourceLoc.${d1[a]} = start[${a}] + coords.${d1[a]};`);o=` + `}};var zot=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=L().getBool("WEBGL_PACK_NORMALIZATION")?new hI(n.shape,o.shape,s.shape,c,p,u):new dI(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},f3={kernelName:gs,backendName:"webgl",kernelFunc:zot};var gI=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=zt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Bot(this.rank),o,s=t.map((i,a)=>`sourceLoc.${C1[a]} = start[${a}] + coords.${C1[a]};`);o=` ${e} sourceLoc; ${e} coords = getOutputCoords(); ${s.join(` @@ -2082,7 +2086,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN ${o} setOutput(getSource(${n})); } - `}},d1=["x","y","z","w","u","v"];function wot(r){if(r===1)return"sourceLoc";if(r<=6)return d1.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var pI=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=zt(this.rank),n=er("coords",this.rank),o=er("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=` + `}},C1=["x","y","z","w","u","v"];function Bot(r){if(r===1)return"sourceLoc";if(r<=6)return C1.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var xI=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=zt(this.rank),n=er("coords",this.rank),o=er("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=` result.x = ${i}; if (++${n[this.rank-1]} < ${t[this.rank-1]}) { ++${o[this.rank-1]}; @@ -2111,7 +2115,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN ${u} setOutput(result); } - `}};function Iot(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=ze.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function vi(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=ze.parseSliceParams(o,s,i);if(ze.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=e.texData.get(o.dataId),m=WL(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=ze.isSliceContinous(o.shape,a,u);if(l||!c){let p=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new pI(u):new cI(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),Iot(o,a,u,e)}var e3={kernelName:Vi,backendName:"webgl",kernelFunc:vi};var Cot=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=rt({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:c}}),x=vi({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},r3={kernelName:Di,backendName:"webgl",kernelFunc:Cot};function vot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=Uw(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var n3={kernelName:Da,backendName:"webgl",kernelFunc:vot};function Sot(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var o3={kernelName:Yl,backendName:"webgl",kernelFunc:Sot};var Not="return float(a != b);",h1=ue({opSnippet:Not,cpuKernelImpl:FL,dtype:"bool"}),s3={kernelName:Ja,backendName:"webgl",kernelFunc:h1};function Gl(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.real},backend:e})}var i3={kernelName:jp,backendName:"webgl",kernelFunc:Gl};var kot="return float(int(x));";function a3(r,t){let e=new zr(r.shape,kot),n=t.runWebGLProgram(e,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function g1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return rr({inputs:{x:o},backend:e});let i=Te(o.shape),a=g1({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),u=$n({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype==="complex64"){let i=Gl({inputs:{input:o},backend:e}),a=g1({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!y.hasEncodingLoss(o.dtype,s)){let i=rr({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(e.shouldExecuteOnCPU([o])){let i=e.texData.get(o.dataId).values,[a,u,l]=dL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s==="int32")return a3(o,e);if(s==="bool"){let i=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),u=h1({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var l3={kernelName:fo,backendName:"webgl",kernelFunc:g1};var u3="return ceil(x);",Tot=wt({opSnippet:u3,packedOpSnippet:u3,cpuKernelImpl:hL}),c3={kernelName:ts,backendName:"webgl",kernelFunc:Tot};var mI=class{constructor(t){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` + `}};function Vot(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=ze.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function Si(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=ze.parseSliceParams(o,s,i);if(ze.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=e.texData.get(o.dataId),m=rz(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=ze.isSliceContinous(o.shape,a,u);if(l||!c){let p=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xI(u):new gI(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),Vot(o,a,u,e)}var d3={kernelName:Gi,backendName:"webgl",kernelFunc:Si};var Got=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=rt({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Si({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},h3={kernelName:$i,backendName:"webgl",kernelFunc:Got};function Wot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=Yw(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var g3={kernelName:Da,backendName:"webgl",kernelFunc:Wot};function Uot(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var x3={kernelName:Yl,backendName:"webgl",kernelFunc:Uot};var Hot="return float(a != b);",v1=ue({opSnippet:Hot,cpuKernelImpl:KL,dtype:"bool"}),y3={kernelName:Ja,backendName:"webgl",kernelFunc:v1};function Gl(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.real},backend:e})}var b3={kernelName:jp,backendName:"webgl",kernelFunc:Gl};var qot="return float(int(x));";function w3(r,t){let e=new zr(r.shape,qot),n=t.runWebGLProgram(e,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function S1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return rr({inputs:{x:o},backend:e});let i=Te(o.shape),a=S1({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),u=Fn({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype==="complex64"){let i=Gl({inputs:{input:o},backend:e}),a=S1({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!y.hasEncodingLoss(o.dtype,s)){let i=rr({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(e.shouldExecuteOnCPU([o])){let i=e.texData.get(o.dataId).values,[a,u,l]=TL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s==="int32")return w3(o,e);if(s==="bool"){let i=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),u=v1({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var I3={kernelName:ho,backendName:"webgl",kernelFunc:S1};var C3="return ceil(x);",Kot=wt({opSnippet:C3,packedOpSnippet:C3,cpuKernelImpl:_L}),v3={kernelName:es,backendName:"webgl",kernelFunc:Kot};var yI=class{constructor(t){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` void main() { float value = getAAtOutCoords(); @@ -2122,7 +2126,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN setOutput(clamp(value, minVal, maxVal)); } - `}};var fI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` + `}};var bI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` void main() { vec4 value = getAAtOutCoords(); @@ -2133,7 +2137,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function _ot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;L().getBool("WEBGL_PACK_CLIP")?a=new fI(o.shape):a=new mI(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var p3={kernelName:ho,backendName:"webgl",kernelFunc:_ot};var dI=class{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=` + `}};function jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;L().getBool("WEBGL_PACK_CLIP")?a=new bI(o.shape):a=new yI(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var S3={kernelName:go,backendName:"webgl",kernelFunc:jot};var wI=class{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2146,7 +2150,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function m3(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function Eot(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new dI(n.shape),i=[m3(n,o.complexTensorInfos.real),m3(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var f3={kernelName:Zl,backendName:"webgl",kernelFunc:Eot};var hI=class{constructor(t){this.outputShape=[],this.outputShape=S.computeOutShape(t,1),this.variableNames=t.map((i,a)=>`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h= ${u[h-1]}) { return getChannel( - getT${h}(${gI(a,l,g)}), - vec2(${gI(c,l,g)})); + getT${h}(${CI(a,l,g)}), + vec2(${CI(c,l,g)})); }`}let f=u.length,d=u[u.length-1];m+=` return getChannel( - getT${f}(${gI(a,l,d)}), - vec2(${gI(c,l,d)}));`,this.userCode=` + getT${f}(${CI(a,l,d)}), + vec2(${CI(c,l,d)}));`,this.userCode=` float getValue(${a.map(h=>"int "+h)}) { ${m} } @@ -2192,7 +2196,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(result); } - `}};function gI(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function bp(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var d3={kernelName:Gp,backendName:"webgl",kernelFunc:bp};function Md(r,t,e){let n=r[0].dtype;if(n==="complex64"){let f=r.map(b=>Gl({inputs:{input:b},backend:e})),d=r.map(b=>bp({inputs:{input:b},backend:e})),h=Md(f,t,e),g=Md(d,t,e),x=$n({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n==="string"&&(o=!0),o){let f=r.map(I=>{let E=[-1,y.sizeFromShape(I.shape.slice(t))];return rt({inputs:{x:I},backend:e,attrs:{shape:E}})}),d=f.map(I=>({vals:e.readSync(I.dataId),shape:I.shape})),h=S.computeOutShape(f.map(I=>I.shape),1),g=f[0].shape[0]===1,x=gL(d,h,n,g),b=S.computeOutShape(r.map(I=>I.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let f=i?new zr(r[0].shape,Ia):new Dn(r[0].shape,Ia);return e.runWebGLProgram(f,r,n)}let a=L().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>a){let f=[];for(let h=0;hd.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=Aot(s,t,e),c=new hI(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=rt({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function Aot(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>rt({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function x1(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?rr({inputs:{x:u[0]},backend:e}):Md(u,s,e)}var h3={kernelName:$i,backendName:"webgl",kernelFunc:x1};var Ld=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,w=g?3:1,I="",N="";n&&(o?I=`float activation(float a) { + `}};function CI(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function bp(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var T3={kernelName:Gp,backendName:"webgl",kernelFunc:bp};function Ld(r,t,e){let n=r[0].dtype;if(n==="complex64"){let f=r.map(b=>Gl({inputs:{input:b},backend:e})),d=r.map(b=>bp({inputs:{input:b},backend:e})),h=Ld(f,t,e),g=Ld(d,t,e),x=Fn({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n==="string"&&(o=!0),o){let f=r.map(I=>{let E=[-1,y.sizeFromShape(I.shape.slice(t))];return rt({inputs:{x:I},backend:e,attrs:{shape:E}})}),d=f.map(I=>({vals:e.readSync(I.dataId),shape:I.shape})),h=S.computeOutShape(f.map(I=>I.shape),1),g=f[0].shape[0]===1,x=EL(d,h,n,g),b=S.computeOutShape(r.map(I=>I.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let f=i?new zr(r[0].shape,Ia):new Rn(r[0].shape,Ia);return e.runWebGLProgram(f,r,n)}let a=L().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>a){let f=[];for(let h=0;hd.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=Yot(s,t,e),c=new II(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=rt({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function Yot(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>rt({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function N1(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?rr({inputs:{x:u[0]},backend:e}):Ld(u,s,e)}var _3={kernelName:Ri,backendName:"webgl",kernelFunc:N1};var zd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,w=g?3:1,I="",N="";n&&(o?I=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:s?I=`float activation(float a) { @@ -2326,7 +2330,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN ${N} setOutput(result); } - `}},yI=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=` + `}},SI=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${s}, ${i}, ${a}); const ivec3 pads = ivec3(${e}, ${n}, ${o}); @@ -2414,7 +2418,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(dotProd); } - `}};var zd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=` + `}};var Bd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function II({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat==="channelsLast",d=!1,h=!1,g,x=[];if(s!=null){let I=wI(s.shape,f);I!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:I}}),x.push(s))}if(o!=null){let I=wI(o.shape,f);I!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:I}}),x.push(o))}if(!((p===1||m===1)&&c>f1)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let I=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,I,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(qu(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let D=yp({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get(D.dataId);y.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=E,F.shape=e.outShape,g=rr({inputs:{x:D},backend:n}),g.shape=e.outShape,x.push(D)}else{let I=e.outHeight*e.outWidth,N=rt({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,I,e.inChannels]:[e.batchSize,e.inChannels,I]}}),E=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=yp({a:f?N:E,b:f?E:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=rt({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(E),x.push(A)}for(let I of x)n.disposeIntermediateTensorInfo(I);return g}function CI({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f==="channelsLast",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,I=[];if(s!=null){let Z=wI(s.shape,d);Z!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:Z}}),I.push(s))}if(o!=null){let Z=wI(o.shape,d);Z!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:Z}}),I.push(o))}let N=rt({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});I.push(N);let E=new bI(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],D=n.runWebGLProgram(E,[r],"float32",A),F=rt({inputs:{x:D},backend:n,attrs:{shape:x}});I.push(D),I.push(F);let P=o!=null,V=s!=null,G=a==="leakyrelu",W=a?Vl(a,!0):null,q=new Pd(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,P,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));H.push(Z),I.push(Z)}let K=n.runWebGLProgram(q,H,"float32"),X=rt({inputs:{x:K},backend:n,attrs:{shape:e.outShape}});I.push(K);for(let Z of I)n.disposeIntermediateTensorInfo(Z);return X}function Dot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,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=II({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p==="channelsLast"&&L().getBool("WEBGL_EXP_CONV")){let h=new zd(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],"float32",g)}else if(L().getBool("WEBGL_CONV_IM2COL"))f=CI({x:o,filter:s,convInfo:m,backend:e});else{let h=new Ld(m);f=e.runWebGLProgram(h,[o,s],"float32")}let d=rt({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var g3={kernelName:es,backendName:"webgl",kernelFunc:Dot};var vI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat==="channelsLast";this.userCode=` + `}};function kI(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function TI({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat==="channelsLast",d=!1,h=!1,g,x=[];if(s!=null){let I=kI(s.shape,f);I!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:I}}),x.push(s))}if(o!=null){let I=kI(o.shape,f);I!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:I}}),x.push(o))}if(!((p===1||m===1)&&c>I1)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let I=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,I,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(Ku(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let D=yp({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get(D.dataId);y.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=E,F.shape=e.outShape,g=rr({inputs:{x:D},backend:n}),g.shape=e.outShape,x.push(D)}else{let I=e.outHeight*e.outWidth,N=rt({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,I,e.inChannels]:[e.batchSize,e.inChannels,I]}}),E=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=yp({a:f?N:E,b:f?E:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=rt({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(E),x.push(A)}for(let I of x)n.disposeIntermediateTensorInfo(I);return g}function _I({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f==="channelsLast",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,I=[];if(s!=null){let Z=kI(s.shape,d);Z!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:Z}}),I.push(s))}if(o!=null){let Z=kI(o.shape,d);Z!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:Z}}),I.push(o))}let N=rt({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});I.push(N);let E=new NI(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],D=n.runWebGLProgram(E,[r],"float32",A),F=rt({inputs:{x:D},backend:n,attrs:{shape:x}});I.push(D),I.push(F);let P=o!=null,V=s!=null,G=a==="leakyrelu",W=a?Vl(a,!0):null,q=new Md(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,P,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));H.push(Z),I.push(Z)}let K=n.runWebGLProgram(q,H,"float32"),X=rt({inputs:{x:K},backend:n,attrs:{shape:e.outShape}});I.push(K);for(let Z of I)n.disposeIntermediateTensorInfo(Z);return X}function Zot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,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=TI({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p==="channelsLast"&&L().getBool("WEBGL_EXP_CONV")){let h=new Bd(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],"float32",g)}else if(L().getBool("WEBGL_CONV_IM2COL"))f=_I({x:o,filter:s,convInfo:m,backend:e});else{let h=new zd(m);f=e.runWebGLProgram(h,[o,s],"float32")}let d=rt({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var E3={kernelName:rs,backendName:"webgl",kernelFunc:Zot};var EI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2694,7 +2698,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(dotProd); } - `}},SI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat==="channelsLast",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=` + `}},AI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat==="channelsLast",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=` const ivec2 pads = ivec2(${a}, ${u}); void main() { @@ -2747,7 +2751,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(dotProd); } - `}},NI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=` + `}},DI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2789,7 +2793,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(dotProd); } - `}},kI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=` + `}},$I=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${u}, ${l}, ${c}); void main() { @@ -2846,7 +2850,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(dotProd); } - `}};function $ot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new vI(m);return e.runWebGLProgram(f,[o,s],"float32")}var x3={kernelName:Pp,backendName:"webgl",kernelFunc:$ot};var TI=class{constructor(t){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=t.inShape,this.enableShapeUniforms=de(this.outputShape.length);let e=t.filterHeight,n=t.filterWidth,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left;this.userCode=` + `}};function Jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new EI(m);return e.runWebGLProgram(f,[o,s],"float32")}var A3={kernelName:Pp,backendName:"webgl",kernelFunc:Jot};var RI=class{constructor(t){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=t.inShape,this.enableShapeUniforms=de(this.outputShape.length);let e=t.filterHeight,n=t.filterWidth,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left;this.userCode=` const ivec2 pads = ivec2(${o}, ${s}); void main() { @@ -2920,17 +2924,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(result); } - `}};function Rot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p);if(L().getBool("WEBGL_PACK")&&p==="channelsLast"){let f=[[m.strideHeight,m.strideWidth]],d=new TI(m);return e.runWebGLProgram(d,[o,s],"float32",f)}else{let f=new SI(m);return e.runWebGLProgram(f,[o,s],"float32")}}var y3={kernelName:rs,backendName:"webgl",kernelFunc:Rot};function Fot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new yI(l);return e.runWebGLProgram(c,[o,s],"float32")}var b3={kernelName:ns,backendName:"webgl",kernelFunc:Fot};function Oot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new NI(l);return e.runWebGLProgram(c,[o,s],"float32")}var w3={kernelName:$a,backendName:"webgl",kernelFunc:Oot};function Pot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new kI(l);return e.runWebGLProgram(c,[o,s],"float32")}var I3={kernelName:Ra,backendName:"webgl",kernelFunc:Pot};var Mot=zo+` + `}};function Qot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p);if(L().getBool("WEBGL_PACK")&&p==="channelsLast"){let f=[[m.strideHeight,m.strideWidth]],d=new RI(m);return e.runWebGLProgram(d,[o,s],"float32",f)}else{let f=new AI(m);return e.runWebGLProgram(f,[o,s],"float32")}}var D3={kernelName:ns,backendName:"webgl",kernelFunc:Qot};function tst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new SI(l);return e.runWebGLProgram(c,[o,s],"float32")}var $3={kernelName:os,backendName:"webgl",kernelFunc:tst};function est(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new DI(l);return e.runWebGLProgram(c,[o,s],"float32")}var R3={kernelName:$a,backendName:"webgl",kernelFunc:est};function rst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new $I(l);return e.runWebGLProgram(c,[o,s],"float32")}var F3={kernelName:Ra,backendName:"webgl",kernelFunc:rst};var nst=Bo+` return cos(x); -`,Lot=` +`,ost=` vec4 result = cos(x); bvec4 isNaN = isnan(x); - ${jn} + ${Yn} return result; -`,zot=wt({opSnippet:Mot,packedOpSnippet:Lot}),C3={kernelName:os,backendName:"webgl",kernelFunc:zot};var Bot=` +`,sst=wt({opSnippet:nst,packedOpSnippet:ost}),O3={kernelName:ss,backendName:"webgl",kernelFunc:sst};var ist=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,Vot=wt({opSnippet:Bot}),v3={kernelName:ss,backendName:"webgl",kernelFunc:Vot};var _I=class{constructor(t,e,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o==="bilinear"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,I,N]=m>1?[`${(u-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` +`,ast=wt({opSnippet:ist}),P3={kernelName:is,backendName:"webgl",kernelFunc:ast};var FI=class{constructor(t,e,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o==="bilinear"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,I,N]=m>1?[`${(u-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(${w}); void main() { @@ -2991,20 +2995,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN setOutput(newValue); } } - `}};var Got=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new _I(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],"float32")},S3={kernelName:Oa,backendName:"webgl",kernelFunc:Got};var wp;(function(r){r.Prod="*",r.Sum="+"})(wp||(wp={}));var pg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,i=this.op===wp.Prod?"1.0":"0.0",a=n?i:`getX(${N3(s,"coords",this.op)})`,u=this.outputShape[this.outputShape.length-1],l="",c="";n?(l=o?`end != ${u-1}`:"end != 0",c=o?"end + 1":"end - 1"):(l=o?`end + pow2 < ${u}`:"end >= pow2",c=o?"end + pow2":"end - pow2"),this.userCode=` + `}};var lst=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new FI(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],"float32")},M3={kernelName:Oa,backendName:"webgl",kernelFunc:lst};var wp;(function(r){r.Prod="*",r.Sum="+"})(wp||(wp={}));var hg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,i=this.op===wp.Prod?"1.0":"0.0",a=n?i:`getX(${L3(s,"coords",this.op)})`,u=this.outputShape[this.outputShape.length-1],l="",c="";n?(l=o?`end != ${u-1}`:"end != 0",c=o?"end + 1":"end - 1"):(l=o?`end + pow2 < ${u}`:"end >= pow2",c=o?"end + pow2":"end - pow2"),this.userCode=` void main() { ${zt(s)} coords = getOutputCoords(); - int end = ${k3(s,"coords",this.op)}; + int end = ${z3(s,"coords",this.op)}; float val = ${a}; int pow2 = int(pow(2.0, index)); if (${l}) { int idx = ${c}; - ${k3(s,"coords",this.op)} = idx; - val ${this.op}= getX(${N3(s,"coords",this.op)}); + ${z3(s,"coords",this.op)} = idx; + val ${this.op}= getX(${L3(s,"coords",this.op)}); } setOutput(val); } - `}};function N3(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function k3(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function EI(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=rr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new pg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new pg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function Wot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return EI(wp.Prod,o,e,s,i,a)}var T3={kernelName:Fa,backendName:"webgl",kernelFunc:Wot};function Uot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return EI(wp.Sum,o,e,s,i,a)}var _3={kernelName:is,backendName:"webgl",kernelFunc:Uot};function Hot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Uw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=fL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var E3={kernelName:Jl,backendName:"webgl",kernelFunc:Hot};var AI=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=` + `}};function L3(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function z3(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function OI(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=rr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new hg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new hg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function ust(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return OI(wp.Prod,o,e,s,i,a)}var B3={kernelName:Fa,backendName:"webgl",kernelFunc:ust};function cst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return OI(wp.Sum,o,e,s,i,a)}var V3={kernelName:as,backendName:"webgl",kernelFunc:cst};function pst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Yw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=kL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var G3={kernelName:Jl,backendName:"webgl",kernelFunc:pst};var PI=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3023,7 +3027,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN 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 qot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=new AI(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var A3={kernelName:Pa,backendName:"webgl",kernelFunc:qot};var Bd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l="",c="";n&&(o?l=`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 mst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=new PI(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var W3={kernelName:Pa,backendName:"webgl",kernelFunc:mst};var Vd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l="",c="";n&&(o?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:s?l=`float activation(float a) { @@ -3076,7 +3080,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN ${c} setOutput(result); } - `}};var Vd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=` + `}};var Gd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;L().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new Vd(p):m=new Bd(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],"float32",f)}var D3={kernelName:as,backendName:"webgl",kernelFunc:Kot};var DI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=` + `}};function fst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u,dimRoundingMode:l}=n,c=u;c==null&&(c=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;L().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new Gd(p):m=new Vd(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],"float32",f)}var U3={kernelName:ls,backendName:"webgl",kernelFunc:fst};var MI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -3300,7 +3304,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(dotProd); } - `}},$I=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=` + `}},LI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=` const ivec2 pads = ivec2(${i}, ${a}); void main() { @@ -3345,13 +3349,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(dotProd); } - `}};function jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new DI(p);return e.runWebGLProgram(m,[o,s],"float32")}var $3={kernelName:Mp,backendName:"webgl",kernelFunc:jot};function Xot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new $I(p);return e.runWebGLProgram(m,[o,s],"float32")}var R3={kernelName:Lp,backendName:"webgl",kernelFunc:Xot};var RI=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=` + `}};function dst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new MI(p);return e.runWebGLProgram(m,[o,s],"float32")}var H3={kernelName:Mp,backendName:"webgl",kernelFunc:dst};function hst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new LI(p);return e.runWebGLProgram(m,[o,s],"float32")}var q3={kernelName:Lp,backendName:"webgl",kernelFunc:hst};var zI=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function Yot(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=rt({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new RI(s),u=e.runWebGLProgram(a,[i],i.dtype),l=rt({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var F3={kernelName:Ql,backendName:"webgl",kernelFunc:Yot};var FI=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=` + `}};function gst(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=rt({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new zI(s),u=e.runWebGLProgram(a,[i],i.dtype),l=rt({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var K3={kernelName:Ql,backendName:"webgl",kernelFunc:gst};var BI=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=` const ivec2 strides = ivec2(${s}, ${i}); const ivec2 pads = ivec2(${p}, ${m}); const float neg_infinity = -3.4e38; @@ -3389,7 +3393,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN float result = curVal; setOutput(result); } - `}};function Zot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c,p=new FI(l);c=e.runWebGLProgram(p,[o,s],"float32");let m=rt({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var O3={kernelName:ls,backendName:"webgl",kernelFunc:Zot};function Jot(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h=0&&(m=xp({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var P3={kernelName:zp,backendName:"webgl",kernelFunc:Jot};var Qot="return (x >= 0.0) ? x : (exp(x) - 1.0);",tst=` + `}};function xst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c,p=new BI(l);c=e.runWebGLProgram(p,[o,s],"float32");let m=rt({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var j3={kernelName:us,backendName:"webgl",kernelFunc:xst};function yst(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h=0&&(m=xp({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var X3={kernelName:zp,backendName:"webgl",kernelFunc:yst};var bst="return (x >= 0.0) ? x : (exp(x) - 1.0);",wst=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3398,12 +3402,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,est=wt({opSnippet:Qot,packedOpSnippet:tst}),M3={kernelName:cs,backendName:"webgl",kernelFunc:est};var rst="return (b >= 0.0) ? a : a * (b + 1.0);",nst=` +`,Ist=wt({opSnippet:bst,packedOpSnippet:wst}),Y3={kernelName:ps,backendName:"webgl",kernelFunc:Ist};var Cst="return (b >= 0.0) ? a : a * (b + 1.0);",vst=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,ost=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Lo(nst,n.shape,o.shape):new po(rst,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},L3={kernelName:Ma,backendName:"webgl",kernelFunc:ost};var sst=` +`,Sst=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new zo(vst,n.shape,o.shape):new mo(Cst,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},Z3={kernelName:Ma,backendName:"webgl",kernelFunc:Sst};var Nst=` return vec4(equal(a, b)); -`,ist="return float(a == b);",ast=ue({opSnippet:ist,packedOpSnippet:sst,dtype:"bool",cpuKernelImpl:xL}),z3={kernelName:za,backendName:"webgl",kernelFunc:ast};var lst=` +`,kst="return float(a == b);",Tst=ue({opSnippet:kst,packedOpSnippet:Nst,dtype:"bool",cpuKernelImpl:AL}),J3={kernelName:za,backendName:"webgl",kernelFunc:Tst};var _st=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -3418,9 +3422,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN 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)); -`,ust=wt({opSnippet:lst}),B3={kernelName:La,backendName:"webgl",kernelFunc:ust};var cst=zo+` +`,Est=wt({opSnippet:_st}),Q3={kernelName:La,backendName:"webgl",kernelFunc:Est};var Ast=Bo+` return exp(x); -`,pst=` +`,Dst=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -3429,7 +3433,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN result.a = isNaN.a ? x.a : result.a; return result; -`,y1=wt({opSnippet:cst,packedOpSnippet:pst,cpuKernelImpl:yL,dtype:"float32"}),V3={kernelName:ps,backendName:"webgl",kernelFunc:y1};function OI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var G3={kernelName:Ri,backendName:"webgl",kernelFunc:OI};var W3="return exp(x) - 1.0;",mst=wt({opSnippet:W3,packedOpSnippet:W3,cpuKernelImpl:bL}),U3={kernelName:ms,backendName:"webgl",kernelFunc:mst};var mg=class{constructor(t,e,n){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:"1.0",a;if(t==="real")a="return real * expR - imag * expI;";else if(t==="imag")a="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=` +`,k1=wt({opSnippet:Ast,packedOpSnippet:Dst,cpuKernelImpl:DL,dtype:"float32"}),tB={kernelName:ms,backendName:"webgl",kernelFunc:k1};function VI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var eB={kernelName:Fi,backendName:"webgl",kernelFunc:VI};var rB="return exp(x) - 1.0;",$st=wt({opSnippet:rB,packedOpSnippet:rB,cpuKernelImpl:$L}),nB={kernelName:fs,backendName:"webgl",kernelFunc:$st};var gg=class{constructor(t,e,n){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:"1.0",a;if(t==="real")a="return real * expR - imag * expI;";else if(t==="imag")a="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=` const float exponentMultiplier = ${s}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -3462,12 +3466,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function PI(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=rt({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new mg("real",u,t),c=new mg("imag",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=$n({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=rt({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function fst(r){let{inputs:t,backend:e}=r,{input:n}=t;return PI(n,!1,e)}var H3={kernelName:Bp,backendName:"webgl",kernelFunc:fst};var MI=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=` + `}};function GI(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=rt({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new gg("real",u,t),c=new gg("imag",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Fn({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=rt({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function Rst(r){let{inputs:t,backend:e}=r,{input:n}=t;return GI(n,!1,e)}var oB={kernelName:Bp,backendName:"webgl",kernelFunc:Rst};var WI=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function Wl(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s==="string"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new MI(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var q3={kernelName:ru,backendName:"webgl",kernelFunc:Wl};var LI=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=` + `}};function Wl(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s==="string"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new WI(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var sB={kernelName:ru,backendName:"webgl",kernelFunc:Wl};var UI=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; @@ -3481,7 +3485,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(outputValue); } - `}};var K3={kernelName:Ba,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new LI(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var j3="return floor(x);",dst=wt({opSnippet:j3,packedOpSnippet:j3,cpuKernelImpl:wL}),X3={kernelName:fs,backendName:"webgl",kernelFunc:dst};var hst=` + `}};var iB={kernelName:Ba,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new UI(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var aB="return floor(x);",Fst=wt({opSnippet:aB,packedOpSnippet:aB,cpuKernelImpl:RL}),lB={kernelName:ds,backendName:"webgl",kernelFunc:Fst};var Ost=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3491,7 +3495,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } else { return NAN; } -`,gst=` +`,Pst=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -3512,7 +3516,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,xst=ue({opSnippet:hst,packedOpSnippet:gst,dtype:"int32"}),Y3={kernelName:ds,backendName:"webgl",kernelFunc:xst};var zI=class{constructor(t){this.variableNames=["A"];let e=We(),[n,o]=t;this.outputShape=t,this.userCode=` +`,Mst=ue({opSnippet:Ost,packedOpSnippet:Pst,dtype:"int32"}),uB={kernelName:hs,backendName:"webgl",kernelFunc:Mst};var HI=class{constructor(t){this.variableNames=["A"];let e=We(),[n,o]=t;this.outputShape=t,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3534,7 +3538,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN setOutput(floor(value * 255.0 + 0.5)); } - `}};var BI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=We(),[n,o]=t;this.outputShape=t,this.userCode=` + `}};var qI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=We(),[n,o]=t;this.outputShape=t,this.userCode=` void main() { ivec3 coords = getOutputCoords(); 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-`,Lst=ue({opSnippet:Pst,packedOpSnippet:Mst,cpuKernelImpl:NL,dtype:"bool"}),lB={kernelName:Wa,backendName:"webgl",kernelFunc:Lst};var zst="return float(a <= b);",Bst=` +`,oit=ue({opSnippet:rit,packedOpSnippet:nit,cpuKernelImpl:LL,dtype:"bool"}),IB={kernelName:Wa,backendName:"webgl",kernelFunc:oit};var sit="return float(a <= b);",iit=` return vec4(lessThanEqual(a, b)); -`,Vst=ue({opSnippet:zst,packedOpSnippet:Bst,cpuKernelImpl:kL,dtype:"bool"}),uB={kernelName:Ua,backendName:"webgl",kernelFunc:Vst};function Gst(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=TL(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var cB={kernelName:Ha,backendName:"webgl",kernelFunc:Gst};var Wst=zo+` +`,ait=ue({opSnippet:sit,packedOpSnippet:iit,cpuKernelImpl:zL,dtype:"bool"}),CB={kernelName:Ua,backendName:"webgl",kernelFunc:ait};function lit(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=BL(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var vB={kernelName:Ha,backendName:"webgl",kernelFunc:lit};var uit=Bo+` return x < 0.0 ? 0./0. : log(x); -`,Ust=` +`,cit=` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); @@ -3608,18 +3612,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; -`,Hst=wt({opSnippet:Wst,packedOpSnippet:Ust,cpuKernelImpl:_L}),pB={kernelName:Is,backendName:"webgl",kernelFunc:Hst};var qst=zo+` +`,pit=wt({opSnippet:uit,packedOpSnippet:cit,cpuKernelImpl:VL}),SB={kernelName:Cs,backendName:"webgl",kernelFunc:pit};var mit=Bo+` return log(1.0 + x); -`,Kst=wt({opSnippet:qst}),mB={kernelName:Cs,backendName:"webgl",kernelFunc:Kst};var jst="return float(a >= 1.0 && b >= 1.0);",Xst=` +`,fit=wt({opSnippet:mit}),NB={kernelName:vs,backendName:"webgl",kernelFunc:fit};var dit="return float(a >= 1.0 && b >= 1.0);",hit=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,Yst=ue({opSnippet:jst,packedOpSnippet:Xst,dtype:"bool"}),fB={kernelName:qa,backendName:"webgl",kernelFunc:Yst};var Zst="return float(!(x >= 1.0));",Jst=wt({opSnippet:Zst}),dB={kernelName:Ka,backendName:"webgl",kernelFunc:Jst};var Qst="return float(a >= 1.0 || b >= 1.0);",tit=` +`,git=ue({opSnippet:dit,packedOpSnippet:hit,dtype:"bool"}),kB={kernelName:qa,backendName:"webgl",kernelFunc:git};var xit="return float(!(x >= 1.0));",yit=wt({opSnippet:xit}),TB={kernelName:Ka,backendName:"webgl",kernelFunc:yit};var bit="return float(a >= 1.0 || b >= 1.0);",wit=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,eit=ue({opSnippet:Qst,packedOpSnippet:tit,dtype:"bool"}),hB={kernelName:ja,backendName:"webgl",kernelFunc:eit};var WI=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=` +`,Iit=ue({opSnippet:bit,packedOpSnippet:wit,dtype:"bool"}),_B={kernelName:ja,backendName:"webgl",kernelFunc:Iit};var XI=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3638,7 +3642,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN float val = x * ${u}; setOutput(val); } - `}};var UI=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=` + `}};var YI=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; @@ -3700,7 +3704,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN vec4 result = xAtOutputCoords * ${u}; setOutput(result); } - `}};var rit=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=L().getBool("WEBGL_PACK_NORMALIZATION")?new UI(o.shape,s,i,a,u):new WI(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},gB={kernelName:vs,backendName:"webgl",kernelFunc:rit};var HI=class{constructor(t,e,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=` + `}};var Cit=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=L().getBool("WEBGL_PACK_NORMALIZATION")?new YI(o.shape,s,i,a,u):new XI(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},EB={kernelName:Ss,backendName:"webgl",kernelFunc:Cit};var ZI=class{constructor(t,e,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3755,16 +3759,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(result); } - `}};var nit=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new HI(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},xB={kernelName:Xa,backendName:"webgl",kernelFunc:nit};function yB(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Xn(a,r.dtype,"max",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function I1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,I=new Array(a);for(let A=0;A{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new ZI(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},AB={kernelName:Xa,backendName:"webgl",kernelFunc:vit};function DB(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Zn(a,r.dtype,"max",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function E1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,I=new Array(a);for(let A=0;A`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new Ci(c,"max",!1);return e.runWebGLProgram(p,[o],o.dtype)}var IB={kernelName:ks,backendName:"webgl",kernelFunc:ait};function lit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new Xu(p,"max",!1);return e.runWebGLProgram(m,[o],o.dtype)}var CB={kernelName:Oi,backendName:"webgl",kernelFunc:lit};var qI=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=` +`,kit=ue({opSnippet:Sit,packedOpSnippet:Nit,cpuKernelImpl:WL}),RB={kernelName:ks,backendName:"webgl",kernelFunc:kit};function Tit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;Ii(o,"maxPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new vi(c,"max",!1);return e.runWebGLProgram(p,[o],o.dtype)}var FB={kernelName:Ts,backendName:"webgl",kernelFunc:Tit};function _it(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new Yu(p,"max",!1);return e.runWebGLProgram(m,[o],o.dtype)}var OB={kernelName:Pi,backendName:"webgl",kernelFunc:_it};var JI=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=` const ivec2 pads = ivec2(${a}, ${u}); void main() { @@ -3810,7 +3814,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(dotProd); } - `}},KI=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=` + `}},QI=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=` const ivec3 pads = ivec3(${p}, ${m}, ${f}); void main() { @@ -3874,16 +3878,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN } setOutput(dotProd); } - `}};function uit(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new Xu(m,"max",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new KI(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var vB={kernelName:nu,backendName:"webgl",kernelFunc:uit};function cit(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;wi([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new Ci(m,"max",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new qI(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var SB={kernelName:Wp,backendName:"webgl",kernelFunc:cit};function NB(r,t,e,n){let o=new Ci(e,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new Ci(e,"max",!0,!0,t);let i=n.runWebGLProgram(o,[r],"float32");return[s,i]}var kB={kernelName:Up,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. 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int end = ${a}; @@ -3912,7 +3916,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN ${s} coords = outC - start; setOutput(getX(${u})); } - `}};var XI=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((d,h)=>d[0]+t[h]+d[1]);let o=t.length,s=zt(o),i=e.map(d=>d[0]).join(","),a=e.map((d,h)=>d[0]+t[h]).join(","),u=er("rc",o),l=er("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=` + `}};var eC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((d,h)=>d[0]+t[h]+d[1]);let o=t.length,s=zt(o),i=e.map(d=>d[0]).join(","),a=e.map((d,h)=>d[0]+t[h]).join(","),u=er("rc",o),l=er("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=` ${s} source = rc; if (source < start) { source = start * 2 - source - ${m}; @@ -3968,13 +3972,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN ${f} setOutput(result); } - `}};var hit=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{paddings:o,mode:s}=e,i=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XI(n.shape,o,s):new jI(n.shape,o,s);return t.runWebGLProgram(i,[n],n.dtype)},DB={kernelName:As,backendName:"webgl",kernelFunc:hit};var git=`if (b == 0.0) return NAN; - return mod(a, b);`,xit=` + `}};var Oit=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{paddings:o,mode:s}=e,i=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eC(n.shape,o,s):new tC(n.shape,o,s);return t.runWebGLProgram(i,[n],n.dtype)},UB={kernelName:Ds,backendName:"webgl",kernelFunc:Oit};var Pit=`if (b == 0.0) return NAN; + return mod(a, b);`,Mit=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - `+jn+` + `+Yn+` return result; -`,yit=ue({opSnippet:git,packedOpSnippet:xit}),$B={kernelName:Ya,backendName:"webgl",kernelFunc:yit};var YI=class{constructor(t,e,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,n],this.userCode=` +`,Lit=ue({opSnippet:Pit,packedOpSnippet:Mit}),HB={kernelName:Ya,backendName:"webgl",kernelFunc:Lit};var rC=class{constructor(t,e,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,n],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -3994,11 +3998,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelN // If no other event happened, last event happened. setOutput(float(${e-1})); } - `}};var bit=` + `}};var zit=` if (a == b) { return 1.0; }; -return a / b;`,wit=` +return a / b;`,Bit=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -4016,9 +4020,9 @@ return a / b;`,wit=` } return result; -`,C1=ue({opSnippet:bit,packedOpSnippet:wit,checkOutOfBounds:!0}),RB={kernelName:us,backendName:"webgl",kernelFunc:C1};var FB="return a - b;",v1=ue({opSnippet:FB,packedOpSnippet:FB,supportsComplex:!0,cpuKernelImpl:JL}),OB={kernelName:ti,backendName:"webgl",kernelFunc:v1};function S1(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=y.parseAxisParam([s],o.shape),a=I1({inputs:{x:o},backend:e,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(a.shape,i),l=rt({inputs:{x:a},backend:e,attrs:{shape:u}}),c=v1({inputs:{a:o,b:l},backend:e}),p=y1({inputs:{x:c},backend:e}),m=xp({inputs:{x:p},backend:e,attrs:{axis:i,keepDims:!1}}),f=rt({inputs:{x:m},backend:e,attrs:{shape:u}}),d=C1({inputs:{a:p,b:f},backend:e});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}var PB={kernelName:Js,backendName:"webgl",kernelFunc:S1};function Iit(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:S1({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new YI(l,c,s),m=[[i]],f=e.runWebGLProgram(p,[u],"int32",m);return a||e.disposeIntermediateTensorInfo(u),f}var MB={kernelName:Za,backendName:"webgl",kernelFunc:Iit};var Cit=yr+` +`,A1=ue({opSnippet:zit,packedOpSnippet:Bit,checkOutOfBounds:!0}),qB={kernelName:cs,backendName:"webgl",kernelFunc:A1};var KB="return a - b;",D1=ue({opSnippet:KB,packedOpSnippet:KB,supportsComplex:!0,cpuKernelImpl:pz}),jB={kernelName:ei,backendName:"webgl",kernelFunc:D1};function $1(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=y.parseAxisParam([s],o.shape),a=E1({inputs:{x:o},backend:e,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(a.shape,i),l=rt({inputs:{x:a},backend:e,attrs:{shape:u}}),c=D1({inputs:{a:o,b:l},backend:e}),p=k1({inputs:{x:c},backend:e}),m=xp({inputs:{x:p},backend:e,attrs:{axis:i,keepDims:!1}}),f=rt({inputs:{x:m},backend:e,attrs:{shape:u}}),d=A1({inputs:{a:p,b:f},backend:e});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}var XB={kernelName:Qs,backendName:"webgl",kernelFunc:$1};function Vit(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:$1({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new rC(l,c,s),m=[[i]],f=e.runWebGLProgram(p,[u],"int32",m);return a||e.disposeIntermediateTensorInfo(u),f}var YB={kernelName:Za,backendName:"webgl",kernelFunc:Vit};var Git=yr+` return -x; -`,vit=` +`,Wit=` vec4 result = -x; bvec4 isNaN = isnan(x); @@ -4028,14 +4032,14 @@ return a / b;`,wit=` result.a = isNaN.a ? x.a : result.a; return result; -`;function Sit(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=RL(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Dn(n.shape,vit):o=new zr(n.shape,Cit),e.runWebGLProgram(o,[n],n.dtype)}var LB={kernelName:Pi,backendName:"webgl",kernelFunc:Sit};var Nit=Kr.nonMaxSuppressionV3Impl;function kit(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),{selectedIndices:p}=Nit(l,c,i,a,u);return e.makeTensorInfo([p.length],"int32",new Int32Array(p))}var zB={kernelName:Qa,backendName:"webgl",kernelFunc:kit};var Tit=Kr.nonMaxSuppressionV4Impl;function _it(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,padToMaxOutputSize:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=Tit(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([f]))]}var BB={kernelName:tl,backendName:"webgl",kernelFunc:_it};var Eit=Kr.nonMaxSuppressionV5Impl;function Ait(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,softNmsSigma:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),m=i,f=a,d=u,h=l,{selectedIndices:g,selectedScores:x}=Eit(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var VB={kernelName:el,backendName:"webgl",kernelFunc:Ait};var ZI=class{constructor(t,e,n,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=` +`;function Uit(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=qL(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Rn(n.shape,Wit):o=new zr(n.shape,Git),e.runWebGLProgram(o,[n],n.dtype)}var ZB={kernelName:Mi,backendName:"webgl",kernelFunc:Uit};var Hit=Kr.nonMaxSuppressionV3Impl;function qit(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),{selectedIndices:p}=Hit(l,c,i,a,u);return e.makeTensorInfo([p.length],"int32",new Int32Array(p))}var JB={kernelName:Qa,backendName:"webgl",kernelFunc:qit};var Kit=Kr.nonMaxSuppressionV4Impl;function jit(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,padToMaxOutputSize:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=Kit(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([f]))]}var QB={kernelName:tl,backendName:"webgl",kernelFunc:jit};var Xit=Kr.nonMaxSuppressionV5Impl;function Yit(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,softNmsSigma:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),m=i,f=a,d=u,h=l,{selectedIndices:g,selectedScores:x}=Xit(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var tV={kernelName:el,backendName:"webgl",kernelFunc:Yit};var nC=class{constructor(t,e,n,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${o}), float(${n}), float(index == coords.y))); } - `}};var Dit=r=>{let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n,l=y.sizeFromShape(o.shape),c=new ZI(l,i,a,u),p=rt({inputs:{x:o},backend:e,attrs:{shape:[l]}}),m=e.runWebGLProgram(c,[p],s);e.disposeIntermediateTensorInfo(p);let f=[...o.shape,i],d=rt({inputs:{x:m},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(m),d},GB={kernelName:$s,backendName:"webgl",kernelFunc:Dit};function fg(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="complex64"){let o=Gl({inputs:{input:n},backend:e}),s=fg({inputs:{x:o},backend:e}),i=bp({inputs:{input:n},backend:e}),a=fg({inputs:{x:i},backend:e}),u=$n({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Wl({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:e})}var WB={kernelName:Hi,backendName:"webgl",kernelFunc:fg};function UB(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=Gl({inputs:{input:n},backend:e}),s=UB({inputs:{x:o},backend:e}),i=bp({inputs:{input:n},backend:e}),a=fg({inputs:{x:i},backend:e}),u=$n({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Wl({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:e})}var HB={kernelName:Mi,backendName:"webgl",kernelFunc:UB};function $it(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return OI({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=OI({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=x1({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var qB={kernelName:Li,backendName:"webgl",kernelFunc:$it};var JI=class{constructor(t,e,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((l,c)=>l[0]+t[c]+l[1]);let o=t.length,s=zt(o),i=e.map(l=>l[0]).join(","),a=e.map((l,c)=>l[0]+t[c]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=` + `}};var Zit=r=>{let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n,l=y.sizeFromShape(o.shape),c=new nC(l,i,a,u),p=rt({inputs:{x:o},backend:e,attrs:{shape:[l]}}),m=e.runWebGLProgram(c,[p],s);e.disposeIntermediateTensorInfo(p);let f=[...o.shape,i],d=rt({inputs:{x:m},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(m),d},eV={kernelName:Rs,backendName:"webgl",kernelFunc:Zit};function xg(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="complex64"){let o=Gl({inputs:{input:n},backend:e}),s=xg({inputs:{x:o},backend:e}),i=bp({inputs:{input:n},backend:e}),a=xg({inputs:{x:i},backend:e}),u=Fn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Wl({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:e})}var rV={kernelName:qi,backendName:"webgl",kernelFunc:xg};function nV(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=Gl({inputs:{input:n},backend:e}),s=nV({inputs:{x:o},backend:e}),i=bp({inputs:{input:n},backend:e}),a=xg({inputs:{x:i},backend:e}),u=Fn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Wl({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:e})}var oV={kernelName:Li,backendName:"webgl",kernelFunc:nV};function Jit(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return VI({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=VI({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=N1({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var sV={kernelName:zi,backendName:"webgl",kernelFunc:Jit};var oC=class{constructor(t,e,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((l,c)=>l[0]+t[c]+l[1]);let o=t.length,s=zt(o),i=e.map(l=>l[0]).join(","),a=e.map((l,c)=>l[0]+t[c]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=` int start = ${i}; int end = ${a}; @@ -4060,7 +4064,7 @@ return a / b;`,wit=` setOutput(getX(${u})); } } - `}};var QI=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=zt(o),i=e.map(h=>h[0]).join(","),a=e.map((h,g)=>h[0]+t[g]).join(","),u=er("rc",o),l=er("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1; + `}};var sC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=zt(o),i=e.map(h=>h[0]).join(","),a=e.map((h,g)=>h[0]+t[g]).join(","),u=er("rc",o),l=er("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1; if(${c}) { `,o===1?"":`} rc = outputLoc; @@ -4084,7 +4088,7 @@ return a / b;`,wit=` ${d} setOutput(result); } - `}};var N1=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Wl({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new QI(o.shape,s,i):new JI(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},KB={kernelName:Rs,backendName:"webgl",kernelFunc:N1};var Rit=` + `}};var R1=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Wl({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sC(o.shape,s,i):new oC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},iV={kernelName:Fs,backendName:"webgl",kernelFunc:R1};var Qit=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4093,7 +4097,7 @@ return a / b;`,wit=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,Fit=` +`,tat=` // 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); @@ -4109,11 +4113,11 @@ return a / b;`,wit=` bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); - `+jn+` + `+Yn+` return result; 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XB={kernelName:Ps,backendName:"webgl",kernelFunc:Pit};function Mit(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=PL(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var YB={kernelName:Hp,backendName:"webgl",kernelFunc:Mit};function Lit(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=ML(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],"int32",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var ZB={kernelName:qp,backendName:"webgl",kernelFunc:Lit};function 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sat(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=ZL(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var pV={kernelName:Kp,backendName:"webgl",kernelFunc:sat};var F1=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=JL(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},mV={kernelName:ou,backendName:"webgl",kernelFunc:F1};var iat="return 1.0 / x;",aat=wt({opSnippet:iat}),fV={kernelName:Ls,backendName:"webgl",kernelFunc:aat};var lat=yr+` return (x < 0.0) ? 0.0 : x; -`,Wit=` +`,uat=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4123,9 +4127,9 @@ return a / b;`,wit=` result.a = isNaN.a ? x.a : result.a; return result; -`,Uit=wt({opSnippet:Git,packedOpSnippet:Wit}),eV={kernelName:Ls,backendName:"webgl",kernelFunc:Uit};var Hit=yr+` +`,cat=wt({opSnippet:lat,packedOpSnippet:uat}),dV={kernelName:zs,backendName:"webgl",kernelFunc:cat};var pat=yr+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,qit=` +`,mat=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4135,7 +4139,7 @@ return a / b;`,wit=` result.a = isNaN.a ? x.a : result.a; return result; -`,Kit=wt({opSnippet:Hit,packedOpSnippet:qit}),rV={kernelName:Vs,backendName:"webgl",kernelFunc:Kit};var tC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,fat=wt({opSnippet:pat,packedOpSnippet:mat}),hV={kernelName:Gs,backendName:"webgl",kernelFunc:fat};var iC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],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]}); @@ -4168,7 +4172,7 @@ return a / b;`,wit=` setOutput(newValue); } - `}};var eC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};var aC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],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]}, @@ -4245,7 +4249,7 @@ return a / b;`,wit=` setOutput(newValue); } - `}};function jit(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new eC(o.shape,u,l,s,i):new tC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],"float32")}var nV={kernelName:Bs,backendName:"webgl",kernelFunc:jit};var rC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=` + `}};function dat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new aC(o.shape,u,l,s,i):new iC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],"float32")}var gV={kernelName:Vs,backendName:"webgl",kernelFunc:dat};var lC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[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]; @@ -4326,7 +4330,7 @@ return a / b;`,wit=` setOutput(accumulator); } - `}};function Xit(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new rC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var oV={kernelName:nl,backendName:"webgl",kernelFunc:Xit};var nC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function hat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new lC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var xV={kernelName:nl,backendName:"webgl",kernelFunc:hat};var uC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + 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vec3(0.0))":f="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/p[0]}, ${c[1]/p[1]}, @@ -4389,7 +4393,7 @@ return a / b;`,wit=` setOutput(newValue); } - `}};function Yit(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new oC(o.shape,u,l,s,i):new nC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var sV={kernelName:zs,backendName:"webgl",kernelFunc:Yit};var sC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=` + `}};function gat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new cC(o.shape,u,l,s,i):new uC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var yV={kernelName:Bs,backendName:"webgl",kernelFunc:gat};var pC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4459,7 +4463,7 @@ return a / b;`,wit=` setOutput(accumulator); } - `}};function Zit(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new sC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var iV={kernelName:rl,backendName:"webgl",kernelFunc:Zit};var iC=class{constructor(t,e){this.variableNames=["x"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=` + `}};function xat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new pC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var bV={kernelName:rl,backendName:"webgl",kernelFunc:xat};var mC=class{constructor(t,e){this.variableNames=["x"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${t[0]} - coord - 1)); @@ -4469,7 +4473,7 @@ return a / b;`,wit=` ${i} coords = getOutputCoords(); setOutput(getX(${s})); } - `}};var aC=class{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=er("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=zt(n);n===1?this.userCode=` + `}};var fC=class{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=er("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=zt(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -4497,7 +4501,7 @@ return a / b;`,wit=` } setOutput(result); } - `;function u(d){return m(d)}function l(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function Jit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return rr({inputs:{x:o},backend:e});let u=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new aC(o.shape,a):new iC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var aV={kernelName:Gs,backendName:"webgl",kernelFunc:Jit};var lC=class{constructor(t,e){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=t[1],o=t[2];this.outputShape=t;let s="";typeof e=="number"?s=`float outputValue = ${e.toFixed(2)};`:s=` + `;function u(d){return m(d)}function l(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function yat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return rr({inputs:{x:o},backend:e});let u=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fC(o.shape,a):new mC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var wV={kernelName:Ws,backendName:"webgl",kernelFunc:yat};var dC=class{constructor(t,e){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=t[1],o=t[2];this.outputShape=t;let s="";typeof e=="number"?s=`float outputValue = ${e.toFixed(2)};`:s=` vec3 fill = vec3(${e.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -4516,7 +4520,7 @@ return a / b;`,wit=` } setOutput(outputValue); } - `}};var lV={kernelName:fl,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new lC(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var Qit=` + `}};var IV={kernelName:fl,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new dC(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var bat=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4531,7 +4535,7 @@ return a / b;`,wit=` return base + 1.0; } } -`,tat=wt({opSnippet:Qit}),uV={kernelName:Ws,backendName:"webgl",kernelFunc:tat};var eat="return inversesqrt(x);",rat=wt({opSnippet:eat,cpuKernelImpl:BL}),cV={kernelName:Us,backendName:"webgl",kernelFunc:rat};var Yu=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${p})`,f="";o===1?f="i":o===2&&(f="i, coords[1]");let d=`getUpdates(${f})`,h="";u&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides";this.userCode=` +`,wat=wt({opSnippet:bat}),CV={kernelName:Us,backendName:"webgl",kernelFunc:wat};var Iat="return inversesqrt(x);",Cat=wt({opSnippet:Iat,cpuKernelImpl:QL}),vV={kernelName:Hs,backendName:"webgl",kernelFunc:Cat};var Zu=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${p})`,f="";o===1?f="i":o===2&&(f="i, coords[1]");let d=`getUpdates(${f})`,h="";u&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides";this.userCode=` ${l} strides = ${l}(${s}); void main() { @@ -4551,7 +4555,7 @@ return a / b;`,wit=` } setOutput(mix(${g}, sum, float(found))); } - `}};var uC=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${p})`,f="";o===1?f="i":o===2&&(f="i, coords[1]");let d=`getUpdates(${f})`,h="";u&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides",b=e>1?"strides[j + 1]":"strides";this.userCode=` + `}};var hC=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${p})`,f="";o===1?f="i":o===2&&(f="i, coords[1]");let d=`getUpdates(${f})`,h="";u&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides",b=e>1?"strides[j + 1]":"strides";this.userCode=` ${l} strides = ${l}(${s}); void main() { @@ -4588,7 +4592,7 @@ return a / b;`,wit=` } setOutput(mix(${g}, sum, found)); } - `}};function nat(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=rt({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],"float32",new Float32Array([0])),g;L().getBool("WEBGL_PACK")?g=new uC(u,a,f.shape.length,d.shape.length,c,m):g=new Yu(u,a,f.shape.length,d.shape.length,c,m);let x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var pV={kernelName:ol,backendName:"webgl",kernelFunc:nat};var cC=class{constructor(t,e,n,o){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[t,n];let s="while (left < right) {",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,a=L().getNumber("WEBGL_VERSION")===2?s:i,u=o==="left"?"<":"<=";this.userCode=` + `}};function vat(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=rt({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],"float32",new Float32Array([0])),g;L().getBool("WEBGL_PACK")?g=new hC(u,a,f.shape.length,d.shape.length,c,m):g=new Zu(u,a,f.shape.length,d.shape.length,c,m);let x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var SV={kernelName:ol,backendName:"webgl",kernelFunc:vat};var gC=class{constructor(t,e,n,o){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[t,n];let s="while (left < right) {",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,a=L().getNumber("WEBGL_VERSION")===2?s:i,u=o==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; @@ -4613,7 +4617,7 @@ return a / b;`,wit=` setOutput(float(findBound(batch, value))); } - `}};function oat(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=new cC(o.shape[0],o.shape[1],s.shape[1],i),u=[[o.shape[1]]];return e.runWebGLProgram(a,[o,s],"int32",u)}var mV={kernelName:il,backendName:"webgl",kernelFunc:oat};var pC=class{constructor(t,e,n){this.variableNames=["c","a","b"],this.outputShape=e;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],u=[],l=[];for(let c=0;c4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],u=[],l=[];for(let c=0;c= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,aat=wt({opSnippet:iat}),dV={kernelName:Hs,backendName:"webgl",kernelFunc:aat};var lat=zo+` +`,Tat=wt({opSnippet:kat}),TV={kernelName:qs,backendName:"webgl",kernelFunc:Tat};var _at=Bo+` return 1.0 / (1.0 + exp(-1.0 * x)); -`,uat=` +`,Eat=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -4641,20 +4645,20 @@ return a / b;`,wit=` result.a = isNaN.a ? x.a : result.a; return result; -`,cat=wt({opSnippet:lat,packedOpSnippet:uat,cpuKernelImpl:GL}),hV={kernelName:js,backendName:"webgl",kernelFunc:cat};var pat=` +`,Aat=wt({opSnippet:_at,packedOpSnippet:Eat,cpuKernelImpl:ez}),_V={kernelName:Xs,backendName:"webgl",kernelFunc:Aat};var Dat=` if (isnan(x)) { return 0.0; } return sign(x); -`,mat=wt({opSnippet:pat}),gV={kernelName:Ks,backendName:"webgl",kernelFunc:mat};var fat=zo+` +`,$at=wt({opSnippet:Dat}),EV={kernelName:js,backendName:"webgl",kernelFunc:$at};var Rat=Bo+` return sin(x); -`,dat=` +`,Fat=` vec4 result = sin(x); bvec4 isNaN = isnan(x); - ${jn} + ${Yn} return result; -`,hat=wt({opSnippet:fat,packedOpSnippet:dat}),xV={kernelName:qs,backendName:"webgl",kernelFunc:hat};var gat=` +`,Oat=wt({opSnippet:Rat,packedOpSnippet:Fat}),AV={kernelName:Ks,backendName:"webgl",kernelFunc:Oat};var Pat=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,xat=wt({opSnippet:gat}),yV={kernelName:al,backendName:"webgl",kernelFunc:xat};var yat=` +`,Mat=wt({opSnippet:Pat}),DV={kernelName:al,backendName:"webgl",kernelFunc:Mat};var Lat=` float epsilon = 1.1920928955078125e-7; float threshold = 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MV={kernelName:au,backendName:"webgl",kernelFunc:Uat};function Hat(r){let{inputs:t,backend:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=t,{outputShape:a}=n,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=S.calculateShapes(s,o,a),f=!1;if(s.dtype==="string"){let x=e.bufferSync(o),b=e.bufferSync(s),w=y.decodeString(e.readSync(i.dataId)[0]),I=tz(x,b,a,m,c,l,u,p,w,f);return e.makeTensorInfo(a,I.dtype,I.values)}let d=new Zu(l,u,o.shape.length,s.shape.length,p,[m,1],f),h=e.runWebGLProgram(d,[s,o,i],s.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(h),g}var LV={kernelName:ul,backendName:"webgl",kernelFunc:Hat};function qat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=Si({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return 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mC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=zt(n.length),i=zt(n.length),a="";if(o===1)a="coords * strides + begin";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=` + `,s=new zr(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var qV={kernelName:yo,backendName:"webgl",kernelFunc:Jat};var yC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=zt(n.length),i=zt(n.length),a="";if(o===1)a="coords * strides + begin";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=` ${s} begin = ${s}(${t}); ${s} strides = ${s}(${e}); @@ -4692,15 +4696,15 @@ return a / b;`,wit=` ${i} coords = getOutputCoords(); setOutput(getX(${a})); } - `}};function 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x=GV(s),b=GV(c),w=null,I=()=>w===null?[g,g]:[g,w],N=(P,V,G)=>{let W=I(),q=new dC(G),K=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[P],[V]],X=w;w=e.runWebGLProgram(q,W,"int32",K),Ip(e,X)};for(let P=1;P=1;G/=2)N(V,G,[h,b])}for(let P=b;P>x;P/=2){let V=I(),G=new hC([h,P/2]),q=[[c],[w===null?1:0],[x]],H=w;w=e.runWebGLProgram(G,V,"int32",q),Ip(e,H);let K=x/2,X=K*2;for(let Z=K;Z>=1;Z/=2)N(X,Z,w.shape)}let E=w;w=vi({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),Ip(e,E);let A=w1({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});Ip(e,g);let D=l.slice(0,-1);D.push(s),E=w,w=rt({inputs:{x:w},attrs:{shape:D},backend:e}),Ip(e,E);let F=A;return A=rt({inputs:{x:A},attrs:{shape:D},backend:e}),Ip(e,F),[A,w]}var WV={kernelName:pl,backendName:"webgl",kernelFunc:Wat};var gC=class{constructor(t,e,n,o,s,i){this.variableNames=["Image","Transforms"],this.outputShape=i;let 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V=I(),G=new IC([h,P/2]),q=[[c],[w===null?1:0],[x]],H=w;w=e.runWebGLProgram(G,V,"int32",q),Ip(e,H);let K=x/2,X=K*2;for(let Z=K;Z>=1;Z/=2)N(X,Z,w.shape)}let E=w;w=Si({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),Ip(e,E);let A=_1({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});Ip(e,g);let D=l.slice(0,-1);D.push(s),E=w,w=rt({inputs:{x:w},attrs:{shape:D},backend:e}),Ip(e,E);let F=A;return A=rt({inputs:{x:A},attrs:{shape:D},backend:e}),Ip(e,F),[A,w]}var rG={kernelName:pl,backendName:"webgl",kernelFunc:ult};var CC=class{constructor(t,e,n,o,s,i){this.variableNames=["Image","Transforms"],this.outputShape=i;let a=n==="nearest"?1:2,u;switch(o){case"constant":u=1;break;case"reflect":u=2;break;case"wrap":u=3;break;case"nearest":u=4;break;default:u=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; if(${u} == 2) { @@ -4886,7 +4890,7 @@ return a / b;`,wit=` } setOutput(outputValue); } - `}};function 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xC=class{constructor(t,e){this.variableNames=["x","segmentIds"];let n=t.windowSize,o=t.batchSize,s=t.inSize,i=t.numSegments,a=i*Math.ceil(s/n);this.outputShape=[o,a];let u="0.0",l="sumValue",c=Math.floor(n/4)*4,p=n%4,m=` + `}};function clt(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new CC(p,m,i,a,u,g);return e.runWebGLProgram(x,[o,s],"float32")}var nG={kernelName:ml,backendName:"webgl",kernelFunc:clt};function plt(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;Ii(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:a,outputShape:u,indices:l}=dz(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],"int32",l)]}var oG={kernelName:mu,backendName:"webgl",kernelFunc:plt};function 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YG={kernelName:Fa,backendName:"wasm",setupFunc:Plt,kernelFunc:Mlt};var ZG;function Llt(r){ZG=r.wasm.cwrap(is,null,["number","number","number","number","number","number"])}function zlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype==="float32"||o.dtype==="int32",()=>`cumsum does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=mo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumsum",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;ZG(d,i?1:0,a?1:0,f,h,kt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var JG={kernelName:is,backendName:"wasm",setupFunc:Llt,kernelFunc:zlt};var QG;function 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Hlt(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,I=f.dilationHeight,N=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,D=f.inChannels,F=f.outChannels,P=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. 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Got ${o.dtype} and ${s.dtype}`);let l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c=e.makeOutput(l.outShape,o.dtype);return aW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,kt[o.dtype],l.batchSize,l.inChannels,l.inHeight,l.inWidth,l.outHeight,l.outWidth,l.strideHeight,l.strideWidth,l.dilationHeight,l.dilationWidth,l.filterHeight,l.filterWidth,l.padInfo.top,l.padInfo.left),c}var lW={kernelName:ls,backendName:"wasm",setupFunc:jlt,kernelFunc:Xlt};var uW;function Ylt(r){uW=r.wasm.cwrap(eu,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Zlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,"NHWC",l),p=e.makeOutput(s.shape,s.dtype);return uW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,kt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var cW={kernelName:eu,backendName:"wasm",setupFunc:Ylt,kernelFunc:Zlt};var pW;function Jlt(r){pW=r.wasm.cwrap(tu,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,"NHWC",l),p=e.makeOutput(o.shape,o.dtype);return pW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,kt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var mW={kernelName:tu,backendName:"wasm",setupFunc:Jlt,kernelFunc:Qlt};var fW=Ct(cs);var dW;function tut(r){dW=r.wasm.cwrap(Ma,null,["number","number","number"])}function eut(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=e.makeOutput(o.shape,"float32"),i=a=>e.dataIdMap.get(a.dataId).id;return dW(i(o),i(n),i(s)),s}var hW={kernelName:Ma,backendName:"wasm",setupFunc:tut,kernelFunc:eut};var rut=!1,gW=ae(za,rut,"bool");var xW=Ct(ps,"float32");function bC(r){let{inputs:t,attrs:e,backend:n}=r,{input:o}=t,{dim:s}=e,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),pr({inputs:{x:o},backend:n,attrs:{shape:a}})}var yW={kernelName:Ri,backendName:"wasm",kernelFunc:bC};var bW=Ct(ms,"float32");function A1(r){let{attrs:{shape:t,value:e,dtype:n},backend:o}=r,s=o.makeOutput(t,n);return o.typedArrayFromHeap(s).fill(e),s}var wW={kernelName:ru,backendName:"wasm",kernelFunc:A1};var IW;function nut(r){IW=r.wasm.cwrap(Ba,null,["number","number","number","number","number","number"])}function out(r){let{inputs:t,backend:e}=r,{image:n}=t,o=e.makeOutput(n.shape,n.dtype),s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,[a,u,l,c]=n.shape;return IW(s,a,u,l,c,i),o}var CW={kernelName:Ba,backendName:"wasm",kernelFunc:out,setupFunc:nut};var vW=Ct(fs);var sut=!1,SW=ae(ds,sut);var NW;function iut(r){NW=r.wasm.cwrap(hs,null,["number","number","number","number","number","number","number"])}function aut(r){let{backend:t,inputs:e,attrs:n}=r,{varianceEpsilon:o}=n,{x:s,mean:i,variance:a,offset:u,scale:l}=e,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,m=t.dataIdMap.get(a.dataId).id,f=u!=null?t.dataIdMap.get(u.dataId).id:0,d=l!=null?t.dataIdMap.get(l.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return NW(c,p,m,f,d,o,g),h}var kW={kernelName:hs,backendName:"wasm",setupFunc:iut,kernelFunc:aut};var TW;function lut(r){TW=r.wasm.cwrap(Ki,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function uut(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m),g=Zu[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,P=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,K=h.padInfo.type==="SAME"?1:0,X=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,"float32"),st=n.dataIdMap.get(nt.dataId).id,at=a==null?0:n.dataIdMap.get(a.dataId).id;return TW(x,X,Z,et,b,N,E,I,A,D,F,P,K,V,G,W,q,H,w,g,at,d||0,st),nt}var _W={kernelName:Ki,backendName:"wasm",setupFunc:lut,kernelFunc:uut};var EW;function cut(r){EW=r.wasm.cwrap(ji,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 put(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m,!0),g=Zu[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,P=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,K=h.padInfo.type==="SAME"?1:0,X=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,"float32"),st=n.dataIdMap.get(nt.dataId).id,at=a==null?0:n.dataIdMap.get(a.dataId).id;return EW(x,X,Z,et,b,N,E,I,A,D,F,P,K,V,G,W,q,H,w,g,at,d||0,st),nt}var AW={kernelName:ji,backendName:"wasm",setupFunc:cut,kernelFunc:put};var DW;function mut(r){DW=r.wasm.cwrap(Va,null,["number","number","number","number","number","number","array","number"])}function fut(r){let{backend:t,inputs:e}=r,{params:n,indices:o}=e,[s,i,a,u]=Ny.prepareAndValidate(n,o),l=t.makeOutput(s,n.dtype);if(i===0)return l;let c=o.shape,p=c[c.length-1],f=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(o.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),x=t.dataIdMap.get(l.dataId).id;return DW(f,kt[n.dtype],h,i,p,a,g,x),l}var $W={kernelName:Va,backendName:"wasm",setupFunc:mut,kernelFunc:fut};var RW;function dut(r){RW=r.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function hut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,indices:s}=e,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0],l=t.readSync(s.dataId),c=o.shape[u];for(let F=0;F=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),m=pr({inputs:{x:o},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),f=y.sizeFromShape(s.shape),d=pr({inputs:{x:s},attrs:{shape:[p.batchSize,f/p.batchSize]},backend:t}),h=[p.batchSize,p.outerSize,f/p.batchSize,p.sliceSize],g=t.makeOutput(h,o.dtype);if(y.sizeFromShape(o.shape)===0)return g;let x=m.shape.length-1,w=t.dataIdMap.get(m.dataId).id,N=t.dataIdMap.get(d.dataId).id,E=t.dataIdMap.get(g.dataId).id,A=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),D=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return RW(w,kt[o.dtype],A,x,N,p.batchSize,D,E),t.disposeData(m.dataId),t.disposeData(d.dataId),g.shape=p.outputShape,g}var FW={kernelName:Fi,backendName:"wasm",setupFunc:dut,kernelFunc:hut};var gut=!1,OW=ae(Ga,gut,"bool");var xut=!1,PW=ae(gs,xut,"bool");var MW=Ct(xs,"bool");var LW=Ct(ys,"bool");var zW=Ct(bs,"bool");var BW;function yut(r){BW=r.wasm.cwrap(ws,null,["number","number","number","number"])}function but(r){let{inputs:{x:t},attrs:{alpha:e},backend:n}=r,o=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(y.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;BW(o,kt[t.dtype],e,i)}return s}var VW={kernelName:ws,backendName:"wasm",setupFunc:yut,kernelFunc:but};var wut=!1,GW=ae(Wa,wut,"bool");var Iut=!1,WW=ae(Ua,Iut,"bool");var UW;function Cut(r){UW=r.wasm.cwrap(Ha,null,["number","number","number","number"])}function vut(r){let{attrs:t,backend:e}=r,{start:n,stop:o,num:s}=t,i=Math.floor(s),a=e.makeOutput([i],"float32");return UW(e.dataIdMap.get(a.dataId).id,n,o,i),a}var HW={kernelName:Ha,backendName:"wasm",setupFunc:Cut,kernelFunc:vut};var qW=Ct(Is);var KW=Ct(Cs);var Sut=!1,jW=ae(qa,Sut,"bool");var XW=Ct(Ka);var Nut=!1,YW=ae(ja,Nut,"bool");var kut=!1,ZW=ae(l_,kut,"bool");var JW;function Tut(r){JW=r.wasm.cwrap(vs,null,["number","number","number","number","number","number","number"])}function _ut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;if(o.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let l=e.makeOutput(o.shape,o.dtype);return JW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(l.dataId).id,o.shape[3],s,i,a,u),l}var QW={kernelName:vs,backendName:"wasm",setupFunc:Tut,kernelFunc:_ut};var tU;function Eut(r){tU=r.wasm.cwrap(Xa,null,["number","number","number","number","number","number","number","number","number"])}function Aut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n;if(o.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let p=e.makeOutput(o.shape,o.dtype);return tU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,i.shape[3],a,u,l,c),p}var eU={kernelName:Xa,backendName:"wasm",setupFunc:Eut,kernelFunc:Aut};var rU;function Dut(r){rU=r.wasm.cwrap(Ss,null,["number","number","number","number"])}function $ut(r){let{backend:t,inputs:e,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=In(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims("max",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;rU(u,kt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var nU={kernelName:Ss,backendName:"wasm",setupFunc:Dut,kernelFunc:$ut};var Rut=!1,oU=ae(Ns,Rut);var sU;function Fut(r){sU=r.wasm.cwrap(ks,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Out(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id;y.assert(o.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${o.dtype}.`);let{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=e,c=S.computePool2DInfo(o.shape,i,a,1,u,l),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,w=c.strideHeight,I=c.strideWidth,N=c.inChannels,E=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let A=n.makeOutput(c.outShape,"float32"),D=n.dataIdMap.get(A.dataId).id;return sU(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,b,w,I,N,E,D),A}var iU={kernelName:ks,backendName:"wasm",setupFunc:Fut,kernelFunc:Out};var aU;function Put(r){aU=r.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Mut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.makeOutput(c.outShape,o.dtype);return aU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var lU={kernelName:Oi,backendName:"wasm",setupFunc:Put,kernelFunc:Mut};var uU;function Lut(r){uU=r.wasm.cwrap("MaxPool3DGrad",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 zut(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return uU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var cU={kernelName:nu,backendName:"wasm",setupFunc:Lut,kernelFunc:zut};var pU;function But(r){pU=r.wasm.cwrap(Ts,null,["number, number, number"])}function Vut(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=In(i,o,t),d=p;if(f){let I=t.dataIdMap.get(c.dataId).id;I!==a&&(l=c,u=I,d=S.getInnerMostAxes(d.length,l.shape.length))}S.assertAxesAreInnerMostDims("mean",d,l.shape.length);let[h,g]=S.computeOutAndReduceShapes(l.shape,d),x=y.sizeFromShape(g),b=l;l.dtype!=="float32"&&(b=Rn({backend:t,inputs:{x:l},attrs:{dtype:"float32"}}),u=t.dataIdMap.get(b.dataId).id);let w=t.makeOutput(h,"float32");if(y.sizeFromShape(l.shape)!==0){let I=t.dataIdMap.get(w.dataId).id;pU(u,x,I)}if(f&&t.disposeData(c.dataId),s){let I=S.expandShapeToKeepDim(w.shape,m);w.shape=I}return l.dtype!=="float32"&&t.disposeData(b.dataId),w}var mU={kernelName:Ts,backendName:"wasm",setupFunc:But,kernelFunc:Vut};var fU;function Gut(r){fU=r.wasm.cwrap(_s,null,["number","number","number","number"])}function Wut(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=In(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;w!==a&&(l=c,u=w)}let 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Ju;(function(r){r[r.linear=0]="linear",r[r.relu=1]="relu",r[r.relu6=2]="relu6",r[r.prelu=3]="prelu",r[r.leakyrelu=4]="leakyrelu",r[r.sigmoid=5]="sigmoid",r[r.elu=6]="elu"})(Ju||(Ju={}));var aG;function hlt(r){aG=r.wasm.cwrap(Ki,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function glt(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t;if(o.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=0;if(i!=null){let D=e.dataIdMap.get(i.dataId);if(D.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${D.shape.length}.`);d=D.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=Ju[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=u?o.shape[2]:o.shape[1],b=l?s.shape[1]:s.shape[2],w=Ur.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)),I=e.makeOutput([...w,x,b],o.dtype),N=e.dataIdMap.get(I.dataId).id,E=new Uint8Array(new Int32Array(o.shape).buffer),A=new Uint8Array(new Int32Array(s.shape).buffer);return aG(m,E,o.shape.length,f,A,s.shape.length,u,l,g,d,h,p||0,N),I}var lG={kernelName:Ki,backendName:"wasm",setupFunc:hlt,kernelFunc:glt};function Ct(r,t){let e;function n(s){e=s.wasm.cwrap(r,null,["number","number","number"])}function o(s){let{backend:i,inputs:{x:a}}=s,u=i.dataIdMap.get(a.dataId).id,l=i.makeOutput(a.shape,t||a.dtype),c=i.dataIdMap.get(l.dataId).id;return y.sizeFromShape(l.shape)===0||e(u,kt[a.dtype],c),l}return{kernelName:r,backendName:"wasm",setupFunc:n,kernelFunc:o}}var uG=Ct(_i);var cG=Ct(Ho);var pG=Ct(qo);function ae(r,t,e){let n;function o(i){n=i.wasm.cwrap(r,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:a,inputs:u}=i,{a:l,b:c}=u,p=a.dataIdMap.get(l.dataId).id,m=a.dataIdMap.get(c.dataId).id,f=e!=null?e:l.dtype,d=S.assertAndGetBroadcastShape(l.shape,c.shape),h=a.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(l.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=a.dataIdMap.get(h.dataId).id;return(()=>n(p,g,l.shape.length,m,x,c.shape.length,kt[l.dtype],b))(),h}return{kernelName:r,backendName:"wasm",setupFunc:o,kernelFunc:s}}var xlt=!0,mG=ae(oo,xlt);var fG;function ylt(r){fG=r.wasm.cwrap(Ko,null,["array","number","number","number"])}function blt(r){let{inputs:t,backend:e}=r,n=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(n.shape)===0)return n;let o=t.map(a=>e.dataIdMap.get(a.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),i=e.dataIdMap.get(n.dataId).id;return fG(s,o.length,kt[n.dtype],i),n}var dG={kernelName:Ko,backendName:"wasm",setupFunc:ylt,kernelFunc:blt};function Cp(r){let{inputs:{x:t},backend:e}=r;if(t.dtype==="string")return sr(e.readSync(t.dataId),t.shape,t.dtype);let n=e.makeOutput(t.shape,t.dtype),o=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(n).set(o),n}var hG={kernelName:xo,backendName:"wasm",kernelFunc:Cp};var gG;function wlt(r){gG=r.wasm.cwrap(io,null,["number","array","number","number","number","array","number"])}function fo(r){let{inputs:t,backend:e,attrs:n}=r,[o,s]=Clt(t.x.shape,n.perm),i=!0;for(let d=0;d=o&&(s===-1||n[s]>n[i])&&(s=i);n[s]=o}return[e,n]}var xG={kernelName:io,backendName:"wasm",kernelFunc:fo,setupFunc:wlt};function vn(r,t,e){let n=r.shape,o=r.shape.length,s=y.parseAxisParam(t,n),i=s,a=S.getAxesPermutation(i,o),u=null,l=!1;if(a!=null){let c=new Array(o);for(let f=0;f`new shape: ${i}, old shape: ${n.shape}. 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t.dtype==="string"?p.stringBytes=u.slice(d,d+y.sizeFromShape(i)):o.typedArrayFromHeap(l).set(u.subarray(d,d+y.sizeFromShape(i))),l}if(t.dtype==="string"){let d=sp(u,s,i,t.shape,t.dtype);return p.stringBytes=d,l}let m=o.typedArrayFromHeap(l),f=t.shape.length;if(f===2)Olt(u,c[0],m,s,i);else if(f===3)Plt(u,c[0],c[1],m,s,i);else if(f===4)Mlt(u,c[0],c[1],c[2],m,s,i);else{let d=sp(u,s,i,t.shape,t.dtype);m.set(d)}return l}function Olt(r,t,e,n,o){let s=0,i=n[0],a=n[1],u=i+o[0];for(let l=i;lx*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=mr({inputs:{x:o},backend:e,attrs:{shape:u}}),d=fo({inputs:{x:f},backend:e,attrs:{perm:l}}),h=mr({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Vo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeData(f.dataId),e.disposeData(d.dataId),e.disposeData(f.dataId),g}var zG={kernelName:$i,backendName:"wasm",kernelFunc:Llt};var BG;function zlt(r){BG=r.wasm.cwrap(Da,null,["number","number","boolean","number","number","number"])}function Blt(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i}=n,a=s.shape.reduce((p,m)=>p*m,1)!==0,u=o.shape.length===1?[i]:[o.shape[0],i],l=t.makeOutput(u,s.dtype);function c(p){return t.dataIdMap.get(p.dataId).id}return BG(c(o),i,a,c(s),kt[s.dtype],c(l)),l}var VG={kernelName:Da,backendName:"wasm",setupFunc:zlt,kernelFunc:Blt};function Vlt(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.typedArrayFromHeap(n),i=e.typedArrayFromHeap(o),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeOutput([a.length],"int32",void 0,new Int32Array(a))}var GG={kernelName:Yl,backendName:"wasm",kernelFunc:Vlt};function On(r){let{inputs:{x:t},attrs:{dtype:e},backend:n}=r,o=n.makeOutput(t.shape,e),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(o).set(s),o}var WG={kernelName:ho,backendName:"wasm",kernelFunc:On};var UG=Ct(es);var HG;function Glt(r){HG=r.wasm.cwrap(go,null,["number","number","number","number"])}function Wlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a=e.dataIdMap.get(o.dataId).id,u=e.makeOutput(o.shape,o.dtype),l=e.dataIdMap.get(u.dataId).id;return HG(a,s,i,l),u}var qG={kernelName:go,backendName:"wasm",setupFunc:Glt,kernelFunc:Wlt};function P1(r){let{inputs:t,backend:e}=r,n=y.parseAxisParam(r.attrs.axis,t[0].shape)[0],o=t.map(f=>f.shape);S.assertParamsConsistent(o,n);let s=S.computeOutShape(t.map(f=>f.shape),n),i=t.filter(f=>y.sizeFromShape(f.shape)>0);if(i.length===1)return Cp({inputs:{x:i[0]},backend:e});let a=e.makeOutput(s,t[0].dtype);if(y.sizeFromShape(s)===0)return a;if(i[0].dtype==="string"){let f=i.map(w=>{let N=[-1,y.sizeFromShape(w.shape.slice(n))];return mr({inputs:{x:w},backend:e,attrs:{shape:N}})}),d=f.map(w=>({vals:e.readSync(w.dataId),shape:w.shape}));s=S.computeOutShape(f.map(w=>w.shape),1);let h=f[0].shape[0]===1,g=rp(d,s,t[0].dtype,h),x=S.computeOutShape(i.map(w=>w.shape),n);a.shape=x;let b=e.dataIdMap.get(a.dataId);return b.stringBytes=S.fromStringArrayToUint8(g),f.forEach(w=>e.disposeData(w.dataId)),a}let u=y.sizeFromShape(i[0].shape.slice(0,n)),l=0,c=i.map(f=>{let d=y.sizeFromShape(f.shape.slice(n));return l+=d,d}),p=i.map(f=>e.typedArrayFromHeap(f)),m=e.typedArrayFromHeap(a);for(let f=0;f`cumprod does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=fo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumprod",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;lW(d,i?1:0,a?1:0,f,h,kt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=fo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var uW={kernelName:Fa,backendName:"wasm",setupFunc:rut,kernelFunc:nut};var cW;function out(r){cW=r.wasm.cwrap(as,null,["number","number","number","number","number","number"])}function sut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype==="float32"||o.dtype==="int32",()=>`cumsum does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=fo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumsum",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;cW(d,i?1:0,a?1:0,f,h,kt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=fo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var pW={kernelName:as,backendName:"wasm",setupFunc:out,kernelFunc:sut};var mW;function iut(r){mW=r.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function aut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i,binaryOutput:a}=n,u=s.shape.reduce((m,f)=>m*f,1)!==0,l=o.shape.length===1?[i]:[o.shape[0],i],c=t.makeOutput(l,s.dtype);function p(m){return t.dataIdMap.get(m.dataId).id}return mW(p(o),new Uint8Array(new Int32Array(o.shape).buffer),o.shape.length,i,u,p(s),kt[s.dtype],a,p(c)),c}var fW={kernelName:Jl,backendName:"wasm",setupFunc:iut,kernelFunc:aut};var dW;function lut(r){dW=r.wasm.cwrap(Pa,null,["number","number","number","array","number","array","array","number","number"])}function uut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,"float32"),x=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),I=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),N=t.dataIdMap.get(h.dataId).id;return dW(x,s,i==="NHWC"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var hW={kernelName:Pa,backendName:"wasm",setupFunc:lut,kernelFunc:uut};var gW;function cut(r){gW=r.wasm.cwrap(ls,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function put(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,I=f.dilationHeight,N=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,D=f.inChannels,F=f.outChannels,P=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let V=n.makeOutput(f.outShape,"float32"),G=n.dataIdMap.get(V.dataId).id;return gW(i,o.shape[0],o.shape[1],o.shape[2],a,d,h,g,x,b,w,P,I,N,E,A,D,F,G),V}var xW={kernelName:ls,backendName:"wasm",setupFunc:cut,kernelFunc:put};var yW;function mut(r){yW=r.wasm.cwrap("Diag",null,["number","number","number","number"])}function fut(r){let{inputs:t,backend:e}=r,{x:n}=t,o=y.sizeFromShape(n.shape),s=e.makeOutput([...n.shape,...n.shape],n.dtype);return yW(e.dataIdMap.get(n.dataId).id,kt[n.dtype],o,e.dataIdMap.get(s.dataId).id),s}var bW={kernelName:Ql,backendName:"wasm",setupFunc:mut,kernelFunc:fut};var wW;function dut(r){wW=r.wasm.cwrap(us,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function hut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;if(o.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. 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Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,"NHWC",l),p=e.makeOutput(s.shape,s.dtype);return CW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,kt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var vW={kernelName:eu,backendName:"wasm",setupFunc:gut,kernelFunc:xut};var SW;function yut(r){SW=r.wasm.cwrap(tu,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function but(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,"NHWC",l),p=e.makeOutput(o.shape,o.dtype);return SW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,kt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var NW={kernelName:tu,backendName:"wasm",setupFunc:yut,kernelFunc:but};var kW=Ct(ps);var TW;function wut(r){TW=r.wasm.cwrap(Ma,null,["number","number","number"])}function Iut(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=e.makeOutput(o.shape,"float32"),i=a=>e.dataIdMap.get(a.dataId).id;return TW(i(o),i(n),i(s)),s}var _W={kernelName:Ma,backendName:"wasm",setupFunc:wut,kernelFunc:Iut};var Cut=!1,EW=ae(za,Cut,"bool");var AW=Ct(ms,"float32");function NC(r){let{inputs:t,attrs:e,backend:n}=r,{input:o}=t,{dim:s}=e,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),mr({inputs:{x:o},backend:n,attrs:{shape:a}})}var DW={kernelName:Fi,backendName:"wasm",kernelFunc:NC};var $W=Ct(fs,"float32");function L1(r){let{attrs:{shape:t,value:e,dtype:n},backend:o}=r,s=o.makeOutput(t,n);return o.typedArrayFromHeap(s).fill(e),s}var RW={kernelName:ru,backendName:"wasm",kernelFunc:L1};var FW;function vut(r){FW=r.wasm.cwrap(Ba,null,["number","number","number","number","number","number"])}function Sut(r){let{inputs:t,backend:e}=r,{image:n}=t,o=e.makeOutput(n.shape,n.dtype),s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,[a,u,l,c]=n.shape;return FW(s,a,u,l,c,i),o}var OW={kernelName:Ba,backendName:"wasm",kernelFunc:Sut,setupFunc:vut};var PW=Ct(ds);var Nut=!1,MW=ae(hs,Nut);var LW;function kut(r){LW=r.wasm.cwrap(gs,null,["number","number","number","number","number","number","number"])}function Tut(r){let{backend:t,inputs:e,attrs:n}=r,{varianceEpsilon:o}=n,{x:s,mean:i,variance:a,offset:u,scale:l}=e,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,m=t.dataIdMap.get(a.dataId).id,f=u!=null?t.dataIdMap.get(u.dataId).id:0,d=l!=null?t.dataIdMap.get(l.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return LW(c,p,m,f,d,o,g),h}var zW={kernelName:gs,backendName:"wasm",setupFunc:kut,kernelFunc:Tut};var BW;function _ut(r){BW=r.wasm.cwrap(ji,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Eut(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m),g=Ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,P=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,K=h.padInfo.type==="SAME"?1:0,X=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,"float32"),st=n.dataIdMap.get(nt.dataId).id,at=a==null?0:n.dataIdMap.get(a.dataId).id;return BW(x,X,Z,et,b,N,E,I,A,D,F,P,K,V,G,W,q,H,w,g,at,d||0,st),nt}var VW={kernelName:ji,backendName:"wasm",setupFunc:_ut,kernelFunc:Eut};var GW;function Aut(r){GW=r.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 Dut(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m,!0),g=Ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,P=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,K=h.padInfo.type==="SAME"?1:0,X=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,"float32"),st=n.dataIdMap.get(nt.dataId).id,at=a==null?0:n.dataIdMap.get(a.dataId).id;return GW(x,X,Z,et,b,N,E,I,A,D,F,P,K,V,G,W,q,H,w,g,at,d||0,st),nt}var WW={kernelName:Xi,backendName:"wasm",setupFunc:Aut,kernelFunc:Dut};var UW;function $ut(r){UW=r.wasm.cwrap(Va,null,["number","number","number","number","number","number","array","number"])}function Rut(r){let{backend:t,inputs:e}=r,{params:n,indices:o}=e,[s,i,a,u]=Dy.prepareAndValidate(n,o),l=t.makeOutput(s,n.dtype);if(i===0)return l;let c=o.shape,p=c[c.length-1],f=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(o.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),x=t.dataIdMap.get(l.dataId).id;return UW(f,kt[n.dtype],h,i,p,a,g,x),l}var HW={kernelName:Va,backendName:"wasm",setupFunc:$ut,kernelFunc:Rut};var qW;function Fut(r){qW=r.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Out(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,indices:s}=e,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0],l=t.readSync(s.dataId),c=o.shape[u];for(let F=0;F=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),m=mr({inputs:{x:o},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),f=y.sizeFromShape(s.shape),d=mr({inputs:{x:s},attrs:{shape:[p.batchSize,f/p.batchSize]},backend:t}),h=[p.batchSize,p.outerSize,f/p.batchSize,p.sliceSize],g=t.makeOutput(h,o.dtype);if(y.sizeFromShape(o.shape)===0)return g;let x=m.shape.length-1,w=t.dataIdMap.get(m.dataId).id,N=t.dataIdMap.get(d.dataId).id,E=t.dataIdMap.get(g.dataId).id,A=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),D=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return qW(w,kt[o.dtype],A,x,N,p.batchSize,D,E),t.disposeData(m.dataId),t.disposeData(d.dataId),g.shape=p.outputShape,g}var KW={kernelName:Oi,backendName:"wasm",setupFunc:Fut,kernelFunc:Out};var Put=!1,jW=ae(Ga,Put,"bool");var Mut=!1,XW=ae(xs,Mut,"bool");var YW=Ct(ys,"bool");var ZW=Ct(bs,"bool");var JW=Ct(ws,"bool");var QW;function Lut(r){QW=r.wasm.cwrap(Is,null,["number","number","number","number"])}function zut(r){let{inputs:{x:t},attrs:{alpha:e},backend:n}=r,o=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(y.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;QW(o,kt[t.dtype],e,i)}return s}var tU={kernelName:Is,backendName:"wasm",setupFunc:Lut,kernelFunc:zut};var But=!1,eU=ae(Wa,But,"bool");var Vut=!1,rU=ae(Ua,Vut,"bool");var nU;function Gut(r){nU=r.wasm.cwrap(Ha,null,["number","number","number","number"])}function Wut(r){let{attrs:t,backend:e}=r,{start:n,stop:o,num:s}=t,i=Math.floor(s),a=e.makeOutput([i],"float32");return nU(e.dataIdMap.get(a.dataId).id,n,o,i),a}var oU={kernelName:Ha,backendName:"wasm",setupFunc:Gut,kernelFunc:Wut};var sU=Ct(Cs);var iU=Ct(vs);var Uut=!1,aU=ae(qa,Uut,"bool");var lU=Ct(Ka);var Hut=!1,uU=ae(ja,Hut,"bool");var qut=!1,cU=ae(g_,qut,"bool");var pU;function Kut(r){pU=r.wasm.cwrap(Ss,null,["number","number","number","number","number","number","number"])}function jut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;if(o.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let l=e.makeOutput(o.shape,o.dtype);return pU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(l.dataId).id,o.shape[3],s,i,a,u),l}var mU={kernelName:Ss,backendName:"wasm",setupFunc:Kut,kernelFunc:jut};var fU;function Xut(r){fU=r.wasm.cwrap(Xa,null,["number","number","number","number","number","number","number","number","number"])}function Yut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n;if(o.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let p=e.makeOutput(o.shape,o.dtype);return fU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,i.shape[3],a,u,l,c),p}var dU={kernelName:Xa,backendName:"wasm",setupFunc:Xut,kernelFunc:Yut};var hU;function Zut(r){hU=r.wasm.cwrap(Ns,null,["number","number","number","number"])}function Jut(r){let{backend:t,inputs:e,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=vn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims("max",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;hU(u,kt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var gU={kernelName:Ns,backendName:"wasm",setupFunc:Zut,kernelFunc:Jut};var Qut=!1,xU=ae(ks,Qut);var yU;function tct(r){yU=r.wasm.cwrap(Ts,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ect(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id;y.assert(o.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${o.dtype}.`);let{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=e,c=S.computePool2DInfo(o.shape,i,a,1,u,l),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,w=c.strideHeight,I=c.strideWidth,N=c.inChannels,E=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let A=n.makeOutput(c.outShape,"float32"),D=n.dataIdMap.get(A.dataId).id;return yU(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,b,w,I,N,E,D),A}var bU={kernelName:Ts,backendName:"wasm",setupFunc:tct,kernelFunc:ect};var wU;function rct(r){wU=r.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function nct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.makeOutput(c.outShape,o.dtype);return wU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var IU={kernelName:Pi,backendName:"wasm",setupFunc:rct,kernelFunc:nct};var CU;function oct(r){CU=r.wasm.cwrap("MaxPool3DGrad",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 sct(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return CU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var vU={kernelName:nu,backendName:"wasm",setupFunc:oct,kernelFunc:sct};var SU;function ict(r){SU=r.wasm.cwrap(_s,null,["number, number, number"])}function act(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=vn(i,o,t),d=p;if(f){let I=t.dataIdMap.get(c.dataId).id;I!==a&&(l=c,u=I,d=S.getInnerMostAxes(d.length,l.shape.length))}S.assertAxesAreInnerMostDims("mean",d,l.shape.length);let[h,g]=S.computeOutAndReduceShapes(l.shape,d),x=y.sizeFromShape(g),b=l;l.dtype!=="float32"&&(b=On({backend:t,inputs:{x:l},attrs:{dtype:"float32"}}),u=t.dataIdMap.get(b.dataId).id);let w=t.makeOutput(h,"float32");if(y.sizeFromShape(l.shape)!==0){let I=t.dataIdMap.get(w.dataId).id;SU(u,x,I)}if(f&&t.disposeData(c.dataId),s){let I=S.expandShapeToKeepDim(w.shape,m);w.shape=I}return l.dtype!=="float32"&&t.disposeData(b.dataId),w}var NU={kernelName:_s,backendName:"wasm",setupFunc:ict,kernelFunc:act};var kU;function lct(r){kU=r.wasm.cwrap(Es,null,["number","number","number","number"])}function uct(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=vn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;w!==a&&(l=c,u=w)}let d=l.shape.length;S.assertAxesAreInnerMostDims("min",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,l.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;kU(u,kt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var TU={kernelName:Es,backendName:"wasm",setupFunc:lct,kernelFunc:uct};var cct=!1,_U=ae(As,cct);var z1;(function(r){r[r.reflect=0]="reflect",r[r.symmetric=1]="symmetric"})(z1||(z1={}));var EU;function pct(r){EU=r.wasm.cwrap(Ds,null,["number","array","number","number","array","array","number","number"])}function mct(r){let{inputs:{x:t},backend:e,attrs:{paddings:n,mode:o}}=r,s=n.map((d,h)=>d[0]+t.shape[h]+d[1]),i=e.dataIdMap.get(t.dataId).id,a=e.makeOutput(s,t.dtype),u=e.dataIdMap.get(a.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=n.map(d=>d[0]),p=n.map(d=>d[1]),m=new Uint8Array(new Int32Array(c).buffer),f=new Uint8Array(new 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History,Vp as IFFT,go as Identity,Gp as Imag,Ie as InputSpec,xs as IsFinite,ys as IsInf,bs as IsNan,Go as KernelBackend,vs as LRN,Xa as LRNGrad,Eh as LayerVariable,Un as LayersModel,ws as LeakyRelu,Wa as Less,Ua as LessEqual,Ha as LinSpace,Is as Log,Cs as Log1p,u_ as LogSoftmax,qa as LogicalAnd,Ka as LogicalNot,ja as LogicalOr,l_ as LogicalXor,Ppt as LowerBound,Wu as MathBackendCPU,Ku as MathBackendWebGL,Mpt as MatrixBandPart,Ss as Max,ks as MaxPool,Oi as MaxPool3D,nu as MaxPool3DGrad,Wp as MaxPoolGrad,Up as MaxPoolWithArgmax,Ns as Maximum,Ts as Mean,_s as Min,Es as Minimum,As as MirrorPad,Ya as Mod,Ac as MomentumOptimizer,Za as Multinomial,Ds as Multiply,Pi as Neg,Qa as NonMaxSuppressionV3,tl as NonMaxSuppressionV4,el as NonMaxSuppressionV5,Ja as NotEqual,v0 as OP_SCOPE_SUFFIX,$s as OneHot,Mi as OnesLike,qr as Optimizer,vh as OptimizerConstructors,Li as Pack,Rs as PadV2,Lpt as Pool,Fs as Pow,Os as Prelu,Ps as Prod,Dc as RMSPropOptimizer,_n as RNN,Hp as RaggedGather,qp as 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this.readSync(t)}readSync(t,e,n){let{memoryOffset:o,dtype:s,shape:i,stringBytes:a}=this.dataIdMap.get(t);if(s==="string")return(e==null||e===0)&&(n==null||n>=a.length)?a:a.slice(e,n);e=e||0,n=n||y.sizeFromShape(i);let u=y.bytesPerElement(s),l=this.wasm.HEAPU8.slice(o+e*u,o+n*u);return Lpt(l.buffer,s)}disposeData(t,e=!1){if(this.dataIdMap.has(t)){let n=this.dataIdMap.get(t);if(n.refCount--,!e&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(t)}return!0}refCount(t){return this.dataIdMap.has(t)?this.dataIdMap.get(t).refCount:0}incRef(t){let e=this.dataIdMap.get(t);e!=null&&e.refCount++}floatPrecision(){return 32}getMemoryOffset(t){return this.dataIdMap.get(t).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(t,e,n,o){let s;if(n==null)s=this.write(o!=null?o:null,t,e);else{let i=this.dataIdNextNumber++;s={id:i},this.dataIdMap.set(s,{id:i,memoryOffset:n,shape:t,dtype:e,refCount:1});let a=y.sizeFromShape(t);this.wasm.tfjs.registerTensor(i,a,n)}return{dataId:s,shape:t,dtype:e}}typedArrayFromHeap({shape:t,dtype:e,dataId:n}){let o=this.wasm.HEAPU8.buffer,{memoryOffset:s}=this.dataIdMap.get(n),i=y.sizeFromShape(t);switch(e){case"float32":return new Float32Array(o,s,i);case"int32":return new Int32Array(o,s,i);case"bool":return new Uint8Array(o,s,i);default:throw new Error(`Unknown dtype ${e}`)}}};function Mpt(r){return(t,e)=>(y.fetch(r,{credentials:"same-origin"}).then(n=>{n.ok||t.env.a(`failed to load wasm binary file at '${r}'`),n.arrayBuffer().then(o=>{WebAssembly.instantiate(o,t).then(s=>{e(s.instance,s.module)})})}),{})}function CH(r,t,e){if(DC!=null)return DC;let n="tfjs-backend-wasm.wasm";return r&&t?n="tfjs-backend-wasm-threaded-simd.wasm":r&&(n="tfjs-backend-wasm-simd.wasm"),bg!=null&&bg[n]!=null?bg[n]:e+n}async function SH(){let[r,t]=await Promise.all([L().getAsync("WASM_HAS_SIMD_SUPPORT"),L().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((e,n)=>{let o={};o.locateFile=(a,u)=>{if(a.endsWith(".worker.js")){let l=vH.wasmWorkerContents.replace(/\n/g,"\\n"),c=new Blob([l],{type:"application/javascript"});return URL.createObjectURL(c)}return a.endsWith(".wasm")?CH(r,t,yg!=null?yg:u):u+a},Y1&&(o.instantiateWasm=Mpt(CH(r,t,yg!=null?yg:"")));let s=!1;o.onAbort=()=>{if(s||wg)return;wg=!0,n({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"})};let i;t&&r&&DC==null?(o.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+IH.toString()],{type:"text/javascript"}),i=IH(o)):i=Ppt(o),i.then(a=>{s=!0,wg=!1;let u=null;a.tfjs={init:a.cwrap("init",null,[]),initWithThreadsCount:a.cwrap("init_with_threads_count",null,["number"]),getThreadsCount:a.cwrap("get_threads_count","number",[]),registerTensor:a.cwrap("register_tensor",null,["number","number","number"]),disposeData:a.cwrap("dispose_data",u,["number"]),dispose:a.cwrap("dispose",u,[])},e({wasm:a})}).catch(n)})}function Lpt(r,t){switch(t){case"float32":return new Float32Array(r);case"int32":return new Int32Array(r);case"bool":return new Uint8Array(r);default:throw new Error(`Unknown dtype ${t}`)}}var zpt=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],DC=null,yg=null,bg={},wg=!1,Y1=!1;function Bpt(r,t=!1){if(B0("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),wg)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");DC=r,Y1=t}function Vpt(r,t=!1){if(wg)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof r=="string")yg=r;else{bg=r;let e=zpt.filter(n=>bg[n]==null);if(e.length>0)throw new Error(`There were no entries found for the following binaries: ${e.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}Y1=t}var NH=-1,K1=-1;function Gpt(r){NH=r}function Wpt(){if(K1===-1)throw new Error("WASM backend not initialized.");return K1}var Upt="4.5.0";var Hpt=2;sm("wasm",async()=>{let{wasm:r}=await SH();return new Ig(r)},Hpt);var kH="4.5.0",qpt="4.5.0",Kpt="4.5.0",jpt="4.5.0",Xpt="4.5.0",Ypt={tfjs:kH,"tfjs-core":kH,"tfjs-converter":qpt,"tfjs-backend-cpu":Kpt,"tfjs-backend-webgl":jpt,"tfjs-backend-wasm":Xpt};export{_i as Abs,Ho as Acos,qo as Acosh,Tc as AdadeltaOptimizer,_c as AdagradOptimizer,Ec as AdamOptimizer,Ac as AdamaxOptimizer,oo as Add,Ko as AddN,Ea as All,Aa as Any,Ei as ArgMax,Ai as ArgMin,jo as Asin,Xo as Asinh,Yo as Atan,Jo as Atan2,Zo as Atanh,Qo as AvgPool,Di as AvgPool3D,Xl as AvgPool3DGrad,Fp as AvgPoolGrad,Ig as BackendWasm,ts as BatchMatMul,$i as BatchToSpaceND,Da as Bincount,nh as BitwiseAnd,Yl as BroadcastArgs,h_ as BroadcastTo,Mb as Callback,Yy as CallbackList,ho as Cast,es as Ceil,go as ClipByValue,Op as Complex,Zl as ComplexAbs,Ri as Concat,rs as Conv2D,Pp as Conv2DBackpropFilter,ns as Conv2DBackpropInput,os as Conv3D,$a as Conv3DBackpropFilterV2,Ra as Conv3DBackpropInputV2,ss as Cos,is as Cosh,Oa as CropAndResize,Fa as Cumprod,as as Cumsum,Jy as CustomCallback,Ta as DataStorage,Jl as DenseBincount,Pa as DepthToSpace,ls as DepthwiseConv2dNative,Mp as DepthwiseConv2dNativeBackpropFilter,Lp as DepthwiseConv2dNativeBackpropInput,Ql as Diag,us as Dilation2D,eu as Dilation2DBackpropFilter,tu as Dilation2DBackpropInput,u0 as ENV,Lb as EarlyStopping,zp as Einsum,ps as Elu,Ma as EluGrad,eh as Environment,za as Equal,La as Erf,ms as Exp,Fi as ExpandDims,fs as Expm1,Bp as FFT,ru as Fill,Ba as FlipLeftRight,ds as Floor,hs as FloorDiv,oh as FromPixels,gs as FusedBatchNorm,ji as FusedConv2D,Xi as FusedDepthwiseConv2D,hp as GPGPUContext,Va as GatherNd,Oi as GatherV2,jh as GraphModel,Ga as Greater,xs as GreaterEqual,Zy as History,Vp as IFFT,xo as Identity,Gp as Imag,Ie as InputSpec,ys as IsFinite,bs as IsInf,ws as IsNan,Wo as KernelBackend,Ss as LRN,Xa as LRNGrad,Dh as LayerVariable,qn as LayersModel,Is as LeakyRelu,Wa as Less,Ua as LessEqual,Ha as LinSpace,Cs as Log,vs as Log1p,x_ as LogSoftmax,qa as LogicalAnd,Ka as LogicalNot,ja as LogicalOr,g_ as LogicalXor,rmt as LowerBound,Uu as MathBackendCPU,ju as MathBackendWebGL,nmt as MatrixBandPart,Ns as Max,Ts as MaxPool,Pi as MaxPool3D,nu as MaxPool3DGrad,Wp as MaxPoolGrad,Up as MaxPoolWithArgmax,ks as Maximum,_s as Mean,Es as Min,As as Minimum,Ds as MirrorPad,Ya as Mod,Dc as MomentumOptimizer,Za as Multinomial,$s as Multiply,Mi as Neg,Qa as NonMaxSuppressionV3,tl as NonMaxSuppressionV4,el as NonMaxSuppressionV5,Ja as NotEqual,A0 as OP_SCOPE_SUFFIX,Rs as OneHot,Li as OnesLike,qr as Optimizer,Nh as OptimizerConstructors,zi as Pack,Fs as PadV2,omt as Pool,Os as Pow,Ps as Prelu,Ms as Prod,$c as RMSPropOptimizer,An as RNN,Hp as RaggedGather,qp as RaggedRange,Kp as RaggedTensorToTensor,ou as Range,w0 as Rank,jp as Real,cs as RealDiv,Ls as Reciprocal,Ze as Reduction,zs as Relu,Gs as Relu6,Bi as Reshape,Vs as ResizeBilinear,nl as ResizeBilinearGrad,Bs as ResizeNearestNeighbor,rl as ResizeNearestNeighborGrad,Ws as Reverse,fl as RotateWithOffset,Us as Round,Hs as Rsqrt,Cl as SGDOptimizer,ol as ScatterNd,il as SearchSorted,Vi as Select,qs as Selu,xa as Sequential,Xs as Sigmoid,js as Sign,Ks as Sin,al as Sinh,Gi as Slice,Qs as Softmax,Ys as Softplus,Wi as SpaceToBatchND,su as SparseFillEmptyRows,ll as SparseReshape,iu as SparseSegmentMean,au as SparseSegmentSum,ul as SparseToDense,Ui as SplitV,Zs as Sqrt,lu as Square,ti as SquaredDifference,sc as StaticRegexReplace,yo as Step,cl as StridedSlice,uu as StringNGrams,cu as StringSplit,pu as StringToHashBucketFast,ei as Sub,Js as Sum,rn as SymbolicTensor,ri as Tan,ni as Tanh,Pt as Tensor,le as TensorBuffer,sl as TensorScatterUpdate,so as Tile,pl as TopK,ml as Transform,io as Transpose,mu as Unique,Hi as Unpack,fu as UnsortedSegmentSum,smt as UpperBound,dl as Variable,qi as ZerosLike,Ki as _FusedMatMul,Ee as abs,dx as acos,hx as acosh,Y as add,fE as addN,am as all,dc as any,ta as argMax,gx as argMin,xx as asin,yx as asinh,bx as atan,wx as atan2,Ix as atanh,bu as avgPool,Cx as avgPool3d,mE as backend,S as backend_util,gE as basicLSTMCell,na as batchNorm,vx as batchNorm2d,Sx as batchNorm3d,Nx as batchNorm4d,wu as batchToSpaceND,kx as bincount,yE as bitwiseAnd,c5 as booleanMaskAsync,bE as broadcastArgs,oa as broadcastTo,Ur as broadcast_util,Ay as browser,bt as buffer,T9 as callbacks,Q as cast,Tx as ceil,vr as clipByValue,un as clone,Nn as complex,se as concat,_x as concat1d,Ex as concat2d,Ax as concat3d,Dx as concat4d,nR as constraints,um as conv1d,kn as conv2d,pm as conv2dTranspose,$x as conv3d,Fx as conv3dTranspose,mmt as copyRegisteredKernels,Iu as cos,mm as cosh,Ih as cosineWindow,gc as cumprod,fm as cumsum,mn as customGrad,VF as data,gh as denseBincount,B0 as deprecationWarn,Ox as depthToSpace,sa as depthwiseConv2d,D9 as deregisterOp,xu as device_util,wE as diag,Px as dilation2d,vdt as disableDeprecationWarnings,Tt as dispose,Sdt as disposeVariables,ct as div,Mx as divNoNan,Lx as dot,nN as dropout,CE as einsum,ia as elu,Cdt as enableDebugMode,Idt as enableProdMode,oN as enclosingPowerOfTwo,Vn as engine,vE as ensureShape,L as env,Rr as equal,zx as erf,Bx as euclideanNorm,ir as exp,ar as expandDims,Vx as expm1,xc as eye,Au as fft,vo as fill,Adt as findBackend,Ddt as findBackendFactory,aa as floor,im as floorDiv,Dz as forceHalfFloat,Ru as fused,la as gather,w5 as gatherND,Dy as gather_util,_dt as getBackend,m0 as getGradient,ih as getKernel,Zg as getKernelsForBackend,Wpt as getThreadsCount,f1 as gpgpu_util,f6 as grad,d6 as grads,Fe as greater,pn as greaterEqual,Il as ifft,Cu as imag,dn as image,v5 as inTopKAsync,oR as initializers,BN as input,Mr as io,km as irfft,Gx as isFinite,Wx as isInf,Ux as isNaN,$e as keep,Kr as kernel_impls,LR as layers,vu as leakyRelu,bl as less,Gn as lessEqual,iN as linalg,kE as linspace,TQ as loadGraphModel,_Q as loadGraphModelSync,SR as loadLayersModel,Hx as localResponseNormalization,Nr as log,Su as log1p,jx as logSigmoid,dm as logSoftmax,hm as logSumExp,Or as logicalAnd,Nu as logicalNot,gm as logicalOr,Xx as logicalXor,S8 as losses,TE as lowerBound,Bt as matMul,y2 as math,Sr as max,ku as maxPool,Zx as maxPool3d,_E as maxPoolWithArgmax,Tn as maximum,ke as mean,fh as memory,EE as meshgrid,zR as metrics,xl as min,uo as minimum,Jx as mirrorPad,Qx as mod,T7 as model,BR as models,yc as moments,f5 as movingAverage,$ as mul,AE as multiRNNCell,DE as multinomial,Ut as neg,kh as nextFrame,yl as norm,li as notEqual,ua as oneHot,dr as ones,Ir as onesLike,k as op,$E as outerProduct,fn as pad,RE as pad1d,FE as pad2d,OE as pad3d,PE as pad4d,ty as pool,cn as pow,_u as prelu,fx as print,ey as prod,Ndt as profile,ME as raggedGather,LE as raggedRange,zE as raggedTensorToTensor,BE as rand,aA as randomGamma,Ic as randomNormal,lA as randomStandardNormal,Wn as randomUniform,uA as randomUniformInt,ca as range,Tdt as ready,wl as real,ay as reciprocal,sm as registerBackend,E7 as registerCallbackConstructor,b_ as registerGradient,ic as registerKernel,A9 as registerOp,VR as regularizers,Pr as relu,xm as relu6,Edt as removeBackend,R as reshape,hr as reverse,cA as reverse1d,pA as reverse2d,mA as reverse3d,fA as reverse4d,Du as rfft,ym as round,bm as rsqrt,ft as scalar,h5 as scatterND,$u as scatter_util,yh as searchSorted,wm as selu,Im as separableConv2d,_7 as sequential,J as serialization,bK as setBackend,$dt as setPlatform,Gpt as setThreadsCount,Bpt as setWasmPath,Vpt as setWasmPaths,TT as setWebGLContext,dA as setdiff1dAsync,Nw as shared,tn as sigmoid,ly as sign,v8 as signal,Cm as sin,vm as sinh,Ot as slice,Sm as slice1d,wh as slice2d,Nm as slice3d,Cc as slice4d,ze as slice_util,Eu as softmax,ai as softplus,Tu as spaceToBatchND,N8 as sparse,y5 as sparseToDense,C8 as spectral,gr as split,Ne as sqrt,Wt as square,Tm as squaredDifference,Un as squeeze,qe as stack,No as step,uy as stridedSlice,k8 as string,lt as sub,pt as sum,mc as sumOutType,cy as tan,ra as tanh,sr as tensor,Ke as tensor1d,ui as tensor2d,py as tensor3d,hA as tensor4d,gA as tensor5d,xA as tensor6d,bA as tensorScatterUpdate,Co as tensor_util,iA as test_util,B as tidy,Fr as tile,kdt as time,my as topk,Fc as train,Vt as transpose,Em as truncatedNormal,fy as unique,pmt as unregisterGradient,cmt as unregisterKernel,Am as unsortedSegmentSum,xr as unstack,ur as upcastType,wA as upperBound,y as util,h6 as valueAndGrad,g6 as valueAndGrads,dy as variable,qx as variableGrads,Ypt as version,IF as version_converter,A2 as version_core,TO as version_cpu,tf as version_layers,Upt as version_wasm,Az as version_webgl,aDe as webgl,Td as webgl_util,be as where,gy as whereAsync,Te as zeros,vt as zerosLike}; diff --git a/dist/tfjs.version.js b/dist/tfjs.version.js index 841c5c1..653c831 100644 --- a/dist/tfjs.version.js +++ b/dist/tfjs.version.js @@ -4,4 +4,4 @@ author: ' */ -var e="4.3.0";var s="4.3.0";var t="4.3.0";var n="4.3.0";var i="4.3.0";var w={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":i};export{w as version}; +var e="4.5.0";var s="4.5.0";var t="4.5.0";var n="4.5.0";var i="4.5.0";var w={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":i};export{w as version}; diff --git a/typedoc/classes/AgeGenderNet.html b/typedoc/classes/AgeGenderNet.html index 830355f..27db6ad 100644 --- a/typedoc/classes/AgeGenderNet.html +++ b/typedoc/classes/AgeGenderNet.html @@ -1,4 +1,4 @@ -AgeGenderNet | @vladmandic/face-api - v1.7.10
+AgeGenderNet | @vladmandic/face-api - v1.7.11
  • Preparing search index...
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+
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    • @vladmandic/face-api - v1.7.11

    Class AgeGenderNet

    @@ -396,7 +396,7 @@

    Modules

    diff --git a/typedoc/classes/BoundingBox.html b/typedoc/classes/BoundingBox.html index 56fd405..a89bdae 100644 --- a/typedoc/classes/BoundingBox.html +++ b/typedoc/classes/BoundingBox.html @@ -1,4 +1,4 @@ -BoundingBox | @vladmandic/face-api - v1.7.10
    +BoundingBox | @vladmandic/face-api - v1.7.11
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    Class BoundingBox

    @@ -403,7 +403,7 @@

    Modules

    diff --git a/typedoc/classes/Box.html b/typedoc/classes/Box.html index c583be9..bf516dd 100644 --- a/typedoc/classes/Box.html +++ b/typedoc/classes/Box.html @@ -1,4 +1,4 @@ -Box | @vladmandic/face-api - v1.7.10
    +Box | @vladmandic/face-api - v1.7.11
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    Class Box<BoxType>

    @@ -389,7 +389,7 @@

    Modules

    diff --git a/typedoc/classes/ComposableTask.html b/typedoc/classes/ComposableTask.html index 070ce78..4f0f060 100644 --- a/typedoc/classes/ComposableTask.html +++ b/typedoc/classes/ComposableTask.html @@ -1,4 +1,4 @@ -ComposableTask | @vladmandic/face-api - v1.7.10
    +ComposableTask | @vladmandic/face-api - v1.7.11
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    +
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    Class ComposableTask<T>

    @@ -107,7 +107,7 @@

    Modules

    diff --git a/typedoc/classes/ComputeAllFaceDescriptorsTask.html b/typedoc/classes/ComputeAllFaceDescriptorsTask.html index 54a9d14..65f8804 100644 --- a/typedoc/classes/ComputeAllFaceDescriptorsTask.html +++ b/typedoc/classes/ComputeAllFaceDescriptorsTask.html @@ -1,4 +1,4 @@ -ComputeAllFaceDescriptorsTask | @vladmandic/face-api - v1.7.10
    +ComputeAllFaceDescriptorsTask | @vladmandic/face-api - v1.7.11
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    +
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    • @vladmandic/face-api - v1.7.11

    Class ComputeAllFaceDescriptorsTask<TSource>

    @@ -55,7 +55,7 @@

    Type Parameters

    +

    TSource extends WithFaceLandmarks<{
        detection: FaceDetection;
    }>

    Parameters

      @@ -135,7 +135,7 @@

      Modules

      diff --git a/typedoc/classes/ComputeFaceDescriptorsTaskBase.html b/typedoc/classes/ComputeFaceDescriptorsTaskBase.html index 6b1041b..c6c43c2 100644 --- a/typedoc/classes/ComputeFaceDescriptorsTaskBase.html +++ b/typedoc/classes/ComputeFaceDescriptorsTaskBase.html @@ -1,4 +1,4 @@ -ComputeFaceDescriptorsTaskBase | @vladmandic/face-api - v1.7.10
      +ComputeFaceDescriptorsTaskBase | @vladmandic/face-api - v1.7.11
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    Class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn>

    @@ -124,7 +124,7 @@

    Modules

    diff --git a/typedoc/classes/ComputeSingleFaceDescriptorTask.html b/typedoc/classes/ComputeSingleFaceDescriptorTask.html index baea68d..99991b0 100644 --- a/typedoc/classes/ComputeSingleFaceDescriptorTask.html +++ b/typedoc/classes/ComputeSingleFaceDescriptorTask.html @@ -1,4 +1,4 @@ -ComputeSingleFaceDescriptorTask | @vladmandic/face-api - v1.7.10
    +ComputeSingleFaceDescriptorTask | @vladmandic/face-api - v1.7.11
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    Class ComputeSingleFaceDescriptorTask<TSource>

    @@ -55,7 +55,7 @@

    Type Parameters

    +

    TSource extends WithFaceLandmarks<{
        detection: FaceDetection;
    }>

    Parameters

      @@ -135,7 +135,7 @@

      Modules

      diff --git a/typedoc/classes/DetectAllFaceLandmarksTask.html b/typedoc/classes/DetectAllFaceLandmarksTask.html index c23d78d..5c82753 100644 --- a/typedoc/classes/DetectAllFaceLandmarksTask.html +++ b/typedoc/classes/DetectAllFaceLandmarksTask.html @@ -1,4 +1,4 @@ -DetectAllFaceLandmarksTask | @vladmandic/face-api - v1.7.10
      +DetectAllFaceLandmarksTask | @vladmandic/face-api - v1.7.11
      • Preparing search index...
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      @vladmandic/face-api - v1.7.10
      +
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    @vladmandic/face-api - v1.7.11

    Class DetectAllFaceLandmarksTask<TSource>

    @@ -75,59 +75,59 @@

    run

    then

    +

    Returns WithFaceLandmarks<TSource>[] | PromiseLike<WithFaceLandmarks<TSource>[]>

    +

    Returns Promise<WithFaceLandmarks<TSource>[]>

    withAgeAndGender

    withFaceDescriptors

    withFaceExpressions

    @@ -146,7 +146,7 @@

    Modules

    diff --git a/typedoc/classes/DetectAllFacesTask.html b/typedoc/classes/DetectAllFacesTask.html index e0fcb0a..1ad410d 100644 --- a/typedoc/classes/DetectAllFacesTask.html +++ b/typedoc/classes/DetectAllFacesTask.html @@ -1,4 +1,4 @@ -DetectAllFacesTask | @vladmandic/face-api - v1.7.10
    +DetectAllFacesTask | @vladmandic/face-api - v1.7.11
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    @vladmandic/face-api - v1.7.10
    +
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    Class DetectAllFacesTask

    @@ -139,7 +139,7 @@

    Modules

    diff --git a/typedoc/classes/DetectFaceLandmarksTaskBase.html b/typedoc/classes/DetectFaceLandmarksTaskBase.html index 973576c..f3d213b 100644 --- a/typedoc/classes/DetectFaceLandmarksTaskBase.html +++ b/typedoc/classes/DetectFaceLandmarksTaskBase.html @@ -1,4 +1,4 @@ -DetectFaceLandmarksTaskBase | @vladmandic/face-api - v1.7.10
    +DetectFaceLandmarksTaskBase | @vladmandic/face-api - v1.7.11
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    +
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    Class DetectFaceLandmarksTaskBase<TReturn, TParentReturn>

    @@ -126,7 +126,7 @@

    Modules

    diff --git a/typedoc/classes/DetectFacesTaskBase.html b/typedoc/classes/DetectFacesTaskBase.html index 8ceb99a..689ed50 100644 --- a/typedoc/classes/DetectFacesTaskBase.html +++ b/typedoc/classes/DetectFacesTaskBase.html @@ -1,4 +1,4 @@ -DetectFacesTaskBase | @vladmandic/face-api - v1.7.10
    +DetectFacesTaskBase | @vladmandic/face-api - v1.7.11
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    +
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    Class DetectFacesTaskBase<TReturn>

    @@ -120,7 +120,7 @@

    Modules

    diff --git a/typedoc/classes/DetectSingleFaceLandmarksTask.html b/typedoc/classes/DetectSingleFaceLandmarksTask.html index a235a8d..80cc640 100644 --- a/typedoc/classes/DetectSingleFaceLandmarksTask.html +++ b/typedoc/classes/DetectSingleFaceLandmarksTask.html @@ -1,4 +1,4 @@ -DetectSingleFaceLandmarksTask | @vladmandic/face-api - v1.7.10
    +DetectSingleFaceLandmarksTask | @vladmandic/face-api - v1.7.11
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    +
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    Class DetectSingleFaceLandmarksTask<TSource>

    @@ -75,59 +75,59 @@

    run

    then

    +

    Returns undefined | WithFaceLandmarks<TSource> | PromiseLike<undefined | WithFaceLandmarks<TSource>>

    +

    Returns Promise<undefined | WithFaceLandmarks<TSource>>

    withAgeAndGender

    withFaceDescriptor

    withFaceExpressions

    @@ -146,7 +146,7 @@

    Modules

    diff --git a/typedoc/classes/DetectSingleFaceTask.html b/typedoc/classes/DetectSingleFaceTask.html index d8a62a1..73bf338 100644 --- a/typedoc/classes/DetectSingleFaceTask.html +++ b/typedoc/classes/DetectSingleFaceTask.html @@ -1,4 +1,4 @@ -DetectSingleFaceTask | @vladmandic/face-api - v1.7.10
    +DetectSingleFaceTask | @vladmandic/face-api - v1.7.11
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    +
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    Class DetectSingleFaceTask

    @@ -139,7 +139,7 @@

    Modules

    diff --git a/typedoc/classes/Dimensions.html b/typedoc/classes/Dimensions.html index 9b04246..687ac41 100644 --- a/typedoc/classes/Dimensions.html +++ b/typedoc/classes/Dimensions.html @@ -1,4 +1,4 @@ -Dimensions | @vladmandic/face-api - v1.7.10
    +Dimensions | @vladmandic/face-api - v1.7.11
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    +
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    Class Dimensions

    @@ -106,7 +106,7 @@

    Modules

    diff --git a/typedoc/classes/FaceDetection.html b/typedoc/classes/FaceDetection.html index ee91463..0160336 100644 --- a/typedoc/classes/FaceDetection.html +++ b/typedoc/classes/FaceDetection.html @@ -1,4 +1,4 @@ -FaceDetection | @vladmandic/face-api - v1.7.10
    +FaceDetection | @vladmandic/face-api - v1.7.11
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    +
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    Class FaceDetection

    @@ -181,7 +181,7 @@

    Modules

    diff --git a/typedoc/classes/FaceDetectionNet.html b/typedoc/classes/FaceDetectionNet.html index 540adb4..fa55d29 100644 --- a/typedoc/classes/FaceDetectionNet.html +++ b/typedoc/classes/FaceDetectionNet.html @@ -1,4 +1,4 @@ -FaceDetectionNet | @vladmandic/face-api - v1.7.10
    +FaceDetectionNet | @vladmandic/face-api - v1.7.11
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    +
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    Class FaceDetectionNet

    @@ -340,7 +340,7 @@

    Modules

    diff --git a/typedoc/classes/FaceExpressionNet.html b/typedoc/classes/FaceExpressionNet.html index d0da5e0..c1a417e 100644 --- a/typedoc/classes/FaceExpressionNet.html +++ b/typedoc/classes/FaceExpressionNet.html @@ -1,4 +1,4 @@ -FaceExpressionNet | @vladmandic/face-api - v1.7.10
    +FaceExpressionNet | @vladmandic/face-api - v1.7.11
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    +
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    Class FaceExpressionNet

    @@ -400,7 +400,7 @@

    Modules

    diff --git a/typedoc/classes/FaceExpressions.html b/typedoc/classes/FaceExpressions.html index 1bead0f..f8d13ca 100644 --- a/typedoc/classes/FaceExpressions.html +++ b/typedoc/classes/FaceExpressions.html @@ -1,4 +1,4 @@ -FaceExpressions | @vladmandic/face-api - v1.7.10
    +FaceExpressions | @vladmandic/face-api - v1.7.11
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    +
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    Class FaceExpressions

    @@ -122,7 +122,7 @@

    Modules

    diff --git a/typedoc/classes/FaceLandmark68Net.html b/typedoc/classes/FaceLandmark68Net.html index 2be689e..7f436bb 100644 --- a/typedoc/classes/FaceLandmark68Net.html +++ b/typedoc/classes/FaceLandmark68Net.html @@ -1,4 +1,4 @@ -FaceLandmark68Net | @vladmandic/face-api - v1.7.10
    +FaceLandmark68Net | @vladmandic/face-api - v1.7.11
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    +
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    Class FaceLandmark68Net

    @@ -424,7 +424,7 @@

    Modules

    diff --git a/typedoc/classes/FaceLandmark68TinyNet.html b/typedoc/classes/FaceLandmark68TinyNet.html index 437da36..b7e8ad7 100644 --- a/typedoc/classes/FaceLandmark68TinyNet.html +++ b/typedoc/classes/FaceLandmark68TinyNet.html @@ -1,4 +1,4 @@ -FaceLandmark68TinyNet | @vladmandic/face-api - v1.7.10
    +FaceLandmark68TinyNet | @vladmandic/face-api - v1.7.11
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    +
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    Class FaceLandmark68TinyNet

    @@ -422,7 +422,7 @@

    Modules

    diff --git a/typedoc/classes/FaceLandmarkNet.html b/typedoc/classes/FaceLandmarkNet.html index f7ccd3d..319828c 100644 --- a/typedoc/classes/FaceLandmarkNet.html +++ b/typedoc/classes/FaceLandmarkNet.html @@ -1,4 +1,4 @@ -FaceLandmarkNet | @vladmandic/face-api - v1.7.10
    +FaceLandmarkNet | @vladmandic/face-api - v1.7.11
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    +
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    Class FaceLandmarkNet

    @@ -422,7 +422,7 @@

    Modules

    diff --git a/typedoc/classes/FaceLandmarks.html b/typedoc/classes/FaceLandmarks.html index 0771c9d..083a506 100644 --- a/typedoc/classes/FaceLandmarks.html +++ b/typedoc/classes/FaceLandmarks.html @@ -1,4 +1,4 @@ -FaceLandmarks | @vladmandic/face-api - v1.7.10
    +FaceLandmarks | @vladmandic/face-api - v1.7.11
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    @vladmandic/face-api - v1.7.10
    +
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    Class FaceLandmarks

    @@ -222,7 +222,7 @@ it's current shift.

    Modules

    diff --git a/typedoc/classes/FaceLandmarks5.html b/typedoc/classes/FaceLandmarks5.html index 60cf3c8..b9321a1 100644 --- a/typedoc/classes/FaceLandmarks5.html +++ b/typedoc/classes/FaceLandmarks5.html @@ -1,4 +1,4 @@ -FaceLandmarks5 | @vladmandic/face-api - v1.7.10
    +FaceLandmarks5 | @vladmandic/face-api - v1.7.11
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    @vladmandic/face-api - v1.7.10
    +
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    Class FaceLandmarks5

    @@ -225,7 +225,7 @@ it's current shift.

    Modules

    diff --git a/typedoc/classes/FaceLandmarks68.html b/typedoc/classes/FaceLandmarks68.html index 7757fcb..0974e2c 100644 --- a/typedoc/classes/FaceLandmarks68.html +++ b/typedoc/classes/FaceLandmarks68.html @@ -1,4 +1,4 @@ -FaceLandmarks68 | @vladmandic/face-api - v1.7.10
    +FaceLandmarks68 | @vladmandic/face-api - v1.7.11
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    @vladmandic/face-api - v1.7.10
    +
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    Class FaceLandmarks68

    @@ -288,7 +288,7 @@ it's current shift.

    Modules

    diff --git a/typedoc/classes/FaceMatch.html b/typedoc/classes/FaceMatch.html index e0097b8..1a9e2e6 100644 --- a/typedoc/classes/FaceMatch.html +++ b/typedoc/classes/FaceMatch.html @@ -1,4 +1,4 @@ -FaceMatch | @vladmandic/face-api - v1.7.10
    +FaceMatch | @vladmandic/face-api - v1.7.11
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    @vladmandic/face-api - v1.7.10
    +
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    Class FaceMatch

    @@ -111,7 +111,7 @@

    Modules

    diff --git a/typedoc/classes/FaceMatcher.html b/typedoc/classes/FaceMatcher.html index 1624d31..35eb683 100644 --- a/typedoc/classes/FaceMatcher.html +++ b/typedoc/classes/FaceMatcher.html @@ -1,4 +1,4 @@ -FaceMatcher | @vladmandic/face-api - v1.7.10
    +FaceMatcher | @vladmandic/face-api - v1.7.11
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    @vladmandic/face-api - v1.7.10
    +
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    Class FaceMatcher

    @@ -158,7 +158,7 @@

    Modules

    diff --git a/typedoc/classes/FaceRecognitionNet.html b/typedoc/classes/FaceRecognitionNet.html index 4cfeb7f..1aa542c 100644 --- a/typedoc/classes/FaceRecognitionNet.html +++ b/typedoc/classes/FaceRecognitionNet.html @@ -1,4 +1,4 @@ -FaceRecognitionNet | @vladmandic/face-api - v1.7.10
    +FaceRecognitionNet | @vladmandic/face-api - v1.7.11
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    +
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    Class FaceRecognitionNet

    @@ -335,7 +335,7 @@

    Modules

    diff --git a/typedoc/classes/LabeledBox.html b/typedoc/classes/LabeledBox.html index 9f01da2..68fd6f8 100644 --- a/typedoc/classes/LabeledBox.html +++ b/typedoc/classes/LabeledBox.html @@ -1,4 +1,4 @@ -LabeledBox | @vladmandic/face-api - v1.7.10
    +LabeledBox | @vladmandic/face-api - v1.7.11
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    +
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    Class LabeledBox

    @@ -416,7 +416,7 @@

    Modules

    diff --git a/typedoc/classes/LabeledFaceDescriptors.html b/typedoc/classes/LabeledFaceDescriptors.html index 17c0a43..a6cea59 100644 --- a/typedoc/classes/LabeledFaceDescriptors.html +++ b/typedoc/classes/LabeledFaceDescriptors.html @@ -1,4 +1,4 @@ -LabeledFaceDescriptors | @vladmandic/face-api - v1.7.10
    +LabeledFaceDescriptors | @vladmandic/face-api - v1.7.11
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    +
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    Class LabeledFaceDescriptors

    @@ -114,7 +114,7 @@

    Modules

    diff --git a/typedoc/classes/NetInput.html b/typedoc/classes/NetInput.html index 0a6d887..9a23484 100644 --- a/typedoc/classes/NetInput.html +++ b/typedoc/classes/NetInput.html @@ -1,4 +1,4 @@ -NetInput | @vladmandic/face-api - v1.7.10
    +NetInput | @vladmandic/face-api - v1.7.11
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    Class NetInput

    @@ -229,7 +229,7 @@ with size [batchSize, inputSize, inputSize, 3].

    Modules

    diff --git a/typedoc/classes/NeuralNetwork.html b/typedoc/classes/NeuralNetwork.html index 3ef6a94..64c4b48 100644 --- a/typedoc/classes/NeuralNetwork.html +++ b/typedoc/classes/NeuralNetwork.html @@ -1,4 +1,4 @@ -NeuralNetwork | @vladmandic/face-api - v1.7.10
    +NeuralNetwork | @vladmandic/face-api - v1.7.11
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    +
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    Class NeuralNetwork<TNetParams>Abstract

    @@ -291,7 +291,7 @@

    Modules

    diff --git a/typedoc/classes/ObjectDetection.html b/typedoc/classes/ObjectDetection.html index 4d9ec17..1a4f55b 100644 --- a/typedoc/classes/ObjectDetection.html +++ b/typedoc/classes/ObjectDetection.html @@ -1,4 +1,4 @@ -ObjectDetection | @vladmandic/face-api - v1.7.10
    +ObjectDetection | @vladmandic/face-api - v1.7.11
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    Class ObjectDetection

    @@ -169,7 +169,7 @@

    Modules

    diff --git a/typedoc/classes/Point.html b/typedoc/classes/Point.html index c72fd46..c4f4577 100644 --- a/typedoc/classes/Point.html +++ b/typedoc/classes/Point.html @@ -1,4 +1,4 @@ -Point | @vladmandic/face-api - v1.7.10
    +Point | @vladmandic/face-api - v1.7.11
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    Class Point

    @@ -180,7 +180,7 @@

    Modules

    diff --git a/typedoc/classes/PredictedBox.html b/typedoc/classes/PredictedBox.html index de4c7f9..8a163d8 100644 --- a/typedoc/classes/PredictedBox.html +++ b/typedoc/classes/PredictedBox.html @@ -1,4 +1,4 @@ -PredictedBox | @vladmandic/face-api - v1.7.10
    +PredictedBox | @vladmandic/face-api - v1.7.11
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    Class PredictedBox

    @@ -454,7 +454,7 @@

    Modules

    diff --git a/typedoc/classes/Rect.html b/typedoc/classes/Rect.html index a78dda0..33b50fe 100644 --- a/typedoc/classes/Rect.html +++ b/typedoc/classes/Rect.html @@ -1,4 +1,4 @@ -Rect | @vladmandic/face-api - v1.7.10
    +Rect | @vladmandic/face-api - v1.7.11
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    Class Rect

    @@ -403,7 +403,7 @@

    Modules

    diff --git a/typedoc/classes/SsdMobilenetv1.html b/typedoc/classes/SsdMobilenetv1.html index a1052db..61c9428 100644 --- a/typedoc/classes/SsdMobilenetv1.html +++ b/typedoc/classes/SsdMobilenetv1.html @@ -1,4 +1,4 @@ -SsdMobilenetv1 | @vladmandic/face-api - v1.7.10
    +SsdMobilenetv1 | @vladmandic/face-api - v1.7.11
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    Class SsdMobilenetv1

    @@ -339,7 +339,7 @@

    Modules

    diff --git a/typedoc/classes/SsdMobilenetv1Options.html b/typedoc/classes/SsdMobilenetv1Options.html index ec4eae3..89e5f29 100644 --- a/typedoc/classes/SsdMobilenetv1Options.html +++ b/typedoc/classes/SsdMobilenetv1Options.html @@ -1,4 +1,4 @@ -SsdMobilenetv1Options | @vladmandic/face-api - v1.7.10
    +SsdMobilenetv1Options | @vladmandic/face-api - v1.7.11
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    Class SsdMobilenetv1Options

    @@ -84,7 +84,7 @@

    Modules

    diff --git a/typedoc/classes/TinyFaceDetector.html b/typedoc/classes/TinyFaceDetector.html index 9111a58..e55018d 100644 --- a/typedoc/classes/TinyFaceDetector.html +++ b/typedoc/classes/TinyFaceDetector.html @@ -1,4 +1,4 @@ -TinyFaceDetector | @vladmandic/face-api - v1.7.10
    +TinyFaceDetector | @vladmandic/face-api - v1.7.11
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    Class TinyFaceDetector

    @@ -440,7 +440,7 @@

    Modules

    diff --git a/typedoc/classes/TinyFaceDetectorOptions.html b/typedoc/classes/TinyFaceDetectorOptions.html index 84ce4bf..c2bab6d 100644 --- a/typedoc/classes/TinyFaceDetectorOptions.html +++ b/typedoc/classes/TinyFaceDetectorOptions.html @@ -1,4 +1,4 @@ -TinyFaceDetectorOptions | @vladmandic/face-api - v1.7.10
    +TinyFaceDetectorOptions | @vladmandic/face-api - v1.7.11
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    Class TinyFaceDetectorOptions

    @@ -89,7 +89,7 @@

    Modules

    diff --git a/typedoc/classes/TinyYolov2.html b/typedoc/classes/TinyYolov2.html index ec1d07b..e669ba6 100644 --- a/typedoc/classes/TinyYolov2.html +++ b/typedoc/classes/TinyYolov2.html @@ -1,4 +1,4 @@ -TinyYolov2 | @vladmandic/face-api - v1.7.10
    +TinyYolov2 | @vladmandic/face-api - v1.7.11
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    Class TinyYolov2

    @@ -454,7 +454,7 @@

    Modules

    diff --git a/typedoc/classes/TinyYolov2Options.html b/typedoc/classes/TinyYolov2Options.html index 77e51d7..411005e 100644 --- a/typedoc/classes/TinyYolov2Options.html +++ b/typedoc/classes/TinyYolov2Options.html @@ -1,4 +1,4 @@ -TinyYolov2Options | @vladmandic/face-api - v1.7.10
    +TinyYolov2Options | @vladmandic/face-api - v1.7.11
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    +
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    Class TinyYolov2Options

    @@ -86,7 +86,7 @@

    Modules

    diff --git a/typedoc/classes/draw.DrawBox.html b/typedoc/classes/draw.DrawBox.html index e1af7e2..9258fed 100644 --- a/typedoc/classes/draw.DrawBox.html +++ b/typedoc/classes/draw.DrawBox.html @@ -1,4 +1,4 @@ -DrawBox | @vladmandic/face-api - v1.7.10
    +DrawBox | @vladmandic/face-api - v1.7.11
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    +
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    • @vladmandic/face-api - v1.7.11

    Class DrawBox

    @@ -100,7 +100,7 @@

    Modules

    diff --git a/typedoc/classes/draw.DrawBoxOptions.html b/typedoc/classes/draw.DrawBoxOptions.html index a35e81f..34d79e9 100644 --- a/typedoc/classes/draw.DrawBoxOptions.html +++ b/typedoc/classes/draw.DrawBoxOptions.html @@ -1,4 +1,4 @@ -DrawBoxOptions | @vladmandic/face-api - v1.7.10
    +DrawBoxOptions | @vladmandic/face-api - v1.7.11
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    +
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    Class DrawBoxOptions

    @@ -91,7 +91,7 @@

    Modules

    diff --git a/typedoc/classes/draw.DrawFaceLandmarks.html b/typedoc/classes/draw.DrawFaceLandmarks.html index b1fdaf7..ee26cb4 100644 --- a/typedoc/classes/draw.DrawFaceLandmarks.html +++ b/typedoc/classes/draw.DrawFaceLandmarks.html @@ -1,4 +1,4 @@ -DrawFaceLandmarks | @vladmandic/face-api - v1.7.10
    +DrawFaceLandmarks | @vladmandic/face-api - v1.7.11
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    +
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    Class DrawFaceLandmarks

    @@ -100,7 +100,7 @@

    Modules

    diff --git a/typedoc/classes/draw.DrawFaceLandmarksOptions.html b/typedoc/classes/draw.DrawFaceLandmarksOptions.html index 9666563..1e23061 100644 --- a/typedoc/classes/draw.DrawFaceLandmarksOptions.html +++ b/typedoc/classes/draw.DrawFaceLandmarksOptions.html @@ -1,4 +1,4 @@ -DrawFaceLandmarksOptions | @vladmandic/face-api - v1.7.10
    +DrawFaceLandmarksOptions | @vladmandic/face-api - v1.7.11
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    +
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    Class DrawFaceLandmarksOptions

    @@ -103,7 +103,7 @@

    Modules

    diff --git a/typedoc/classes/draw.DrawTextField.html b/typedoc/classes/draw.DrawTextField.html index f0f4a01..9fbf823 100644 --- a/typedoc/classes/draw.DrawTextField.html +++ b/typedoc/classes/draw.DrawTextField.html @@ -1,4 +1,4 @@ -DrawTextField | @vladmandic/face-api - v1.7.10
    +DrawTextField | @vladmandic/face-api - v1.7.11
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    +
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    Class DrawTextField

    @@ -147,7 +147,7 @@

    Modules

    diff --git a/typedoc/classes/draw.DrawTextFieldOptions.html b/typedoc/classes/draw.DrawTextFieldOptions.html index 07f4b3f..da5b20d 100644 --- a/typedoc/classes/draw.DrawTextFieldOptions.html +++ b/typedoc/classes/draw.DrawTextFieldOptions.html @@ -1,4 +1,4 @@ -DrawTextFieldOptions | @vladmandic/face-api - v1.7.10
    +DrawTextFieldOptions | @vladmandic/face-api - v1.7.11
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    +
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    Class DrawTextFieldOptions

    @@ -113,7 +113,7 @@

    Modules

    diff --git a/typedoc/enums/Gender.html b/typedoc/enums/Gender.html index bb5a260..54eef6f 100644 --- a/typedoc/enums/Gender.html +++ b/typedoc/enums/Gender.html @@ -1,4 +1,4 @@ -Gender | @vladmandic/face-api - v1.7.10
    +Gender | @vladmandic/face-api - v1.7.11
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    +
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    Enumeration Gender

    Enumeration AnchorPosition

    Function allFaces

    @@ -45,7 +45,7 @@

    Modules

    diff --git a/typedoc/functions/allFacesSsdMobilenetv1.html b/typedoc/functions/allFacesSsdMobilenetv1.html index 3fd4a91..e52bbd8 100644 --- a/typedoc/functions/allFacesSsdMobilenetv1.html +++ b/typedoc/functions/allFacesSsdMobilenetv1.html @@ -1,4 +1,4 @@ -allFacesSsdMobilenetv1 | @vladmandic/face-api - v1.7.10
    +allFacesSsdMobilenetv1 | @vladmandic/face-api - v1.7.11
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    +
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    Function allFacesSsdMobilenetv1

    @@ -45,7 +45,7 @@

    Modules

    diff --git a/typedoc/functions/allFacesTinyYolov2.html b/typedoc/functions/allFacesTinyYolov2.html index 4e350a9..10f01d8 100644 --- a/typedoc/functions/allFacesTinyYolov2.html +++ b/typedoc/functions/allFacesTinyYolov2.html @@ -1,4 +1,4 @@ -allFacesTinyYolov2 | @vladmandic/face-api - v1.7.10
    +allFacesTinyYolov2 | @vladmandic/face-api - v1.7.11
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    +
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    Function allFacesTinyYolov2

    @@ -45,7 +45,7 @@

    Modules

    diff --git a/typedoc/functions/awaitMediaLoaded.html b/typedoc/functions/awaitMediaLoaded.html index 1640ad4..448cc41 100644 --- a/typedoc/functions/awaitMediaLoaded.html +++ b/typedoc/functions/awaitMediaLoaded.html @@ -1,4 +1,4 @@ -awaitMediaLoaded | @vladmandic/face-api - v1.7.10
    +awaitMediaLoaded | @vladmandic/face-api - v1.7.11
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    +
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    Function awaitMediaLoaded

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/bufferToImage.html b/typedoc/functions/bufferToImage.html index 4a48f4c..c8ca6ec 100644 --- a/typedoc/functions/bufferToImage.html +++ b/typedoc/functions/bufferToImage.html @@ -1,4 +1,4 @@ -bufferToImage | @vladmandic/face-api - v1.7.10
    +bufferToImage | @vladmandic/face-api - v1.7.11
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    +
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    Function bufferToImage

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/computeFaceDescriptor.html b/typedoc/functions/computeFaceDescriptor.html index 535d476..1d491e0 100644 --- a/typedoc/functions/computeFaceDescriptor.html +++ b/typedoc/functions/computeFaceDescriptor.html @@ -1,4 +1,4 @@ -computeFaceDescriptor | @vladmandic/face-api - v1.7.10
    +computeFaceDescriptor | @vladmandic/face-api - v1.7.11
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    +
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    Function computeFaceDescriptor

    @@ -50,7 +50,7 @@ face descriptors.

    Modules

    diff --git a/typedoc/functions/createCanvas.html b/typedoc/functions/createCanvas.html index 903ed88..0553203 100644 --- a/typedoc/functions/createCanvas.html +++ b/typedoc/functions/createCanvas.html @@ -1,4 +1,4 @@ -createCanvas | @vladmandic/face-api - v1.7.10
    +createCanvas | @vladmandic/face-api - v1.7.11
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    +
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    Function createCanvas

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/createCanvasFromMedia.html b/typedoc/functions/createCanvasFromMedia.html index fafee36..7469c2d 100644 --- a/typedoc/functions/createCanvasFromMedia.html +++ b/typedoc/functions/createCanvasFromMedia.html @@ -1,4 +1,4 @@ -createCanvasFromMedia | @vladmandic/face-api - v1.7.10
    +createCanvasFromMedia | @vladmandic/face-api - v1.7.11
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    +
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    • @vladmandic/face-api - v1.7.11

    Function createCanvasFromMedia

    @@ -45,7 +45,7 @@

    Modules

    diff --git a/typedoc/functions/createFaceDetectionNet.html b/typedoc/functions/createFaceDetectionNet.html index 7aab857..079fdfd 100644 --- a/typedoc/functions/createFaceDetectionNet.html +++ b/typedoc/functions/createFaceDetectionNet.html @@ -1,4 +1,4 @@ -createFaceDetectionNet | @vladmandic/face-api - v1.7.10
    +createFaceDetectionNet | @vladmandic/face-api - v1.7.11
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    +
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    Function createFaceDetectionNet

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/createFaceRecognitionNet.html b/typedoc/functions/createFaceRecognitionNet.html index 436abdb..f6146cc 100644 --- a/typedoc/functions/createFaceRecognitionNet.html +++ b/typedoc/functions/createFaceRecognitionNet.html @@ -1,4 +1,4 @@ -createFaceRecognitionNet | @vladmandic/face-api - v1.7.10
    +createFaceRecognitionNet | @vladmandic/face-api - v1.7.11
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    @vladmandic/face-api - v1.7.10
    +
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    Function createFaceRecognitionNet

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/createSsdMobilenetv1.html b/typedoc/functions/createSsdMobilenetv1.html index 6a2edad..e98b9ed 100644 --- a/typedoc/functions/createSsdMobilenetv1.html +++ b/typedoc/functions/createSsdMobilenetv1.html @@ -1,4 +1,4 @@ -createSsdMobilenetv1 | @vladmandic/face-api - v1.7.10
    +createSsdMobilenetv1 | @vladmandic/face-api - v1.7.11
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    +
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    Function createSsdMobilenetv1

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/createTinyFaceDetector.html b/typedoc/functions/createTinyFaceDetector.html index 4925d99..cc485da 100644 --- a/typedoc/functions/createTinyFaceDetector.html +++ b/typedoc/functions/createTinyFaceDetector.html @@ -1,4 +1,4 @@ -createTinyFaceDetector | @vladmandic/face-api - v1.7.10
    +createTinyFaceDetector | @vladmandic/face-api - v1.7.11
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    +
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    Function createTinyFaceDetector

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/createTinyYolov2.html b/typedoc/functions/createTinyYolov2.html index 0db1159..9e2831f 100644 --- a/typedoc/functions/createTinyYolov2.html +++ b/typedoc/functions/createTinyYolov2.html @@ -1,4 +1,4 @@ -createTinyYolov2 | @vladmandic/face-api - v1.7.10
    +createTinyYolov2 | @vladmandic/face-api - v1.7.11
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    @vladmandic/face-api - v1.7.10
    +
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    Function createTinyYolov2

    @@ -45,7 +45,7 @@

    Modules

    diff --git a/typedoc/functions/detectAllFaces.html b/typedoc/functions/detectAllFaces.html index 9d55930..0c4b755 100644 --- a/typedoc/functions/detectAllFaces.html +++ b/typedoc/functions/detectAllFaces.html @@ -1,4 +1,4 @@ -detectAllFaces | @vladmandic/face-api - v1.7.10
    +detectAllFaces | @vladmandic/face-api - v1.7.11
    • Preparing search index...
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    @vladmandic/face-api - v1.7.10
    +
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    Function detectAllFaces

    @@ -45,7 +45,7 @@

    Modules

    diff --git a/typedoc/functions/detectFaceLandmarks.html b/typedoc/functions/detectFaceLandmarks.html index 2aebfc5..69a1ffd 100644 --- a/typedoc/functions/detectFaceLandmarks.html +++ b/typedoc/functions/detectFaceLandmarks.html @@ -1,4 +1,4 @@ -detectFaceLandmarks | @vladmandic/face-api - v1.7.10
    +detectFaceLandmarks | @vladmandic/face-api - v1.7.11
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    @vladmandic/face-api - v1.7.10
    +
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    Function detectFaceLandmarks

    @@ -47,7 +47,7 @@

    Modules

    diff --git a/typedoc/functions/detectFaceLandmarksTiny.html b/typedoc/functions/detectFaceLandmarksTiny.html index 75a0d4c..503f7f5 100644 --- a/typedoc/functions/detectFaceLandmarksTiny.html +++ b/typedoc/functions/detectFaceLandmarksTiny.html @@ -1,4 +1,4 @@ -detectFaceLandmarksTiny | @vladmandic/face-api - v1.7.10
    +detectFaceLandmarksTiny | @vladmandic/face-api - v1.7.11
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    @vladmandic/face-api - v1.7.10
    +
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    Function detectFaceLandmarksTiny

    @@ -49,7 +49,7 @@ faster at inference, but also slightly less accurate.

    Modules

    diff --git a/typedoc/functions/detectLandmarks.html b/typedoc/functions/detectLandmarks.html index afbdd85..664d87e 100644 --- a/typedoc/functions/detectLandmarks.html +++ b/typedoc/functions/detectLandmarks.html @@ -1,4 +1,4 @@ -detectLandmarks | @vladmandic/face-api - v1.7.10
    +detectLandmarks | @vladmandic/face-api - v1.7.11
    • Preparing search index...
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function detectLandmarks

    @@ -47,7 +47,7 @@

    Modules

    diff --git a/typedoc/functions/detectSingleFace.html b/typedoc/functions/detectSingleFace.html index 60f1f81..8dbcaab 100644 --- a/typedoc/functions/detectSingleFace.html +++ b/typedoc/functions/detectSingleFace.html @@ -1,4 +1,4 @@ -detectSingleFace | @vladmandic/face-api - v1.7.10
    +detectSingleFace | @vladmandic/face-api - v1.7.11
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    @vladmandic/face-api - v1.7.10
    +
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    Function detectSingleFace

    @@ -45,7 +45,7 @@

    Modules

    diff --git a/typedoc/functions/draw.drawContour.html b/typedoc/functions/draw.drawContour.html index 3ec817b..7379d72 100644 --- a/typedoc/functions/draw.drawContour.html +++ b/typedoc/functions/draw.drawContour.html @@ -1,4 +1,4 @@ -drawContour | @vladmandic/face-api - v1.7.10
    +drawContour | @vladmandic/face-api - v1.7.11
    • Preparing search index...
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    +
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    Function drawContour

    @@ -48,7 +48,7 @@

    Modules

    diff --git a/typedoc/functions/draw.drawDetections.html b/typedoc/functions/draw.drawDetections.html index 19cb203..cb5b4aa 100644 --- a/typedoc/functions/draw.drawDetections.html +++ b/typedoc/functions/draw.drawDetections.html @@ -1,4 +1,4 @@ -drawDetections | @vladmandic/face-api - v1.7.10
    +drawDetections | @vladmandic/face-api - v1.7.11
    • Preparing search index...
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    +
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    Function drawDetections

    @@ -46,7 +46,7 @@

    Modules

    diff --git a/typedoc/functions/draw.drawFaceExpressions.html b/typedoc/functions/draw.drawFaceExpressions.html index 33d0b54..41e65ed 100644 --- a/typedoc/functions/draw.drawFaceExpressions.html +++ b/typedoc/functions/draw.drawFaceExpressions.html @@ -1,4 +1,4 @@ -drawFaceExpressions | @vladmandic/face-api - v1.7.10
    +drawFaceExpressions | @vladmandic/face-api - v1.7.11
    • Preparing search index...
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    +
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    • @vladmandic/face-api - v1.7.11

    Function drawFaceExpressions

    @@ -50,7 +50,7 @@

    Modules

    diff --git a/typedoc/functions/draw.drawFaceLandmarks-1.html b/typedoc/functions/draw.drawFaceLandmarks-1.html index ab65a07..20f853d 100644 --- a/typedoc/functions/draw.drawFaceLandmarks-1.html +++ b/typedoc/functions/draw.drawFaceLandmarks-1.html @@ -1,4 +1,4 @@ -drawFaceLandmarks | @vladmandic/face-api - v1.7.10
    +drawFaceLandmarks | @vladmandic/face-api - v1.7.11
    • Preparing search index...
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function drawFaceLandmarks

    @@ -46,7 +46,7 @@

    Modules

    diff --git a/typedoc/functions/euclideanDistance.html b/typedoc/functions/euclideanDistance.html index 641b80a..9553fe8 100644 --- a/typedoc/functions/euclideanDistance.html +++ b/typedoc/functions/euclideanDistance.html @@ -1,4 +1,4 @@ -euclideanDistance | @vladmandic/face-api - v1.7.10
    +euclideanDistance | @vladmandic/face-api - v1.7.11
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    +
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    Function euclideanDistance

    @@ -45,7 +45,7 @@

    Modules

    diff --git a/typedoc/functions/extendWithAge.html b/typedoc/functions/extendWithAge.html index 1caa38a..b4b83f3 100644 --- a/typedoc/functions/extendWithAge.html +++ b/typedoc/functions/extendWithAge.html @@ -1,4 +1,4 @@ -extendWithAge | @vladmandic/face-api - v1.7.10
    +extendWithAge | @vladmandic/face-api - v1.7.11
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function extendWithAge

    @@ -50,7 +50,7 @@

    Modules

    diff --git a/typedoc/functions/extendWithFaceDescriptor.html b/typedoc/functions/extendWithFaceDescriptor.html index a663aac..6965a53 100644 --- a/typedoc/functions/extendWithFaceDescriptor.html +++ b/typedoc/functions/extendWithFaceDescriptor.html @@ -1,4 +1,4 @@ -extendWithFaceDescriptor | @vladmandic/face-api - v1.7.10
    +extendWithFaceDescriptor | @vladmandic/face-api - v1.7.11
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function extendWithFaceDescriptor

    @@ -50,7 +50,7 @@

    Modules

    diff --git a/typedoc/functions/extendWithFaceDetection.html b/typedoc/functions/extendWithFaceDetection.html index 47b717c..b93e8f9 100644 --- a/typedoc/functions/extendWithFaceDetection.html +++ b/typedoc/functions/extendWithFaceDetection.html @@ -1,4 +1,4 @@ -extendWithFaceDetection | @vladmandic/face-api - v1.7.10
    +extendWithFaceDetection | @vladmandic/face-api - v1.7.11
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function extendWithFaceDetection

    @@ -50,7 +50,7 @@

    Modules

    diff --git a/typedoc/functions/extendWithFaceExpressions.html b/typedoc/functions/extendWithFaceExpressions.html index c518e08..dd701a3 100644 --- a/typedoc/functions/extendWithFaceExpressions.html +++ b/typedoc/functions/extendWithFaceExpressions.html @@ -1,4 +1,4 @@ -extendWithFaceExpressions | @vladmandic/face-api - v1.7.10
    +extendWithFaceExpressions | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function extendWithFaceExpressions

    @@ -50,7 +50,7 @@

    Modules

    diff --git a/typedoc/functions/extendWithFaceLandmarks.html b/typedoc/functions/extendWithFaceLandmarks.html index 39068a9..9f1c447 100644 --- a/typedoc/functions/extendWithFaceLandmarks.html +++ b/typedoc/functions/extendWithFaceLandmarks.html @@ -1,4 +1,4 @@ -extendWithFaceLandmarks | @vladmandic/face-api - v1.7.10
    +extendWithFaceLandmarks | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function extendWithFaceLandmarks

    @@ -52,7 +52,7 @@

    Modules

    diff --git a/typedoc/functions/extendWithGender.html b/typedoc/functions/extendWithGender.html index aa8b1bb..7c16ee5 100644 --- a/typedoc/functions/extendWithGender.html +++ b/typedoc/functions/extendWithGender.html @@ -1,4 +1,4 @@ -extendWithGender | @vladmandic/face-api - v1.7.10
    +extendWithGender | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function extendWithGender

    @@ -52,7 +52,7 @@

    Modules

    diff --git a/typedoc/functions/extractFaceTensors.html b/typedoc/functions/extractFaceTensors.html index a71dc05..ff9cf90 100644 --- a/typedoc/functions/extractFaceTensors.html +++ b/typedoc/functions/extractFaceTensors.html @@ -1,4 +1,4 @@ -extractFaceTensors | @vladmandic/face-api - v1.7.10
    +extractFaceTensors | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function extractFaceTensors

    @@ -56,7 +56,7 @@ converting them to tensors individually.

    Modules

    diff --git a/typedoc/functions/extractFaces.html b/typedoc/functions/extractFaces.html index 4143382..f7129f4 100644 --- a/typedoc/functions/extractFaces.html +++ b/typedoc/functions/extractFaces.html @@ -1,4 +1,4 @@ -extractFaces | @vladmandic/face-api - v1.7.10
    +extractFaces | @vladmandic/face-api - v1.7.11
    • Preparing search index...
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function extractFaces

    @@ -53,7 +53,7 @@

    Modules

    diff --git a/typedoc/functions/fetchImage.html b/typedoc/functions/fetchImage.html index e203715..3f665bc 100644 --- a/typedoc/functions/fetchImage.html +++ b/typedoc/functions/fetchImage.html @@ -1,4 +1,4 @@ -fetchImage | @vladmandic/face-api - v1.7.10
    +fetchImage | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function fetchImage

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/fetchJson.html b/typedoc/functions/fetchJson.html index 337fbb3..85e79d0 100644 --- a/typedoc/functions/fetchJson.html +++ b/typedoc/functions/fetchJson.html @@ -1,4 +1,4 @@ -fetchJson | @vladmandic/face-api - v1.7.10
    +fetchJson | @vladmandic/face-api - v1.7.11
    • Preparing search index...
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function fetchJson

    @@ -48,7 +48,7 @@

    Modules

    diff --git a/typedoc/functions/fetchNetWeights.html b/typedoc/functions/fetchNetWeights.html index 4f96a97..1c1d83b 100644 --- a/typedoc/functions/fetchNetWeights.html +++ b/typedoc/functions/fetchNetWeights.html @@ -1,4 +1,4 @@ -fetchNetWeights | @vladmandic/face-api - v1.7.10
    +fetchNetWeights | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function fetchNetWeights

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/fetchOrThrow.html b/typedoc/functions/fetchOrThrow.html index 529681b..59de6fb 100644 --- a/typedoc/functions/fetchOrThrow.html +++ b/typedoc/functions/fetchOrThrow.html @@ -1,4 +1,4 @@ -fetchOrThrow | @vladmandic/face-api - v1.7.10
    +fetchOrThrow | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function fetchOrThrow

    @@ -45,7 +45,7 @@

    Modules

    diff --git a/typedoc/functions/fetchVideo.html b/typedoc/functions/fetchVideo.html index eaf9336..11d7e95 100644 --- a/typedoc/functions/fetchVideo.html +++ b/typedoc/functions/fetchVideo.html @@ -1,4 +1,4 @@ -fetchVideo | @vladmandic/face-api - v1.7.10
    +fetchVideo | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function fetchVideo

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/getContext2dOrThrow.html b/typedoc/functions/getContext2dOrThrow.html index 7023021..ea5df14 100644 --- a/typedoc/functions/getContext2dOrThrow.html +++ b/typedoc/functions/getContext2dOrThrow.html @@ -1,4 +1,4 @@ -getContext2dOrThrow | @vladmandic/face-api - v1.7.10
    +getContext2dOrThrow | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function getContext2dOrThrow

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/getMediaDimensions.html b/typedoc/functions/getMediaDimensions.html index a9a7c18..b5d253e 100644 --- a/typedoc/functions/getMediaDimensions.html +++ b/typedoc/functions/getMediaDimensions.html @@ -1,4 +1,4 @@ -getMediaDimensions | @vladmandic/face-api - v1.7.10
    +getMediaDimensions | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function getMediaDimensions

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/imageTensorToCanvas.html b/typedoc/functions/imageTensorToCanvas.html index d6db71d..8d296cc 100644 --- a/typedoc/functions/imageTensorToCanvas.html +++ b/typedoc/functions/imageTensorToCanvas.html @@ -1,4 +1,4 @@ -imageTensorToCanvas | @vladmandic/face-api - v1.7.10
    +imageTensorToCanvas | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function imageTensorToCanvas

    @@ -45,7 +45,7 @@

    Modules

    diff --git a/typedoc/functions/imageToSquare.html b/typedoc/functions/imageToSquare.html index a9bd5c1..669282d 100644 --- a/typedoc/functions/imageToSquare.html +++ b/typedoc/functions/imageToSquare.html @@ -1,4 +1,4 @@ -imageToSquare | @vladmandic/face-api - v1.7.10
    +imageToSquare | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function imageToSquare

    @@ -47,7 +47,7 @@

    Modules

    diff --git a/typedoc/functions/inverseSigmoid.html b/typedoc/functions/inverseSigmoid.html index 9b96b3d..a8c14f3 100644 --- a/typedoc/functions/inverseSigmoid.html +++ b/typedoc/functions/inverseSigmoid.html @@ -1,4 +1,4 @@ -inverseSigmoid | @vladmandic/face-api - v1.7.10
    +inverseSigmoid | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function inverseSigmoid

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/iou.html b/typedoc/functions/iou.html index bd05f3f..a14b384 100644 --- a/typedoc/functions/iou.html +++ b/typedoc/functions/iou.html @@ -1,4 +1,4 @@ -iou | @vladmandic/face-api - v1.7.10
    +iou | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function iou

    @@ -47,7 +47,7 @@

    Modules

    diff --git a/typedoc/functions/isMediaElement.html b/typedoc/functions/isMediaElement.html index 6b984bc..68fbdf6 100644 --- a/typedoc/functions/isMediaElement.html +++ b/typedoc/functions/isMediaElement.html @@ -1,4 +1,4 @@ -isMediaElement | @vladmandic/face-api - v1.7.10
    +isMediaElement | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function isMediaElement

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/isMediaLoaded.html b/typedoc/functions/isMediaLoaded.html index ac9a267..657baaf 100644 --- a/typedoc/functions/isMediaLoaded.html +++ b/typedoc/functions/isMediaLoaded.html @@ -1,4 +1,4 @@ -isMediaLoaded | @vladmandic/face-api - v1.7.10
    +isMediaLoaded | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function isMediaLoaded

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/isWithAge.html b/typedoc/functions/isWithAge.html index 52ea74f..fc09ff9 100644 --- a/typedoc/functions/isWithAge.html +++ b/typedoc/functions/isWithAge.html @@ -1,4 +1,4 @@ -isWithAge | @vladmandic/face-api - v1.7.10
    +isWithAge | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function isWithAge

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/isWithFaceDetection.html b/typedoc/functions/isWithFaceDetection.html index 24c1187..0c09818 100644 --- a/typedoc/functions/isWithFaceDetection.html +++ b/typedoc/functions/isWithFaceDetection.html @@ -1,4 +1,4 @@ -isWithFaceDetection | @vladmandic/face-api - v1.7.10
    +isWithFaceDetection | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function isWithFaceDetection

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/isWithFaceExpressions.html b/typedoc/functions/isWithFaceExpressions.html index 08fcb7b..7b9e313 100644 --- a/typedoc/functions/isWithFaceExpressions.html +++ b/typedoc/functions/isWithFaceExpressions.html @@ -1,4 +1,4 @@ -isWithFaceExpressions | @vladmandic/face-api - v1.7.10
    +isWithFaceExpressions | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function isWithFaceExpressions

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/isWithFaceLandmarks.html b/typedoc/functions/isWithFaceLandmarks.html index 9f9db37..b3833e5 100644 --- a/typedoc/functions/isWithFaceLandmarks.html +++ b/typedoc/functions/isWithFaceLandmarks.html @@ -1,4 +1,4 @@ -isWithFaceLandmarks | @vladmandic/face-api - v1.7.10
    +isWithFaceLandmarks | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function isWithFaceLandmarks

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/isWithGender.html b/typedoc/functions/isWithGender.html index f0e3297..31058b1 100644 --- a/typedoc/functions/isWithGender.html +++ b/typedoc/functions/isWithGender.html @@ -1,4 +1,4 @@ -isWithGender | @vladmandic/face-api - v1.7.10
    +isWithGender | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function isWithGender

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/loadAgeGenderModel.html b/typedoc/functions/loadAgeGenderModel.html index c66b46d..a068336 100644 --- a/typedoc/functions/loadAgeGenderModel.html +++ b/typedoc/functions/loadAgeGenderModel.html @@ -1,4 +1,4 @@ -loadAgeGenderModel | @vladmandic/face-api - v1.7.10
    +loadAgeGenderModel | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function loadAgeGenderModel

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/loadFaceDetectionModel.html b/typedoc/functions/loadFaceDetectionModel.html index d41e71c..1d3a01c 100644 --- a/typedoc/functions/loadFaceDetectionModel.html +++ b/typedoc/functions/loadFaceDetectionModel.html @@ -1,4 +1,4 @@ -loadFaceDetectionModel | @vladmandic/face-api - v1.7.10
    +loadFaceDetectionModel | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function loadFaceDetectionModel

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/loadFaceExpressionModel.html b/typedoc/functions/loadFaceExpressionModel.html index 3e58880..97178b2 100644 --- a/typedoc/functions/loadFaceExpressionModel.html +++ b/typedoc/functions/loadFaceExpressionModel.html @@ -1,4 +1,4 @@ -loadFaceExpressionModel | @vladmandic/face-api - v1.7.10
    +loadFaceExpressionModel | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function loadFaceExpressionModel

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/loadFaceLandmarkModel.html b/typedoc/functions/loadFaceLandmarkModel.html index 7ca8290..053d2fc 100644 --- a/typedoc/functions/loadFaceLandmarkModel.html +++ b/typedoc/functions/loadFaceLandmarkModel.html @@ -1,4 +1,4 @@ -loadFaceLandmarkModel | @vladmandic/face-api - v1.7.10
    +loadFaceLandmarkModel | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function loadFaceLandmarkModel

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/loadFaceLandmarkTinyModel.html b/typedoc/functions/loadFaceLandmarkTinyModel.html index a52c888..f05279b 100644 --- a/typedoc/functions/loadFaceLandmarkTinyModel.html +++ b/typedoc/functions/loadFaceLandmarkTinyModel.html @@ -1,4 +1,4 @@ -loadFaceLandmarkTinyModel | @vladmandic/face-api - v1.7.10
    +loadFaceLandmarkTinyModel | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function loadFaceLandmarkTinyModel

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/loadFaceRecognitionModel.html b/typedoc/functions/loadFaceRecognitionModel.html index 8ade7eb..bc0bab2 100644 --- a/typedoc/functions/loadFaceRecognitionModel.html +++ b/typedoc/functions/loadFaceRecognitionModel.html @@ -1,4 +1,4 @@ -loadFaceRecognitionModel | @vladmandic/face-api - v1.7.10
    +loadFaceRecognitionModel | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function loadFaceRecognitionModel

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/loadSsdMobilenetv1Model.html b/typedoc/functions/loadSsdMobilenetv1Model.html index 1a9fed9..216b114 100644 --- a/typedoc/functions/loadSsdMobilenetv1Model.html +++ b/typedoc/functions/loadSsdMobilenetv1Model.html @@ -1,4 +1,4 @@ -loadSsdMobilenetv1Model | @vladmandic/face-api - v1.7.10
    +loadSsdMobilenetv1Model | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function loadSsdMobilenetv1Model

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/loadTinyFaceDetectorModel.html b/typedoc/functions/loadTinyFaceDetectorModel.html index 3a22685..3d95c5a 100644 --- a/typedoc/functions/loadTinyFaceDetectorModel.html +++ b/typedoc/functions/loadTinyFaceDetectorModel.html @@ -1,4 +1,4 @@ -loadTinyFaceDetectorModel | @vladmandic/face-api - v1.7.10
    +loadTinyFaceDetectorModel | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function loadTinyFaceDetectorModel

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/loadTinyYolov2Model.html b/typedoc/functions/loadTinyYolov2Model.html index b46c4df..cd872a0 100644 --- a/typedoc/functions/loadTinyYolov2Model.html +++ b/typedoc/functions/loadTinyYolov2Model.html @@ -1,4 +1,4 @@ -loadTinyYolov2Model | @vladmandic/face-api - v1.7.10
    +loadTinyYolov2Model | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function loadTinyYolov2Model

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/loadWeightMap.html b/typedoc/functions/loadWeightMap.html index 2e2cb3f..caafffb 100644 --- a/typedoc/functions/loadWeightMap.html +++ b/typedoc/functions/loadWeightMap.html @@ -1,4 +1,4 @@ -loadWeightMap | @vladmandic/face-api - v1.7.10
    +loadWeightMap | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function loadWeightMap

    @@ -45,7 +45,7 @@

    Modules

    diff --git a/typedoc/functions/locateFaces.html b/typedoc/functions/locateFaces.html index 0aefad2..0611509 100644 --- a/typedoc/functions/locateFaces.html +++ b/typedoc/functions/locateFaces.html @@ -1,4 +1,4 @@ -locateFaces | @vladmandic/face-api - v1.7.10
    +locateFaces | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function locateFaces

    @@ -53,7 +53,7 @@

    Modules

    diff --git a/typedoc/functions/matchDimensions.html b/typedoc/functions/matchDimensions.html index cec533f..03f2244 100644 --- a/typedoc/functions/matchDimensions.html +++ b/typedoc/functions/matchDimensions.html @@ -1,4 +1,4 @@ -matchDimensions | @vladmandic/face-api - v1.7.10
    +matchDimensions | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function matchDimensions

    @@ -52,7 +52,7 @@

    Modules

    diff --git a/typedoc/functions/minBbox.html b/typedoc/functions/minBbox.html index 7eeff11..a830c97 100644 --- a/typedoc/functions/minBbox.html +++ b/typedoc/functions/minBbox.html @@ -1,4 +1,4 @@ -minBbox | @vladmandic/face-api - v1.7.10
    +minBbox | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function minBbox

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/nonMaxSuppression.html b/typedoc/functions/nonMaxSuppression.html index e8f6fb6..4019641 100644 --- a/typedoc/functions/nonMaxSuppression.html +++ b/typedoc/functions/nonMaxSuppression.html @@ -1,4 +1,4 @@ -nonMaxSuppression | @vladmandic/face-api - v1.7.10
    +nonMaxSuppression | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function nonMaxSuppression

    @@ -49,7 +49,7 @@

    Modules

    diff --git a/typedoc/functions/normalize.html b/typedoc/functions/normalize.html index 09bd67d..ae9cc31 100644 --- a/typedoc/functions/normalize.html +++ b/typedoc/functions/normalize.html @@ -1,4 +1,4 @@ -normalize | @vladmandic/face-api - v1.7.10
    +normalize | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function normalize

    @@ -45,7 +45,7 @@

    Modules

    diff --git a/typedoc/functions/padToSquare.html b/typedoc/functions/padToSquare.html index 064bfc8..8a98625 100644 --- a/typedoc/functions/padToSquare.html +++ b/typedoc/functions/padToSquare.html @@ -1,4 +1,4 @@ -padToSquare | @vladmandic/face-api - v1.7.10
    +padToSquare | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function padToSquare

    @@ -54,7 +54,7 @@ both sides of the minor dimension oof the image.

    Modules

    diff --git a/typedoc/functions/predictAgeAndGender.html b/typedoc/functions/predictAgeAndGender.html index 7de0c87..eb62891 100644 --- a/typedoc/functions/predictAgeAndGender.html +++ b/typedoc/functions/predictAgeAndGender.html @@ -1,4 +1,4 @@ -predictAgeAndGender | @vladmandic/face-api - v1.7.10
    +predictAgeAndGender | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function predictAgeAndGender

    @@ -47,7 +47,7 @@

    Modules

    diff --git a/typedoc/functions/recognizeFaceExpressions.html b/typedoc/functions/recognizeFaceExpressions.html index 17205ef..ffeddcc 100644 --- a/typedoc/functions/recognizeFaceExpressions.html +++ b/typedoc/functions/recognizeFaceExpressions.html @@ -1,4 +1,4 @@ -recognizeFaceExpressions | @vladmandic/face-api - v1.7.10
    +recognizeFaceExpressions | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function recognizeFaceExpressions

    @@ -47,7 +47,7 @@

    Modules

    diff --git a/typedoc/functions/resizeResults.html b/typedoc/functions/resizeResults.html index 39a9d28..6a27243 100644 --- a/typedoc/functions/resizeResults.html +++ b/typedoc/functions/resizeResults.html @@ -1,4 +1,4 @@ -resizeResults | @vladmandic/face-api - v1.7.10
    +resizeResults | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function resizeResults

    @@ -50,7 +50,7 @@

    Modules

    diff --git a/typedoc/functions/resolveInput.html b/typedoc/functions/resolveInput.html index e1017c9..2a7e501 100644 --- a/typedoc/functions/resolveInput.html +++ b/typedoc/functions/resolveInput.html @@ -1,4 +1,4 @@ -resolveInput | @vladmandic/face-api - v1.7.10
    +resolveInput | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function resolveInput

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/shuffleArray.html b/typedoc/functions/shuffleArray.html index d5321ca..e981012 100644 --- a/typedoc/functions/shuffleArray.html +++ b/typedoc/functions/shuffleArray.html @@ -1,4 +1,4 @@ -shuffleArray | @vladmandic/face-api - v1.7.10
    +shuffleArray | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function shuffleArray

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/sigmoid.html b/typedoc/functions/sigmoid.html index a438595..4c9d652 100644 --- a/typedoc/functions/sigmoid.html +++ b/typedoc/functions/sigmoid.html @@ -1,4 +1,4 @@ -sigmoid | @vladmandic/face-api - v1.7.10
    +sigmoid | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function sigmoid

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/functions/ssdMobilenetv1-1.html b/typedoc/functions/ssdMobilenetv1-1.html index 9a47324..682e98c 100644 --- a/typedoc/functions/ssdMobilenetv1-1.html +++ b/typedoc/functions/ssdMobilenetv1-1.html @@ -1,4 +1,4 @@ -ssdMobilenetv1 | @vladmandic/face-api - v1.7.10
    +ssdMobilenetv1 | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function ssdMobilenetv1

    @@ -53,7 +53,7 @@

    Modules

    diff --git a/typedoc/functions/tinyFaceDetector-1.html b/typedoc/functions/tinyFaceDetector-1.html index 9779bc7..db15e0a 100644 --- a/typedoc/functions/tinyFaceDetector-1.html +++ b/typedoc/functions/tinyFaceDetector-1.html @@ -1,4 +1,4 @@ -tinyFaceDetector | @vladmandic/face-api - v1.7.10
    +tinyFaceDetector | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function tinyFaceDetector

    @@ -53,7 +53,7 @@

    Modules

    diff --git a/typedoc/functions/tinyYolov2-1.html b/typedoc/functions/tinyYolov2-1.html index a569e14..b2adc18 100644 --- a/typedoc/functions/tinyYolov2-1.html +++ b/typedoc/functions/tinyYolov2-1.html @@ -1,4 +1,4 @@ -tinyYolov2 | @vladmandic/face-api - v1.7.10
    +tinyYolov2 | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function tinyYolov2

    @@ -53,7 +53,7 @@

    Modules

    diff --git a/typedoc/functions/toNetInput.html b/typedoc/functions/toNetInput.html index 7492ffb..26d8e71 100644 --- a/typedoc/functions/toNetInput.html +++ b/typedoc/functions/toNetInput.html @@ -1,4 +1,4 @@ -toNetInput | @vladmandic/face-api - v1.7.10
    +toNetInput | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function toNetInput

    @@ -48,7 +48,7 @@ to be finished loading.

    Modules

    diff --git a/typedoc/functions/utils.computeReshapedDimensions.html b/typedoc/functions/utils.computeReshapedDimensions.html index 0be275a..0b67ee0 100644 --- a/typedoc/functions/utils.computeReshapedDimensions.html +++ b/typedoc/functions/utils.computeReshapedDimensions.html @@ -1,4 +1,4 @@ -computeReshapedDimensions | @vladmandic/face-api - v1.7.10
    +computeReshapedDimensions | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function computeReshapedDimensions

    @@ -46,7 +46,7 @@

    Modules

    diff --git a/typedoc/functions/utils.getCenterPoint.html b/typedoc/functions/utils.getCenterPoint.html index 7139a28..3ef842e 100644 --- a/typedoc/functions/utils.getCenterPoint.html +++ b/typedoc/functions/utils.getCenterPoint.html @@ -1,4 +1,4 @@ -getCenterPoint | @vladmandic/face-api - v1.7.10
    +getCenterPoint | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function getCenterPoint

    @@ -44,7 +44,7 @@

    Modules

    diff --git a/typedoc/functions/utils.isDimensions.html b/typedoc/functions/utils.isDimensions.html index 05fd517..24322c5 100644 --- a/typedoc/functions/utils.isDimensions.html +++ b/typedoc/functions/utils.isDimensions.html @@ -1,4 +1,4 @@ -isDimensions | @vladmandic/face-api - v1.7.10
    +isDimensions | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function isDimensions

    @@ -44,7 +44,7 @@

    Modules

    diff --git a/typedoc/functions/utils.isEven.html b/typedoc/functions/utils.isEven.html index 82ef9ef..96c8b04 100644 --- a/typedoc/functions/utils.isEven.html +++ b/typedoc/functions/utils.isEven.html @@ -1,4 +1,4 @@ -isEven | @vladmandic/face-api - v1.7.10
    +isEven | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function isEven

    @@ -44,7 +44,7 @@

    Modules

    diff --git a/typedoc/functions/utils.isFloat.html b/typedoc/functions/utils.isFloat.html index b0ca4f2..819a9c4 100644 --- a/typedoc/functions/utils.isFloat.html +++ b/typedoc/functions/utils.isFloat.html @@ -1,4 +1,4 @@ -isFloat | @vladmandic/face-api - v1.7.10
    +isFloat | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function isFloat

    @@ -44,7 +44,7 @@

    Modules

    diff --git a/typedoc/functions/utils.isTensor.html b/typedoc/functions/utils.isTensor.html index 2dfaead..785119b 100644 --- a/typedoc/functions/utils.isTensor.html +++ b/typedoc/functions/utils.isTensor.html @@ -1,4 +1,4 @@ -isTensor | @vladmandic/face-api - v1.7.10
    +isTensor | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function isTensor

    @@ -46,7 +46,7 @@

    Modules

    diff --git a/typedoc/functions/utils.isTensor1D.html b/typedoc/functions/utils.isTensor1D.html index 266b064..5d53fcb 100644 --- a/typedoc/functions/utils.isTensor1D.html +++ b/typedoc/functions/utils.isTensor1D.html @@ -1,4 +1,4 @@ -isTensor1D | @vladmandic/face-api - v1.7.10
    +isTensor1D | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function isTensor1D

    @@ -44,7 +44,7 @@

    Modules

    diff --git a/typedoc/functions/utils.isTensor2D.html b/typedoc/functions/utils.isTensor2D.html index 228393f..d680651 100644 --- a/typedoc/functions/utils.isTensor2D.html +++ b/typedoc/functions/utils.isTensor2D.html @@ -1,4 +1,4 @@ -isTensor2D | @vladmandic/face-api - v1.7.10
    +isTensor2D | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function isTensor2D

    @@ -44,7 +44,7 @@

    Modules

    diff --git a/typedoc/functions/utils.isTensor3D.html b/typedoc/functions/utils.isTensor3D.html index b8881cb..cb142a2 100644 --- a/typedoc/functions/utils.isTensor3D.html +++ b/typedoc/functions/utils.isTensor3D.html @@ -1,4 +1,4 @@ -isTensor3D | @vladmandic/face-api - v1.7.10
    +isTensor3D | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function isTensor3D

    @@ -44,7 +44,7 @@

    Modules

    diff --git a/typedoc/functions/utils.isTensor4D.html b/typedoc/functions/utils.isTensor4D.html index 546b023..c1c1721 100644 --- a/typedoc/functions/utils.isTensor4D.html +++ b/typedoc/functions/utils.isTensor4D.html @@ -1,4 +1,4 @@ -isTensor4D | @vladmandic/face-api - v1.7.10
    +isTensor4D | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function isTensor4D

    @@ -44,7 +44,7 @@

    Modules

    diff --git a/typedoc/functions/utils.isValidNumber.html b/typedoc/functions/utils.isValidNumber.html index 37db577..ac789f3 100644 --- a/typedoc/functions/utils.isValidNumber.html +++ b/typedoc/functions/utils.isValidNumber.html @@ -1,4 +1,4 @@ -isValidNumber | @vladmandic/face-api - v1.7.10
    +isValidNumber | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function isValidNumber

    @@ -44,7 +44,7 @@

    Modules

    diff --git a/typedoc/functions/utils.isValidProbablitiy.html b/typedoc/functions/utils.isValidProbablitiy.html index 62e7e38..ca3e3d7 100644 --- a/typedoc/functions/utils.isValidProbablitiy.html +++ b/typedoc/functions/utils.isValidProbablitiy.html @@ -1,4 +1,4 @@ -isValidProbablitiy | @vladmandic/face-api - v1.7.10
    +isValidProbablitiy | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
  • The search index is not available
    • @vladmandic/face-api - v1.7.11

    Function isValidProbablitiy

    @@ -44,7 +44,7 @@

    Modules

    diff --git a/typedoc/functions/utils.range.html b/typedoc/functions/utils.range.html index fb9333d..bbac707 100644 --- a/typedoc/functions/utils.range.html +++ b/typedoc/functions/utils.range.html @@ -1,4 +1,4 @@ -range | @vladmandic/face-api - v1.7.10
    +range | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function range

    @@ -48,7 +48,7 @@

    Modules

    diff --git a/typedoc/functions/utils.round.html b/typedoc/functions/utils.round.html index 6ba477e..0014b03 100644 --- a/typedoc/functions/utils.round.html +++ b/typedoc/functions/utils.round.html @@ -1,4 +1,4 @@ -round | @vladmandic/face-api - v1.7.10
    +round | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function round

    @@ -46,7 +46,7 @@

    Modules

    diff --git a/typedoc/functions/validateConfig.html b/typedoc/functions/validateConfig.html index 92d1fb8..967d8b0 100644 --- a/typedoc/functions/validateConfig.html +++ b/typedoc/functions/validateConfig.html @@ -1,4 +1,4 @@ -validateConfig | @vladmandic/face-api - v1.7.10
    +validateConfig | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Function validateConfig

    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/index.html b/typedoc/index.html index 10e803a..2b4a709 100644 --- a/typedoc/index.html +++ b/typedoc/index.html @@ -1,4 +1,4 @@ -@vladmandic/face-api - v1.7.10
    +@vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11
    -

    @vladmandic/face-api - v1.7.10

    +

    @vladmandic/face-api - v1.7.11

    @@ -215,7 +215,7 @@

    Modules
    diff --git a/typedoc/interfaces/IBoundingBox.html b/typedoc/interfaces/IBoundingBox.html index e29eb7e..98ccf64 100644 --- a/typedoc/interfaces/IBoundingBox.html +++ b/typedoc/interfaces/IBoundingBox.html @@ -1,4 +1,4 @@ -IBoundingBox | @vladmandic/face-api - v1.7.10
    +IBoundingBox | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Interface IBoundingBox

    @@ -76,7 +76,7 @@

    Modules

    diff --git a/typedoc/interfaces/IDimensions.html b/typedoc/interfaces/IDimensions.html index e87d5aa..bedee6b 100644 --- a/typedoc/interfaces/IDimensions.html +++ b/typedoc/interfaces/IDimensions.html @@ -1,4 +1,4 @@ -IDimensions | @vladmandic/face-api - v1.7.10
    +IDimensions | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Interface IDimensions

    @@ -63,7 +63,7 @@

    Modules

    diff --git a/typedoc/interfaces/IFaceDetecion.html b/typedoc/interfaces/IFaceDetecion.html index 54c0150..82031a5 100644 --- a/typedoc/interfaces/IFaceDetecion.html +++ b/typedoc/interfaces/IFaceDetecion.html @@ -1,4 +1,4 @@ -IFaceDetecion | @vladmandic/face-api - v1.7.10
    +IFaceDetecion | @vladmandic/face-api - v1.7.11
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.10
    +
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    • @vladmandic/face-api - v1.7.11

    Interface IFaceDetecion

    @@ -63,7 +63,7 @@

    Modules

    diff --git a/typedoc/interfaces/IFaceLandmarks.html b/typedoc/interfaces/IFaceLandmarks.html index 046d712..49e9fa6 100644 --- a/typedoc/interfaces/IFaceLandmarks.html +++ b/typedoc/interfaces/IFaceLandmarks.html @@ -1,4 +1,4 @@ -IFaceLandmarks | @vladmandic/face-api - v1.7.10
    +IFaceLandmarks | @vladmandic/face-api - v1.7.11
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    Interface IFaceLandmarks

    @@ -63,7 +63,7 @@

    Modules

    diff --git a/typedoc/interfaces/IFaceMatch.html b/typedoc/interfaces/IFaceMatch.html index 8377d48..c0af694 100644 --- a/typedoc/interfaces/IFaceMatch.html +++ b/typedoc/interfaces/IFaceMatch.html @@ -1,4 +1,4 @@ -IFaceMatch | @vladmandic/face-api - v1.7.10
    +IFaceMatch | @vladmandic/face-api - v1.7.11
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    Interface IFaceMatch

    @@ -63,7 +63,7 @@

    Modules

    diff --git a/typedoc/interfaces/IPoint.html b/typedoc/interfaces/IPoint.html index 97b486a..14c4979 100644 --- a/typedoc/interfaces/IPoint.html +++ b/typedoc/interfaces/IPoint.html @@ -1,4 +1,4 @@ -IPoint | @vladmandic/face-api - v1.7.10
    +IPoint | @vladmandic/face-api - v1.7.11
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    Interface IPoint

    @@ -63,7 +63,7 @@

    Modules

    diff --git a/typedoc/interfaces/IRect.html b/typedoc/interfaces/IRect.html index 0234038..e87de3b 100644 --- a/typedoc/interfaces/IRect.html +++ b/typedoc/interfaces/IRect.html @@ -1,4 +1,4 @@ -IRect | @vladmandic/face-api - v1.7.10
    +IRect | @vladmandic/face-api - v1.7.11
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    Interface IRect

    @@ -76,7 +76,7 @@

    Modules

    diff --git a/typedoc/interfaces/ISsdMobilenetv1Options.html b/typedoc/interfaces/ISsdMobilenetv1Options.html index be4780a..0e9ee42 100644 --- a/typedoc/interfaces/ISsdMobilenetv1Options.html +++ b/typedoc/interfaces/ISsdMobilenetv1Options.html @@ -1,4 +1,4 @@ -ISsdMobilenetv1Options | @vladmandic/face-api - v1.7.10
    +ISsdMobilenetv1Options | @vladmandic/face-api - v1.7.11
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    Interface ISsdMobilenetv1Options

    @@ -59,7 +59,7 @@

    Modules

    diff --git a/typedoc/interfaces/ITinyYolov2Options.html b/typedoc/interfaces/ITinyYolov2Options.html index ac6e10d..3ac8d4f 100644 --- a/typedoc/interfaces/ITinyYolov2Options.html +++ b/typedoc/interfaces/ITinyYolov2Options.html @@ -1,4 +1,4 @@ -ITinyYolov2Options | @vladmandic/face-api - v1.7.10
    +ITinyYolov2Options | @vladmandic/face-api - v1.7.11
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    Interface ITinyYolov2Options

    @@ -59,7 +59,7 @@

    Modules

    diff --git a/typedoc/interfaces/draw.IDrawBoxOptions.html b/typedoc/interfaces/draw.IDrawBoxOptions.html index 4c6eadc..993cf25 100644 --- a/typedoc/interfaces/draw.IDrawBoxOptions.html +++ b/typedoc/interfaces/draw.IDrawBoxOptions.html @@ -1,4 +1,4 @@ -IDrawBoxOptions | @vladmandic/face-api - v1.7.10
    +IDrawBoxOptions | @vladmandic/face-api - v1.7.11
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    Interface IDrawBoxOptions

    @@ -72,7 +72,7 @@

    Modules

    diff --git a/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html b/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html index c188c7c..fb2d40f 100644 --- a/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html +++ b/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html @@ -1,4 +1,4 @@ -IDrawFaceLandmarksOptions | @vladmandic/face-api - v1.7.10
    +IDrawFaceLandmarksOptions | @vladmandic/face-api - v1.7.11
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    Interface IDrawFaceLandmarksOptions

    @@ -84,7 +84,7 @@

    Modules

    diff --git a/typedoc/interfaces/draw.IDrawTextFieldOptions.html b/typedoc/interfaces/draw.IDrawTextFieldOptions.html index 141249d..e6b649a 100644 --- a/typedoc/interfaces/draw.IDrawTextFieldOptions.html +++ b/typedoc/interfaces/draw.IDrawTextFieldOptions.html @@ -1,4 +1,4 @@ -IDrawTextFieldOptions | @vladmandic/face-api - v1.7.10
    +IDrawTextFieldOptions | @vladmandic/face-api - v1.7.11
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    Interface IDrawTextFieldOptions

    @@ -88,7 +88,7 @@

    Modules

    diff --git a/typedoc/modules/draw.html b/typedoc/modules/draw.html index fb97c50..c93e7a8 100644 --- a/typedoc/modules/draw.html +++ b/typedoc/modules/draw.html @@ -1,4 +1,4 @@ -draw | @vladmandic/face-api - v1.7.10
    +draw | @vladmandic/face-api - v1.7.11
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    Namespace draw

    Namespace utils

    Type alias AgeAndGenderPrediction

    AgeAndGenderPrediction: {
        age: number;
        gender: Gender;
        genderProbability: number;
    }
    @@ -43,7 +43,7 @@

    Modules

    diff --git a/typedoc/types/BatchNorm.html b/typedoc/types/BatchNorm.html index b3df7ae..a4490e3 100644 --- a/typedoc/types/BatchNorm.html +++ b/typedoc/types/BatchNorm.html @@ -1,4 +1,4 @@ -BatchNorm | @vladmandic/face-api - v1.7.10
    +BatchNorm | @vladmandic/face-api - v1.7.11
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    Type alias BatchNorm

    BatchNorm: {
        sub: tf.Tensor1D;
        truediv: tf.Tensor1D;
    }
    @@ -41,7 +41,7 @@

    Modules

    diff --git a/typedoc/types/ConvWithBatchNorm.html b/typedoc/types/ConvWithBatchNorm.html index 45ef3b0..4b873ae 100644 --- a/typedoc/types/ConvWithBatchNorm.html +++ b/typedoc/types/ConvWithBatchNorm.html @@ -1,4 +1,4 @@ -ConvWithBatchNorm | @vladmandic/face-api - v1.7.10
    +ConvWithBatchNorm | @vladmandic/face-api - v1.7.11
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    Type alias ConvWithBatchNorm

    ConvWithBatchNorm: {
        bn: BatchNorm;
        conv: ConvParams;
    }
    @@ -41,7 +41,7 @@

    Modules

    diff --git a/typedoc/types/DefaultTinyYolov2NetParams.html b/typedoc/types/DefaultTinyYolov2NetParams.html index 295d1f1..fd77680 100644 --- a/typedoc/types/DefaultTinyYolov2NetParams.html +++ b/typedoc/types/DefaultTinyYolov2NetParams.html @@ -1,4 +1,4 @@ -DefaultTinyYolov2NetParams | @vladmandic/face-api - v1.7.10
    +DefaultTinyYolov2NetParams | @vladmandic/face-api - v1.7.11
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    Type alias DefaultTinyYolov2NetParams

    DefaultTinyYolov2NetParams: {
        conv0: ConvWithBatchNorm;
        conv1: ConvWithBatchNorm;
        conv2: ConvWithBatchNorm;
        conv3: ConvWithBatchNorm;
        conv4: ConvWithBatchNorm;
        conv5: ConvWithBatchNorm;
        conv6: ConvWithBatchNorm;
        conv7: ConvWithBatchNorm;
        conv8: ConvParams;
    }
    @@ -55,7 +55,7 @@

    Modules

    diff --git a/typedoc/types/Environment.html b/typedoc/types/Environment.html index 04f385d..3112aae 100644 --- a/typedoc/types/Environment.html +++ b/typedoc/types/Environment.html @@ -1,4 +1,4 @@ -Environment | @vladmandic/face-api - v1.7.10
    +Environment | @vladmandic/face-api - v1.7.11
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    Type alias Environment

    Environment: FileSystem & {
        Canvas: typeof HTMLCanvasElement;
        CanvasRenderingContext2D: typeof CanvasRenderingContext2D;
        Image: typeof HTMLImageElement;
        ImageData: typeof ImageData;
        Video: typeof HTMLVideoElement;
        createCanvasElement: (() => HTMLCanvasElement);
        createImageElement: (() => HTMLImageElement);
        createVideoElement: (() => HTMLVideoElement);
        fetch: ((url: string, init?: RequestInit) => Promise<Response>);
    }

    Type alias FaceDetectionFunction

    FaceDetectionFunction: ((input: TNetInput) => Promise<FaceDetection[]>)
    @@ -47,7 +47,7 @@

    Modules

    diff --git a/typedoc/types/FaceDetectionOptions.html b/typedoc/types/FaceDetectionOptions.html index cd0df4c..310f793 100644 --- a/typedoc/types/FaceDetectionOptions.html +++ b/typedoc/types/FaceDetectionOptions.html @@ -1,4 +1,4 @@ -FaceDetectionOptions | @vladmandic/face-api - v1.7.10
    +FaceDetectionOptions | @vladmandic/face-api - v1.7.11
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    Type alias FaceDetectionOptions

    FaceDetectionOptions: TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options

    Type alias FileSystem

    FileSystem: {
        readFile: ((filePath: string) => Promise<any>);
    }
    @@ -50,7 +50,7 @@

    Modules

    diff --git a/typedoc/types/ITinyFaceDetectorOptions.html b/typedoc/types/ITinyFaceDetectorOptions.html index b8929d5..16cebf9 100644 --- a/typedoc/types/ITinyFaceDetectorOptions.html +++ b/typedoc/types/ITinyFaceDetectorOptions.html @@ -1,4 +1,4 @@ -ITinyFaceDetectorOptions | @vladmandic/face-api - v1.7.10
    +ITinyFaceDetectorOptions | @vladmandic/face-api - v1.7.11
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    Type alias ITinyFaceDetectorOptions

    ITinyFaceDetectorOptions: ITinyYolov2Options

    Type alias MobilenetParams

    MobilenetParams: {
        conv0: SeparableConvParams | ConvParams;
        conv1: SeparableConvParams;
        conv2: SeparableConvParams;
        conv3: SeparableConvParams;
        conv4: SeparableConvParams;
        conv5: SeparableConvParams;
        conv6?: SeparableConvParams;
        conv7?: SeparableConvParams;
        conv8: ConvParams;
    }
    @@ -55,7 +55,7 @@

    Modules

    diff --git a/typedoc/types/NetOutput.html b/typedoc/types/NetOutput.html index 487d132..2497d52 100644 --- a/typedoc/types/NetOutput.html +++ b/typedoc/types/NetOutput.html @@ -1,4 +1,4 @@ -NetOutput | @vladmandic/face-api - v1.7.10
    +NetOutput | @vladmandic/face-api - v1.7.11
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    Type alias NetOutput

    NetOutput: {
        age: tf.Tensor1D;
        gender: tf.Tensor2D;
    }
    @@ -41,7 +41,7 @@

    Modules

    diff --git a/typedoc/types/NetParams.html b/typedoc/types/NetParams.html index 5a4618c..a1a486f 100644 --- a/typedoc/types/NetParams.html +++ b/typedoc/types/NetParams.html @@ -1,4 +1,4 @@ -NetParams | @vladmandic/face-api - v1.7.10
    +NetParams | @vladmandic/face-api - v1.7.11
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    Type alias NetParams

    NetParams: {
        fc: {
            age: FCParams;
            gender: FCParams;
        };
    }
    @@ -44,7 +44,7 @@

    Modules

    diff --git a/typedoc/types/TMediaElement.html b/typedoc/types/TMediaElement.html index c4c13cf..6f2e006 100644 --- a/typedoc/types/TMediaElement.html +++ b/typedoc/types/TMediaElement.html @@ -1,4 +1,4 @@ -TMediaElement | @vladmandic/face-api - v1.7.10
    +TMediaElement | @vladmandic/face-api - v1.7.11
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    Type alias TMediaElement

    TMediaElement: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement

    Type alias TNetInput

    TNetInput: string | TResolvedNetInput | (string | TResolvedNetInput)[] | NetInput

    Type alias TResolvedNetInput

    TResolvedNetInput: TMediaElement | tf | tf

    Type alias TinyYolov2Config

    TinyYolov2Config: {
        anchors: Point[];
        classes: string[];
        filterSizes?: number[];
        iouThreshold: number;
        isFirstLayerConv2d?: boolean;
        meanRgb?: [number, number, number];
        withClassScores?: boolean;
        withSeparableConvs: boolean;
    }
    @@ -53,7 +53,7 @@

    Modules

    diff --git a/typedoc/types/TinyYolov2NetParams.html b/typedoc/types/TinyYolov2NetParams.html index 0865b93..feb03be 100644 --- a/typedoc/types/TinyYolov2NetParams.html +++ b/typedoc/types/TinyYolov2NetParams.html @@ -1,4 +1,4 @@ -TinyYolov2NetParams | @vladmandic/face-api - v1.7.10
    +TinyYolov2NetParams | @vladmandic/face-api - v1.7.11
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    Type alias TinyYolov2NetParams

    TinyYolov2NetParams: DefaultTinyYolov2NetParams | MobilenetParams

    Type alias WithAge<TSource>

    WithAge<TSource>: TSource & {
        age: number;
    }
    @@ -39,7 +39,7 @@

    Modules

    diff --git a/typedoc/types/WithFaceDescriptor.html b/typedoc/types/WithFaceDescriptor.html index 4c686d4..2d9ec1b 100644 --- a/typedoc/types/WithFaceDescriptor.html +++ b/typedoc/types/WithFaceDescriptor.html @@ -1,4 +1,4 @@ -WithFaceDescriptor | @vladmandic/face-api - v1.7.10
    +WithFaceDescriptor | @vladmandic/face-api - v1.7.11
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    Type alias WithFaceDescriptor<TSource>

    WithFaceDescriptor<TSource>: TSource & {
        descriptor: Float32Array;
    }
    @@ -39,7 +39,7 @@

    Modules

    diff --git a/typedoc/types/WithFaceDetection.html b/typedoc/types/WithFaceDetection.html index 128de50..8a4ccf1 100644 --- a/typedoc/types/WithFaceDetection.html +++ b/typedoc/types/WithFaceDetection.html @@ -1,4 +1,4 @@ -WithFaceDetection | @vladmandic/face-api - v1.7.10
    +WithFaceDetection | @vladmandic/face-api - v1.7.11
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    Type alias WithFaceDetection<TSource>

    WithFaceDetection<TSource>: TSource & {
        detection: FaceDetection;
    }
    @@ -39,7 +39,7 @@

    Modules

    diff --git a/typedoc/types/WithFaceExpressions.html b/typedoc/types/WithFaceExpressions.html index d358cdf..0a4016e 100644 --- a/typedoc/types/WithFaceExpressions.html +++ b/typedoc/types/WithFaceExpressions.html @@ -1,4 +1,4 @@ -WithFaceExpressions | @vladmandic/face-api - v1.7.10
    +WithFaceExpressions | @vladmandic/face-api - v1.7.11
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    Type alias WithFaceExpressions<TSource>

    WithFaceExpressions<TSource>: TSource & {
        expressions: FaceExpressions;
    }
    @@ -39,7 +39,7 @@

    Modules

    diff --git a/typedoc/types/WithFaceLandmarks.html b/typedoc/types/WithFaceLandmarks.html index 9fbf61d..eb9d000 100644 --- a/typedoc/types/WithFaceLandmarks.html +++ b/typedoc/types/WithFaceLandmarks.html @@ -1,4 +1,4 @@ -WithFaceLandmarks | @vladmandic/face-api - v1.7.10
    +WithFaceLandmarks | @vladmandic/face-api - v1.7.11
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    Type alias WithFaceLandmarks<TSource, TFaceLandmarks>

    WithFaceLandmarks<TSource, TFaceLandmarks>: TSource & {
        alignedRect: FaceDetection;
        angle: {
            pitch: number | undefined;
            roll: number | undefined;
            yaw: number | undefined;
        };
        landmarks: TFaceLandmarks;
        unshiftedLandmarks: TFaceLandmarks;
    }
    @@ -41,7 +41,7 @@

    Modules

    diff --git a/typedoc/types/WithGender.html b/typedoc/types/WithGender.html index 65ec761..543236a 100644 --- a/typedoc/types/WithGender.html +++ b/typedoc/types/WithGender.html @@ -1,4 +1,4 @@ -WithGender | @vladmandic/face-api - v1.7.10
    +WithGender | @vladmandic/face-api - v1.7.11
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    Type alias WithGender<TSource>

    WithGender<TSource>: TSource & {
        gender: Gender;
        genderProbability: number;
    }
    @@ -39,7 +39,7 @@

    Modules

    diff --git a/typedoc/types/draw.DrawFaceExpressionsInput.html b/typedoc/types/draw.DrawFaceExpressionsInput.html index e209d3e..8ccc3f6 100644 --- a/typedoc/types/draw.DrawFaceExpressionsInput.html +++ b/typedoc/types/draw.DrawFaceExpressionsInput.html @@ -1,4 +1,4 @@ -DrawFaceExpressionsInput | @vladmandic/face-api - v1.7.10
    +DrawFaceExpressionsInput | @vladmandic/face-api - v1.7.11
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    Type alias DrawFaceExpressionsInput

    @@ -35,7 +35,7 @@

    Modules

    diff --git a/typedoc/types/draw.DrawFaceLandmarksInput.html b/typedoc/types/draw.DrawFaceLandmarksInput.html index 2f62b90..a3dd0d8 100644 --- a/typedoc/types/draw.DrawFaceLandmarksInput.html +++ b/typedoc/types/draw.DrawFaceLandmarksInput.html @@ -1,4 +1,4 @@ -DrawFaceLandmarksInput | @vladmandic/face-api - v1.7.10
    +DrawFaceLandmarksInput | @vladmandic/face-api - v1.7.11
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    Type alias DrawFaceLandmarksInput

    @@ -35,7 +35,7 @@

    Modules

    diff --git a/typedoc/types/draw.TDrawDetectionsInput.html b/typedoc/types/draw.TDrawDetectionsInput.html index 005a749..962d257 100644 --- a/typedoc/types/draw.TDrawDetectionsInput.html +++ b/typedoc/types/draw.TDrawDetectionsInput.html @@ -1,4 +1,4 @@ -TDrawDetectionsInput | @vladmandic/face-api - v1.7.10
    +TDrawDetectionsInput | @vladmandic/face-api - v1.7.11
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    Type alias TDrawDetectionsInput

    @@ -35,7 +35,7 @@

    Modules

    diff --git a/typedoc/variables/FACE_EXPRESSION_LABELS.html b/typedoc/variables/FACE_EXPRESSION_LABELS.html index 62efce2..7f73c5a 100644 --- a/typedoc/variables/FACE_EXPRESSION_LABELS.html +++ b/typedoc/variables/FACE_EXPRESSION_LABELS.html @@ -1,4 +1,4 @@ -FACE_EXPRESSION_LABELS | @vladmandic/face-api - v1.7.10
    +FACE_EXPRESSION_LABELS | @vladmandic/face-api - v1.7.11
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    @vladmandic/face-api - v1.7.10
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    • @vladmandic/face-api - v1.7.11

    Variable FACE_EXPRESSION_LABELSConst

    FACE_EXPRESSION_LABELS: string[] = ...

    Variable envConst

    env: {
        createBrowserEnv: (() => Environment);
        createFileSystem: ((fs?: any) => FileSystem);
        createNodejsEnv: (() => Environment);
        getEnv: (() => Environment);
        initialize: (() => null | void);
        isBrowser: (() => boolean);
        isNodejs: (() => boolean);
        monkeyPatch: ((env: Partial<Environment>) => void);
        setEnv: ((env: Environment) => void);
    } = ...
    @@ -124,7 +124,7 @@

    Modules

    diff --git a/typedoc/variables/nets.html b/typedoc/variables/nets.html index fb1c929..d1560f8 100644 --- a/typedoc/variables/nets.html +++ b/typedoc/variables/nets.html @@ -1,4 +1,4 @@ -nets | @vladmandic/face-api - v1.7.10
    +nets | @vladmandic/face-api - v1.7.11
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    • @vladmandic/face-api - v1.7.11

    Variable netsConst

    nets: {
        ageGenderNet: AgeGenderNet;
        faceExpressionNet: FaceExpressionNet;
        faceLandmark68Net: FaceLandmark68Net;
        faceLandmark68TinyNet: FaceLandmark68TinyNet;
        faceRecognitionNet: FaceRecognitionNet;
        ssdMobilenetv1: SsdMobilenetv1;
        tinyFaceDetector: TinyFaceDetector;
        tinyYolov2: TinyYolov2;
    } = ...
    @@ -53,7 +53,7 @@

    Modules

    diff --git a/typedoc/variables/version.html b/typedoc/variables/version.html index 7bd9e6c..7d1b176 100644 --- a/typedoc/variables/version.html +++ b/typedoc/variables/version.html @@ -1,4 +1,4 @@ -version | @vladmandic/face-api - v1.7.10
    +version | @vladmandic/face-api - v1.7.11
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    Variable versionConst

    version: string = ...